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Sample records for brain tumour classification

  1. Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

    PubMed

    Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

    2015-09-01

    The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. PMID:26256809

  2. Automated classification of human brain tumours by neural network analysis using in vivo 1H magnetic resonance spectroscopic metabolite phenotypes.

    PubMed

    Usenius, J P; Tuohimetsä, S; Vainio, P; Ala-Korpela, M; Hiltunen, Y; Kauppinen, R A

    1996-07-01

    We present a novel method to integrate in vivo nuclear magnetic resonance spectroscopy (MRS) information into the clinical diagnosis of brain tumours. Water-suppressed 1H MRS data were collected from 33 patients with brain tumours and 28 healthy controls in vivo. The data were treated in the time domain for removal of residual water and a region from the frequency domain (from 3.4 to 0.3 p.p.m.) together with the unsuppressed water signal were used as inputs for artificial neural network (ANN) analysis. The ANN distinguished tumour and normal tissue in each case and was able to classify benign and malignant gliomas as well as other brain tumours to match histology in a clinically useful manner with an accuracy of 82%. Thus the present data indicate existence of tumour tissue-specific metabolite phenotypes that can be detected by in vivo 1H MRS. We believe that a user-independent ANN analysis may provide an alternative method for tumour classification in clinical practice. PMID:8904763

  3. Brain and spinal tumour.

    PubMed

    Goh, C H; Lu, Y Y; Lau, B L; Oy, J; Lee, H K; Liew, D; Wong, A

    2014-12-01

    This study reviewed the epidemiology of brain and spinal tumours in Sarawak from January 2009 till December 2012. The crude incidence of brain tumour in Sarawak was 4.6 per 100,000 population/year with cumulative rate 0.5%. Meningioma was the most common brain tumour (32.3%) and followed by astrocytoma (19.4%). Only brain metastases showed a rising trend and cases were doubled in 4 years. This accounted for 15.4% and lung carcinoma was the commonest primary. Others tumour load were consistent. Primitive neuroectodermal tumour (PNET) and astrocytoma were common in paediatrics (60%). We encountered more primary spinal tumour rather than spinal metastases. Intradural schwannoma was the commonest and frequently located at thoracic level. The current healthcare system in Sarawak enables a more consolidate data collection to reflect accurate brain tumours incidence. This advantage allows subsequent future survival outcome research and benchmarking for healthcare resource planning. PMID:25934956

  4. Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes.

    PubMed

    Julià-Sapé, Margarida; Griffiths, John R; Tate, A Rosemary; Tate, Rosemary A; Howe, Franklyn A; Acosta, Dionisio; Postma, Geert; Underwood, Joshua; Majós, Carles; Arús, Carles

    2015-12-01

    The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future. PMID:26768492

  5. Classification of odontogenic tumours. A historical review.

    PubMed

    Philipsen, Hans Peter; Reichart, Peter A

    2006-10-01

    Using the term odontome for any tumour arising from the dental formative tissues, Broca suggested a classification of odontogenic tumours (OTs) in 1869. From 1888 to 1914, Bland-Sutton and Gabell, James and Payne modified tumour terminology, while maintaining Broca's odontome concept. Thoma and Goldman's classification (1946) divided the OTs into tumours of ectodermal, mesodermal and mixed origin and abolished the general term odontome. The Pindborg and Clausen classification (1958) based on the idea that the reciprocal epithelial-mesenchymal tissue interactions were also operating in the pathogenesis of OTs. In 1966, WHO established a Collaborating Centre for the Histological Classification of Odontogenic Tumours and Allied Lesions (including jaw cysts) headed by Dr Jens Pindborg. In 1971, the first authoritative WHO guide to the classification of OTs and cysts appeared followed in 1992 by a second edition. In 2002, Philipsen and Reichart produced a revision of the 1992-edition and in 2003, the editors of the WHO Blue Book series: 'WHO Classification of Tumours' decided to produce a volume on the Head and Neck Tumours including a chapter on Odontogenic Tumours and Bone Related Lesions. In July of 2005 this volume was published by IARC, Lyon. PMID:16968232

  6. The International Histological Classification of Tumours*

    PubMed Central

    Sobin, L. H.

    1981-01-01

    This article reviews the development of the WHO project on the histological classification of tumours, which has included the establishment of several collaborating centres and has involved more than 300 pathologists in over 50 countries. The project has resulted in the publication, over the last 14 years, of 25 volumes in the first series of the International Histological Classification of Tumours (IHCT), each giving a classification of tumours specific to a certain site. The classifications are based primarily on the microscopic characteristics of the tumours and are concerned with morphologically identifiable cell types and histological patterns as seen by means of light microscopy and conventional staining techniques. The article also describes the relationship between IHCT and other classification and coding systems and assesses possible future developments that may result from new approaches to diagnosis. PMID:6978190

  7. Brain tumour mortality in immigrants.

    PubMed

    Neutel, C I; Quinn, A; Brancker, A

    1989-03-01

    All Canadian deaths due to malignant brain tumour for the years 1970-73 were identified and analysed for country of birth. The years 1970-73 were chosen since in later years country of birth was no longer available for each death. The brain tumour population consisted of 1551 male and 1058 female deaths and matched controls were chosen from deaths due to other causes. Americans who died of brain tumour in Canada had a standardized mortality ratio (SMR) of 1.0 compared to their fellow Americans in the USA. Italian, German, Dutch and British immigrants had SMR between 1.5 and 2.6 compared to rates in their home countries and between 1.24 and 2.09 when compared to Canadian rates. A series of graphs shows the increased risk for male immigrants quite dramatically, and indicates that for females the increases were less pronounced. Further analysis showed that the excess risk is confined to those who were born in Western Europe while their Canadian-born children experienced the same rates as all Canadians. Based on the limited information available, occupation could not be shown to play a role in establishing risk. An attempt was made to pinpoint the years of immigration which showed the greatest risk. It is concluded that the determination of risk of brain tumour has a strong environmental component. The possibilities for identification of this component are discussed. PMID:2722385

  8. Telomerase activity in 144 brain tumours.

    PubMed Central

    Sano, T.; Asai, A.; Mishima, K.; Fujimaki, T.; Kirino, T.

    1998-01-01

    Unlimited proliferation in immortalized cells is believed to be highly dependent on the activity of telomerase, a ribonucleoprotein that synthesizes telomeric repeats onto chromosome ends. Using a polymerase chain reaction-based telomeric repeat amplification protocol (TRAP) assay, we analysed telomerase activity in 99 benign and 45 malignant brain tumours. The TRAP assay results were quantitated by normalizing the telomerase activity of each specimen to that of human glioma cell line T98G to obtain the relative telomerase activity. Telomerase activity was also assessed visually from the autoradiograms as being positive or negative. One hundred and sixteen tumours with negative telomerase activity had null relative telomerase activity, whereas 28 tumours with positive telomerase activity had relative telomerase activities of 12-84.3% (mean 0% vs 36.1 +/- 19.3%, P < 0.0001). Thus, quantification of telomerase activity confirmed the results of the visual evaluation of telomerase activity on autoradiograms. Based on the assessment, malignant brain tumours had a higher positive rate of telomerase activity than benign tumours (57.8% vs 2.0%, P < 0.001). These data indicate that positive telomerase activity is strongly associated with malignant brain tumours and is rather rare in benign tumours, such as neurinomas or meningiomas. Images Figure 2 PMID:9635839

  9. Phase congruency map driven brain tumour segmentation

    NASA Astrophysics Data System (ADS)

    Szilágyi, Tünde; Brady, Michael; Berényi, Ervin

    2015-03-01

    Computer Aided Diagnostic (CAD) systems are already of proven value in healthcare, especially for surgical planning, nevertheless much remains to be done. Gliomas are the most common brain tumours (70%) in adults, with a survival time of just 2-3 months if detected at WHO grades III or higher. Such tumours are extremely variable, necessitating multi-modal Magnetic Resonance Images (MRI). The use of Gadolinium-based contrast agents is only relevant at later stages of the disease where it highlights the enhancing rim of the tumour. Currently, there is no single accepted method that can be used as a reference. There are three main challenges with such images: to decide whether there is tumour present and is so localize it; to construct a mask that separates healthy and diseased tissue; and to differentiate between the tumour core and the surrounding oedema. This paper presents two contributions. First, we develop tumour seed selection based on multiscale multi-modal texture feature vectors. Second, we develop a method based on a local phase congruency based feature map to drive level-set segmentation. The segmentations achieved with our method are more accurate than previously presented methods, particularly for challenging low grade tumours.

  10. Neuropsychological Differences between Survivors of Supratentorial and Infratentorial Brain Tumours

    ERIC Educational Resources Information Center

    Patel, S. K.; Mullins, W. A.; O'Neil, S. H.; Wilson, K.

    2011-01-01

    Background: The purpose of this study is to evaluate the relationship between brain tumour location and core areas of cognitive and behavioural functioning for paediatric brain tumour survivors. The extant literature both supports and refutes an association between paediatric brain tumour location and neurocognitive outcomes. We examined…

  11. The 2007 WHO classification of tumours of the central nervous system.

    PubMed

    Louis, David N; Ohgaki, Hiroko; Wiestler, Otmar D; Cavenee, Webster K; Burger, Peter C; Jouvet, Anne; Scheithauer, Bernd W; Kleihues, Paul

    2007-08-01

    The fourth edition of the World Health Organization (WHO) classification of tumours of the central nervous system, published in 2007, lists several new entities, including angiocentric glioma, papillary glioneuronal tumour, rosette-forming glioneuronal tumour of the fourth ventricle, papillary tumour of the pineal region, pituicytoma and spindle cell oncocytoma of the adenohypophysis. Histological variants were added if there was evidence of a different age distribution, location, genetic profile or clinical behaviour; these included pilomyxoid astrocytoma, anaplastic medulloblastoma and medulloblastoma with extensive nodularity. The WHO grading scheme and the sections on genetic profiles were updated and the rhabdoid tumour predisposition syndrome was added to the list of familial tumour syndromes typically involving the nervous system. As in the previous, 2000 edition of the WHO 'Blue Book', the classification is accompanied by a concise commentary on clinico-pathological characteristics of each tumour type. The 2007 WHO classification is based on the consensus of an international Working Group of 25 pathologists and geneticists, as well as contributions from more than 70 international experts overall, and is presented as the standard for the definition of brain tumours to the clinical oncology and cancer research communities world-wide. PMID:17618441

  12. In Vivo Tumour Mapping Using Electrocorticography Alterations During Awake Brain Surgery: A Pilot Study.

    PubMed

    Boussen, Salah; Velly, Lionel; Benar, Christian; Metellus, Philippe; Bruder, Nicolas; Trébuchon, Agnès

    2016-09-01

    During awake brain surgery for tumour resection, in situ EEG recording (ECoG) is used to identify eloquent areas surrounding the tumour. We used the ECoG setup to record the electrical activity of cortical and subcortical tumours and then performed frequency and connectivity analyses in order to identify ECoG impairments and map tumours. We selected 16 patients with cortical (8) and subcortical (8) tumours undergoing awake brain surgery. For each patient, we computed the spectral content of tumoural and healthy areas in each frequency band. We computed connectivity of each electrode using connectivity markers (linear and non-linear correlations, phase-locking and coherence). We performed comparisons between healthy and tumour electrodes. The ECoG alterations were used to implement automated classification of the electrodes using clustering or neural network algorithms. ECoG alterations were used to image cortical tumours.Cortical tumours were found to profoundly alter all frequency contents (normalized and absolute power), with an increase in the δ activity and a decreases for the other bands (P < 0.05). Cortical tumour electrodes showed high level of connectivity compared to surrounding electrodes (all markers, P < 0.05). For subcortical tumours, a relative decrease in the γ1 band and in the alpha band in absolute amplitude (P < 0.05) were the only abnormalities. The neural network algorithm classification had a good performance: 93.6 % of the electrodes were classified adequately on a test subject. We found significant spectral and connectivity ECoG changes for cortical tumours, which allowed tumour recognition. Artificial neural algorithm pattern recognition seems promising for electrode classification in awake tumour surgery. PMID:27324381

  13. Epilepsy-associated tumours: what epileptologists should know about neuropathology, terminology, and classification systems.

    PubMed

    Holthausen, Hans; Blümcke, Ingmar

    2016-09-01

    Brain tumours are an ever-challenging issue in neurology and related medical disciplines. This applies in particular to brain tumours associated with childhood-onset epilepsies, in which seizures are the presenting and only neurological symptom, as our current understanding of the biology and clinical behaviour of an individual tumour is far from being evidence-based. Prospective and randomized clinical trials are lacking in the field of epilepsy-associated tumours and a review of the current literature evokes more questions than provides answers. In this review, current areas of controversy in neuropathology, as well as terminology and classification, are discussed from an epileptologist's perspective. An illustrative case report exemplifies this controversy to further promote interdisciplinary discussion and novel research avenues towards comprehensive patient management in the near future. PMID:27506282

  14. Brain tumour cells interconnect to a functional and resistant network.

    PubMed

    Osswald, Matthias; Jung, Erik; Sahm, Felix; Solecki, Gergely; Venkataramani, Varun; Blaes, Jonas; Weil, Sophie; Horstmann, Heinz; Wiestler, Benedikt; Syed, Mustafa; Huang, Lulu; Ratliff, Miriam; Karimian Jazi, Kianush; Kurz, Felix T; Schmenger, Torsten; Lemke, Dieter; Gömmel, Miriam; Pauli, Martin; Liao, Yunxiang; Häring, Peter; Pusch, Stefan; Herl, Verena; Steinhäuser, Christian; Krunic, Damir; Jarahian, Mostafa; Miletic, Hrvoje; Berghoff, Anna S; Griesbeck, Oliver; Kalamakis, Georgios; Garaschuk, Olga; Preusser, Matthias; Weiss, Samuel; Liu, Haikun; Heiland, Sabine; Platten, Michael; Huber, Peter E; Kuner, Thomas; von Deimling, Andreas; Wick, Wolfgang; Winkler, Frank

    2015-12-01

    Astrocytic brain tumours, including glioblastomas, are incurable neoplasms characterized by diffusely infiltrative growth. Here we show that many tumour cells in astrocytomas extend ultra-long membrane protrusions, and use these distinct tumour microtubes as routes for brain invasion, proliferation, and to interconnect over long distances. The resulting network allows multicellular communication through microtube-associated gap junctions. When damage to the network occurred, tumour microtubes were used for repair. Moreover, the microtube-connected astrocytoma cells, but not those remaining unconnected throughout tumour progression, were protected from cell death inflicted by radiotherapy. The neuronal growth-associated protein 43 was important for microtube formation and function, and drove microtube-dependent tumour cell invasion, proliferation, interconnection, and radioresistance. Oligodendroglial brain tumours were deficient in this mechanism. In summary, astrocytomas can develop functional multicellular network structures. Disconnection of astrocytoma cells by targeting their tumour microtubes emerges as a new principle to reduce the treatment resistance of this disease. PMID:26536111

  15. Automated EEG signal analysis for identification of epilepsy seizures and brain tumour.

    PubMed

    Sharanreddy, M; Kulkarni, P K

    2013-11-01

    Abstract Electroencephalography (EEG) is a clinical test which records neuro-electrical activities generated by brain structures. EEG test results used to monitor brain diseases such as epilepsy seizure, brain tumours, toxic encephalopathies infections and cerebrovascular disorders. Due to the extreme variation in the EEG morphologies, manual analysis of the EEG signal is laborious, time consuming and requires skilled interpreters, who by the nature of the task are prone to subjective judegment and error. Further, manual analysis of the EEG results often fails to detect and uncover subtle features. This paper proposes an automated EEG analysis method by combining digital signal processing and neural network techniques, which will remove error and subjectivity associated with manual analysis and identifies the existence of epilepsy seizure and brain tumour diseases. The system uses multi-wavelet transform for feature extraction in which an input EEG signal is decomposed in a sub-signal. Irregularities and unpredictable fluctuations present in the decomposed signal are measured using approximate entropy. A feed-forward neural network is used to classify the EEG signal as a normal, epilepsy or brain tumour signal. The proposed technique is implemented and tested on data of 500 EEG signals for each disease. Results are promising, with classification accuracy of 98% for normal, 93% for epilepsy and 87% for brain tumour. Along with classification, the paper also highlights the EEG abnormalities associated with brain tumour and epilepsy seizure. PMID:24116656

  16. ABCB1 in children's brain tumours.

    PubMed

    Coyle, Beth; Kessler, Maya; Sabnis, Durgagauri H; Kerr, Ian D

    2015-10-01

    Tumours of the central nervous system are the most common solid tumour, accounting for a quarter of the 1500 cases of childhood cancer diagnosed each year in the U.K. They are the most common cause of cancer-related death in children. Treatment consists of surgery followed by adjuvant chemotherapy and/or radiotherapy. Survival rates have generally increased, but many survivors suffer from radiotherapy-related neurocognitive and endocrine side effects as well as an increased risk of secondary cancer. Adjuvant chemotherapy is normally given in combination to circumvent chemoresistance, but several studies have demonstrated it to be ineffective in the absence of radiotherapy. The identification of children with drug-resistant disease at the outset could allow stratification of those that are potentially curable by chemotherapy alone. Ultimately, however, what is required is a means to overcome this drug resistance and restore the effectiveness of chemotherapy. Medulloblastomas and ependymomas account for over 30% of paediatric brain tumours. Advances in neurosurgery, adjuvant radiotherapy and chemotherapy have led to improvements in 5-year overall survival rates. There remain, however, significant numbers of medulloblastoma patients that have intrinsically drug-resistant tumours and/or present with disseminated disease. Local relapse in ependymoma is also common and has an extremely poor prognosis with only 25% of children surviving first relapse. Each of these is consistent with the acquisition of drug and radiotherapy resistance. Since the majority of chemotherapy drugs currently used to treat these patients are transport substrates for ATP-binding cassette sub-family B member 1 (ABCB1) we will address the hypothesis that ABCB1 expression underlies this drug resistance. PMID:26517917

  17. Mouse Models of Brain Metastasis for Unravelling Tumour Progression.

    PubMed

    Soto, Manuel Sarmiento; Sibson, Nicola R

    2016-01-01

    Secondary tumours in the brain account for 40 % of triple negative breast cancer patients, and the percentage may be higher at the time of autopsy. The use of in vivo models allow us to recapitulate the molecular mechanisms potentially used by circulating breast tumour cells to proliferate within the brain.Metastasis is a multistep process that depends on the success of several stages including cell evasion from the primary tumour, distribution and survival within the blood stream and cerebral microvasculature, penetration of the blood-brain barrier and proliferation within the brain microenvironment. Cellular adhesion molecules are key proteins involved in all of the steps in the metastatic process. Our group has developed two different in vivo models to encompass both seeding and colonisation stages of the metastatic process: (1) haematogenous dissemination of tumour cells by direct injection into the left ventricle of the heart, and (2) direct implantation of the tumour cells into the mouse brain.This chapter describes, in detail, the practical implementation of the intracerebral model, which can be used to analyse tumour proliferation within a specific area of the central nervous system and tumour-host cell interactions. We also describe the use of immunohistochemistry techniques to identify, at the molecular scale, tumour-host cell interactions, which may open new windows for brain metastasis therapy. PMID:27325270

  18. MicroRNA Regulation of Brain Tumour Initiating Cells in Central Nervous System Tumours

    PubMed Central

    Vijayakumar, Thusyanth; Bakhshinyan, David; Venugopal, Chitra; Singh, Sheila K.

    2015-01-01

    CNS tumours occur in both pediatric and adult patients and many of these tumours are associated with poor clinical outcome. Due to a paradigm shift in thinking for the last several years, these tumours are now considered to originate from a small population of stem-like cells within the bulk tumour tissue. These cells, termed as brain tumour initiating cells (BTICs), are perceived to be regulated by microRNAs at the posttranscriptional/translational levels. Proliferation, stemness, differentiation, invasion, angiogenesis, metastasis, apoptosis, and cell cycle constitute some of the significant processes modulated by microRNAs in cancer initiation and progression. Characterization and functional studies on oncogenic or tumour suppressive microRNAs are made possible because of developments in sequencing and microarray techniques. In the current review, we bring recent knowledge of the role of microRNAs in BTIC formation and therapy. Special attention is paid to two highly aggressive and well-characterized brain tumours: gliomas and medulloblastoma. As microRNA seems to be altered in the pathogenesis of many human diseases, “microRNA therapy” may now have potential to improve outcomes for brain tumour patients. In this rapidly evolving field, further understanding of miRNA biology and its contribution towards cancer can be mined for new therapeutic tools. PMID:26064134

  19. Early recognition and management of brain tumours in children.

    PubMed

    Rogers, Eleanor Katie; Cannon, Anna; Zaborowski, Krzysztof; Paul, Siba Prosad

    2016-08-31

    Brain tumours comprise over one quarter of all childhood cancers in the UK and are the most common cause of cancer-related deaths in children. The presentation of brain tumours can vary substantially in children. The presenting symptoms are often similar to less serious conditions, and are often managed as such initially. Therefore, it can be difficult to diagnose brain tumours in children. An early diagnosis is usually associated with more effective treatment and improved health outcomes. The diagnostic interval between first presentation to a health professional and diagnosis for brain tumours in children has been shown to be three times longer in the UK than in other developed countries. As a result, the HeadSmart campaign launched a symptom card in 2011 to increase awareness of brain tumours in children among the general population and healthcare professionals, with the aim of reducing the diagnostic interval to 5 weeks. Nurses have an essential role in early recognition of brain tumours in children, and in providing care and support to the child and their family following a diagnosis. PMID:27577312

  20. Intracerebral haemorrhage in primary and metastatic brain tumours.

    PubMed

    Salmaggi, Andrea; Erbetta, Alessandra; Silvani, Antonio; Maderna, Emanuela; Pollo, Bianca

    2008-09-01

    Intracerebral haemorrhage may both be a presenting manifestation in unrecognised brain tumour or--more frequently--take place in the disease course of known/suspected brain tumour due to diagnostic/therapeutic procedures, including biopsy, locoregional treatments and anti-angiogenic therapies. Apart from the difficulties inherent to accurate neuroradiological diagnosis in selected cases with small tumour volume, the main clinical problem that neurologists face is represented by decision making in prophylaxis/treatment of venous thromboembolism in these patients. These points are briefly discussed and available evidence on the last point is commented on. PMID:18690513

  1. Spectral and lifetime domain measurements of rat brain tumours

    NASA Astrophysics Data System (ADS)

    Abi Haidar, D.; Leh, B.; Allaoua, K.; Genoux, A.; Siebert, R.; Steffenhagen, M.; Peyrot, D.; Sandeau, N.; Vever-Bizet, C.; Bourg-Heckly, G.; Chebbi, I.; Collado-Hilly, M.

    2012-02-01

    During glioblastoma surgery, delineation of the brain tumour margins remains difficult especially since infiltrated and normal tissues have the same visual appearance. This problematic constitutes our research interest. We developed a fibre-optical fluorescence probe for spectroscopic and time domain measurements. First measurements of endogenous tissue fluorescence were performed on fresh and fixed rat tumour brain slices. Spectral characteristics, fluorescence redox ratios and fluorescence lifetime measurements were analysed. Fluorescence information collected from both, lifetime and spectroscopic experiments, appeared promising for tumour tissue discrimination. Two photon measurements were performed on the same fixed tissue. Different wavelengths are used to acquire two-photon excitation-fluorescence of tumorous and healthy sites.

  2. Residential Radon and Brain Tumour Incidence in a Danish Cohort

    PubMed Central

    Bräuner, Elvira V.; Andersen, Zorana J.; Andersen, Claus E.; Pedersen, Camilla; Gravesen, Peter; Ulbak, Kaare; Hertel, Ole; Loft, Steffen; Raaschou-Nielsen, Ole

    2013-01-01

    Background Increased brain tumour incidence over recent decades may reflect improved diagnostic methods and clinical practice, but remain unexplained. Although estimated doses are low a relationship between radon and brain tumours may exist. Objective To investigate the long-term effect of exposure to residential radon on the risk of primary brain tumour in a prospective Danish cohort. Methods During 1993–1997 we recruited 57,053 persons. We followed each cohort member for cancer occurrence from enrolment until 31 December 2009, identifying 121 primary brain tumour cases. We traced residential addresses from 1 January 1971 until 31 December 2009 and calculated radon concentrations at each address using information from central databases regarding geology and house construction. Cox proportional hazards models were used to estimate incidence rate-ratios (IRR) and 95% confidence intervals (CI) for the risk of primary brain tumours associated with residential radon exposure with adjustment for age, sex, occupation, fruit and vegetable consumption and traffic-related air pollution. Effect modification by air pollution was assessed. Results Median estimated radon was 40.5 Bq/m3. The adjusted IRR for primary brain tumour associated with each 100 Bq/m3 increment in average residential radon levels was 1.96 (95% CI: 1.07; 3.58) and this was exposure-dependently higher over the four radon exposure quartiles. This association was not modified by air pollution. Conclusions We found significant associations and exposure-response patterns between long-term residential radon exposure radon in a general population and risk of primary brain tumours, adding new knowledge to this field. This finding could be chance and needs to be challenged in future studies. PMID:24066143

  3. Emotional and personality changes following brain tumour resection.

    PubMed

    Jenkins, Lisanne M; Drummond, Katharine J; Andrewes, David G

    2016-07-01

    Psychological distress has a high prevalence in brain tumour patients, and understanding the emotional and personality changes that may follow neurosurgery is important for clinical management of these patients. We aimed to characterise these emotional and personality changes using subjective, observer-rated and clinical measures. We examined subjective changes in emotional experience and observer-rated changes to personality disturbances following neurosurgery for brain tumours (n=44), compared to a control group that had undergone spinal surgery (n=26). Participants completed the Hospital Anxiety and Depression Scale and a Subjective Emotional Change Questionnaire. Observers who knew the patients well also completed the Iowa Rating Scale of Personality Change. Compared to controls, patients with tumours reported significantly more changes to their subjective experience of emotions following neurosurgery, particularly anger, disgust and sadness. For the observer-ratings, tumour patients were described as having significant changes in the personality disturbances of irritability, impulsivity, moodiness, inflexibility, and being easily overwhelmed. Anxiety and depression were not significantly different between groups. Neurosurgical resection of a brain tumour is a major life event that changes patients' subjective experiences of different emotions, and leads to observer-rated changes in personality. In this study, these changes were not accompanied by increases in anxiety or depression. We conclude with a discussion of biological and psychosocial mechanisms that can impact emotional functioning and personality in patients with brain tumours. PMID:26898575

  4. The evolving classification of soft tissue tumours: an update based on the new WHO classification.

    PubMed

    Fletcher, C D M

    2006-01-01

    Tumour classifications have become an integral part of modern oncology and, for pathologists, they provide guidelines which facilitate diagnostic and prognostic reproducibility. In many organ systems and most especially over the past decade or so, the World Health Organization (WHO) classifications have become pre-eminent, partly enabled by the timely publication of new "blue books" which now incorporate detailed text and copious illustrations. The new WHO classification of soft tissue tumours was introduced in late 2002 and, because it represents a broad consensus view, it has gained widespread acceptance. This review summarizes the changes, both major and minor, which were introduced and briefly describes the significant number of tumour types which have been first recognized or properly characterized during the past decade. Arguably the four most significant conceptual advances have been: (i) the formal recognition that morphologically benign lesions (such as cutaneous fibrous histiocytoma) may very rarely metastasize; (ii) the general acceptance that most pleomorphic sarcomas can be meaningfully subclassified and that so-called malignant fibrous histiocytoma is not a definable entity, but instead represents a wastebasket of undifferentiated pleomorphic sarcomas, accounting for no more than 5% of adult soft tissue sarcomas; (iii) the acknowledgement that most lesions formerly known as haemangiopericytoma show no evidence of pericytic differentiation and, instead, are fibroblastic in nature and form a morphological continuum with solitary fibrous tumour; and (iv) the increasing appreciation that not only do we not know from which cell type(s) most soft tissue tumours originate (histogenesis) but, for many, we do not recognize their line of differentiation or lineage--hence an increasing number of tumours are placed in the "uncertain differentiation" category. PMID:16359532

  5. Monte Carlo dosimetry for synchrotron stereotactic radiotherapy of brain tumours

    NASA Astrophysics Data System (ADS)

    Boudou, Caroline; Balosso, Jacques; Estève, François; Elleaume, Hélène

    2005-10-01

    A radiation dose enhancement can be obtained in brain tumours after infusion of an iodinated contrast agent and irradiation with kilovoltage x-rays in tomography mode. The aim of this study was to assess dosimetric properties of the synchrotron stereotactic radiotherapy technique applied to humans (SSR) for preparing clinical trials. We designed an interface for dose computation based on a Monte Carlo code (MCNPX). A patient head was constructed from computed tomography (CT) data and a tumour volume was modelled. Dose distributions were calculated in SSR configuration for various energy beam and iodine content in the target volume. From the calculations, it appears that the iodine-filled target (10 mg ml-1) can be efficiently irradiated by a monochromatic beam of energy ranging from 50 to 85 keV. This paper demonstrates the feasibility of stereotactic radiotherapy for treating deep-seated brain tumours with monoenergetic x-rays from a synchrotron.

  6. [Meningioma: management of the most common brain tumour].

    PubMed

    Hundsberger, Thomas; Surbeck, Werner; Hader, Claudia; Putora, Paul Martin; Conen, Katrin; Roelcke, Ulrich

    2016-04-13

    Meningiomas are the most common primary brain tumours in adults and are therefore relevant for general practitioners. Most meningiomas are benign and neurosurgical resection offers the best chance of cure. However, complete resection is not achievable in many patients. This accounts for a relevant rate of tumour recurrences within 15 years of follow up. In atypical and anaplastic meningiomas of WHO grade II and III time to recurrence is dramatically shorter and these tumours need multimodal treatment strategies including postoperative radiotherapy. Various systemic treatments have occasionally been used as salvage therapy, but were essentially not effective. Only recently, Sunitinib, a small thyrosine kinase inhibitor as well as bevacizumab, a therapeutic antibody, have shown more promising results in highly pretreated, refractory meningioma patients. PMID:27078728

  7. Iodine-125 brachytherapy for brain tumours - a review

    PubMed Central

    2012-01-01

    Iodine-125 brachytherapy has been applied to brain tumours since 1979. Even though the physical and biological characteristics make these implants particularly attractive for minimal invasive treatment, the place for stereotactic brachytherapy is still poorly defined. An extensive review of the literature has been performed, especially concerning indications, results and complications. Iodine-125 seeds have been implanted in astrocytomas I-III, glioblastomas, metastases and several other tumour entities. Outcome data given in the literature are summarized. Complications are rare in carefully selected patients. All in all, for highly selected patients with newly diagnosed or recurrent primary or metastatic tumours, this method provides encouraging survival rates with relatively low complication rates and a good quality of life. PMID:22394548

  8. Clinical update: recognising brain tumours early in children.

    PubMed

    Paul, Siba Prosad; Debono, Rachel; Walker, David

    2013-04-01

    Brain tumour accounts for a quarter of all childhood cancers and is the leading cause of cancer related deaths in children. Initial symptoms can be misleading and is often misinterpreted as being caused by a less serious childhood illness. Available statistics show that it takes almost three times longer for the brain tumour in children to get diagnosed in the United Kingdom in comparison to other developed countries. Head Smart campaign was launched in the UK in 2011 with an aim to decrease the time from the onset of symptoms to diagnosis; initial results have been highly encouraging. Community practitioners play an important role in not only identifying symptoms (by following Head Smart symptom card) and selecting patients for reassurance, review or early referral but also by providing valuable support to the family post diagnosis in the community. PMID:23646820

  9. Gene selection approach based on improved swarm intelligent optimisation algorithm for tumour classification.

    PubMed

    Jin, Cong; Jin, Shu-Wei

    2016-06-01

    A number of different gene selection approaches based on gene expression profiles (GEP) have been developed for tumour classification. A gene selection approach selects the most informative genes from the whole gene space, which is an important process for tumour classification using GEP. This study presents an improved swarm intelligent optimisation algorithm to select genes for maintaining the diversity of the population. The most essential characteristic of the proposed approach is that it can automatically determine the number of the selected genes. On the basis of the gene selection, the authors construct a variety of the tumour classifiers, including the ensemble classifiers. Four gene datasets are used to evaluate the performance of the proposed approach. The experimental results confirm that the proposed classifiers for tumour classification are indeed effective. PMID:27187989

  10. Somatic CRISPR/Cas9-mediated tumour suppressor disruption enables versatile brain tumour modelling

    PubMed Central

    Zuckermann, Marc; Hovestadt, Volker; Knobbe-Thomsen, Christiane B.; Zapatka, Marc; Northcott, Paul A.; Schramm, Kathrin; Belic, Jelena; Jones, David T. W.; Tschida, Barbara; Moriarity, Branden; Largaespada, David; Roussel, Martine F.; Korshunov, Andrey; Reifenberger, Guido; Pfister, Stefan M.; Lichter, Peter; Kawauchi, Daisuke; Gronych, Jan

    2015-01-01

    In vivo functional investigation of oncogenes using somatic gene transfer has been successfully exploited to validate their role in tumorigenesis. For tumour suppressor genes this has proven more challenging due to technical aspects. To provide a flexible and effective method for investigating somatic loss-of-function alterations and their influence on tumorigenesis, we have established CRISPR/Cas9-mediated somatic gene disruption, allowing for in vivo targeting of TSGs. Here we demonstrate the utility of this approach by deleting single (Ptch1) or multiple genes (Trp53, Pten, Nf1) in the mouse brain, resulting in the development of medulloblastoma and glioblastoma, respectively. Using whole-genome sequencing (WGS) we characterized the medulloblastoma-driving Ptch1 deletions in detail and show that no off-targets were detected in these tumours. This method provides a fast and convenient system for validating the emerging wealth of novel candidate tumour suppressor genes and the generation of faithful animal models of human cancer. PMID:26067104

  11. New technologies to combat malignant tumours of the brain.

    PubMed

    Heppner, F

    1982-01-01

    1. The primary problem in an effective treatment of a glioblastoma is the prevention of a recurrence. 2. For that purpose were the following therapeutical procedures undertaken: (a) Temporary implantation of radio cobalt in the brain itself (1957): (b) Clostridium butyricum M 55 was used to render the centre of the tumour fluid (1967): (c) Podophyllin was used to destroy the border of the tumour (1980); (d) The CO2 Laser beam (1975); (e) The electromagnetic heat induction deep in the brain (1973-1978). 3. In order to make the operation and postoperative phase safer for the patient, the following precautions were drawn upon or employed: (a) Hyperbaric oxygenisation in the pressure chamber (1971); (b) The anti-G-suit (1974); (c) the computer controlled automatic infusion pump (1980), and (d) the telemetric measurement of intra-cranial pressure (1975). 4. Apart from the pressure chamber, the mentioned devices were all supervised and developed in the department of the author. 5. The first successful means in the prevention of the recurrence of a glioblastoma multiform seems to be the telethermic method mentioned in 2 (e) above. PMID:6287907

  12. Refinements in Sarcoma Classification in the Current 2013 World Health Organization Classification of Tumours of Soft Tissue and Bone.

    PubMed

    Jo, Vickie Y; Doyle, Leona A

    2016-10-01

    The fourth edition of the World Health Organization (WHO) Classification of Tumours of Soft Tissue and Bone was published in February 2013. The 2013 WHO volume provides an updated classification scheme and reproducible diagnostic criteria, which are based on recent clinicopathologic studies and genetic and molecular data that facilitated refined definition of established tumor types, recognition of novel entities, and the development of novel diagnostic markers. This article reviews updates and changes in the classification of bone and soft tissue tumors from the 2002 volume. PMID:27591490

  13. Guiding intracortical brain tumour cells to an extracortical cytotoxic hydrogel using aligned polymeric nanofibres

    NASA Astrophysics Data System (ADS)

    Jain, Anjana; Betancur, Martha; Patel, Gaurangkumar D.; Valmikinathan, Chandra M.; Mukhatyar, Vivek J.; Vakharia, Ajit; Pai, S. Balakrishna; Brahma, Barunashish; MacDonald, Tobey J.; Bellamkonda, Ravi V.

    2014-03-01

    Glioblastoma multiforme is an aggressive, invasive brain tumour with a poor survival rate. Available treatments are ineffective and some tumours remain inoperable because of their size or location. The tumours are known to invade and migrate along white matter tracts and blood vessels. Here, we exploit this characteristic of glioblastoma multiforme by engineering aligned polycaprolactone (PCL)-based nanofibres for tumour cells to invade and, hence, guide cells away from the primary tumour site to an extracortical location. This extracortial sink is a cyclopamine drug-conjugated, collagen-based hydrogel. When aligned PCL-nanofibre films in a PCL/polyurethane carrier conduit were inserted in the vicinity of an intracortical human U87MG glioblastoma xenograft, a significant number of human glioblastoma cells migrated along the aligned nanofibre films and underwent apoptosis in the extracortical hydrogel. Tumour volume in the brain was significantly lower following insertion of aligned nanofibre implants compared with the application of smooth fibres or no implants.

  14. Combined radiotherapy and chemotherapy for high-grade brain tumours

    NASA Astrophysics Data System (ADS)

    Barazzuol, Lara

    Glioblastoma (GBM) is the most common primary brain tumour in adults and among the most aggressive of all tumours. For several decades, the standard care of GBM was surgical resection followed by radiotherapy alone. In 2005, a landmark phase III clinical trial coordinated by the European Organization for Research and Treatment of Cancer (EORTC) and the National Cancer Institute of Canada (NCIC) demonstrated the benefit of radiotherapy with concomitant and adjuvant temozolomide (TMZ) chemotherapy. With TMZ, the median life expectancy in optimally managed patients is still only 12-14 months, with only 25% surviving 24 months. There is an urgent need for new therapies in particular in those patients whose tumour has an unmethylated methylguanine methyltransferase gene (MGMT) promoter, which is a predictive factor of benefit from TMZ. In this dissertation, the nature of the interaction between TMZ and radiation is investigated using both a mathematical model, based on in vivo population statistics of survival, and in vitro experimentation on a panel of human GBM cell lines. The results show that TMZ has an additive effect in vitro and that the population-based model may be insufficient in predicting TMZ response. The combination of TMZ with particle therapy is also investigated. Very little preclinical data exists on the effects of charged particles on GBM cell lines as well as on the concomitant application of chemotherapy. In this study, human GBM cells are exposed to 3 MeV protons and 6 MeV alpha particles in concomitance with TMZ. The results suggest that the radiation quality does not affect the nature of the interaction between TMZ and radiation, showing reproducible additive cytotoxicity. Since TMZ and radiation cause DNA damage in cancer cells, there has been increased attention to the use of poly(ADP-ribose) polymerase (PARP) inhibitors. PARP is a family of enzymes that play a key role in the repair of DNA breaks. In this study, a novel PARP inhibitor, ABT-888

  15. Targeting breast to brain metastatic tumours with death receptor ligand expressing therapeutic stem cells

    PubMed Central

    Bagci-Onder, Tugba; Du, Wanlu; Figueiredo, Jose-Luiz; Martinez-Quintanilla, Jordi

    2015-01-01

    Characterizing clinically relevant brain metastasis models and assessing the therapeutic efficacy in such models are fundamental for the development of novel therapies for metastatic brain cancers. In this study, we have developed an in vivo imageable breast-to-brain metastasis mouse model. Using real time in vivo imaging and subsequent composite fluorescence imaging, we show a widespread distribution of micro- and macro-metastasis in different stages of metastatic progression. We also show extravasation of tumour cells and the close association of tumour cells with blood vessels in the brain thus mimicking the multi-foci metastases observed in the clinics. Next, we explored the ability of engineered adult stem cells to track metastatic deposits in this model and show that engineered stem cells either implanted or injected via circulation efficiently home to metastatic tumour deposits in the brain. Based on the recent findings that metastatic tumour cells adopt unique mechanisms of evading apoptosis to successfully colonize in the brain, we reasoned that TNF receptor superfamily member 10A/10B apoptosis-inducing ligand (TRAIL) based pro-apoptotic therapies that induce death receptor signalling within the metastatic tumour cells might be a favourable therapeutic approach. We engineered stem cells to express a tumour selective, potent and secretable variant of a TRAIL, S-TRAIL, and show that these cells significantly suppressed metastatic tumour growth and prolonged the survival of mice bearing metastatic breast tumours. Furthermore, the incorporation of pro-drug converting enzyme, herpes simplex virus thymidine kinase, into therapeutic S-TRAIL secreting stem cells allowed their eradication post-tumour treatment. These studies are the first of their kind that provide insight into targeting brain metastasis with stem-cell mediated delivery of pro-apoptotic ligands and have important clinical implications. PMID:25910782

  16. Non-negative matrix factorisation methods for the spectral decomposition of MRS data from human brain tumours

    PubMed Central

    2012-01-01

    Background In-vivo single voxel proton magnetic resonance spectroscopy (SV 1H-MRS), coupled with supervised pattern recognition (PR) methods, has been widely used in clinical studies of discrimination of brain tumour types and follow-up of patients bearing abnormal brain masses. SV 1H-MRS provides useful biochemical information about the metabolic state of tumours and can be performed at short (< 45 ms) or long (> 45 ms) echo time (TE), each with particular advantages. Short-TE spectra are more adequate for detecting lipids, while the long-TE provides a much flatter signal baseline in between peaks but also negative signals for metabolites such as lactate. Both, lipids and lactate, are respectively indicative of specific metabolic processes taking place. Ideally, the information provided by both TE should be of use for clinical purposes. In this study, we characterise the performance of a range of Non-negative Matrix Factorisation (NMF) methods in two respects: first, to derive sources correlated with the mean spectra of known tissue types (tumours and normal tissue); second, taking the best performing NMF method for source separation, we compare its accuracy for class assignment when using the mixing matrix directly as a basis for classification, as against using the method for dimensionality reduction (DR). For this, we used SV 1H-MRS data with positive and negative peaks, from a widely tested SV 1H-MRS human brain tumour database. Results The results reported in this paper reveal the advantage of using a recently described variant of NMF, namely Convex-NMF, as an unsupervised method of source extraction from SV1H-MRS. Most of the sources extracted in our experiments closely correspond to the mean spectra of some of the analysed tumour types. This similarity allows accurate diagnostic predictions to be made both in fully unsupervised mode and using Convex-NMF as a DR step previous to standard supervised classification. The obtained results are comparable to, or

  17. Three-dimensional vasculature reconstruction of tumour microenvironment via local clustering and classification

    PubMed Central

    Zhu, Yanqiao; Li, Fuhai; Vadakkan, Tegy J.; Zhang, Mei; Landua, John; Wei, Wei; Ma, Jinwen; Dickinson, Mary E.; Rosen, Jeffrey M.; Lewis, Michael T.; Zhan, Ming; Wong, Stephen T. C.

    2013-01-01

    The vasculature inside breast cancers is one important component of the tumour microenvironment. The investigation of its spatial morphology, distribution and interactions with cancer cells, including cancer stem cells, is essential for elucidating mechanisms of tumour development and treatment response. Using confocal microscopy and fluorescent markers, we have acquired three-dimensional images of vasculature within mammary tumours and normal mammary gland of mouse models. However, it is difficult to segment and reconstruct complex vasculature accurately from the in vivo three-dimensional images owing to the existence of uneven intensity and regions with low signal-to-noise ratios (SNR). To overcome these challenges, we have developed a novel three-dimensional vasculature segmentation method based on local clustering and classification. First, images of vasculature are clustered into local regions, whose boundaries well delineate vasculature even in low SNR and uneven intensity regions. Then local regions belonging to vasculature are identified by applying a semi-supervised classification method based on three informative features of the local regions. Comparison of results using simulated and real vasculature images, from mouse mammary tumours and normal mammary gland, shows that the new method outperforms existing methods, and can be used for three-dimensional images with uneven background and low SNR to achieve accurate vasculature reconstruction. PMID:24511379

  18. Three-dimensional vasculature reconstruction of tumour microenvironment via local clustering and classification.

    PubMed

    Zhu, Yanqiao; Li, Fuhai; Vadakkan, Tegy J; Zhang, Mei; Landua, John; Wei, Wei; Ma, Jinwen; Dickinson, Mary E; Rosen, Jeffrey M; Lewis, Michael T; Zhan, Ming; Wong, Stephen T C

    2013-08-01

    The vasculature inside breast cancers is one important component of the tumour microenvironment. The investigation of its spatial morphology, distribution and interactions with cancer cells, including cancer stem cells, is essential for elucidating mechanisms of tumour development and treatment response. Using confocal microscopy and fluorescent markers, we have acquired three-dimensional images of vasculature within mammary tumours and normal mammary gland of mouse models. However, it is difficult to segment and reconstruct complex vasculature accurately from the in vivo three-dimensional images owing to the existence of uneven intensity and regions with low signal-to-noise ratios (SNR). To overcome these challenges, we have developed a novel three-dimensional vasculature segmentation method based on local clustering and classification. First, images of vasculature are clustered into local regions, whose boundaries well delineate vasculature even in low SNR and uneven intensity regions. Then local regions belonging to vasculature are identified by applying a semi-supervised classification method based on three informative features of the local regions. Comparison of results using simulated and real vasculature images, from mouse mammary tumours and normal mammary gland, shows that the new method outperforms existing methods, and can be used for three-dimensional images with uneven background and low SNR to achieve accurate vasculature reconstruction. PMID:24511379

  19. Towards the introduction of the ‘Immunoscore’ in the classification of malignant tumours

    PubMed Central

    Galon, Jérôme; Mlecnik, Bernhard; Bindea, Gabriela; Angell, Helen K; Berger, Anne; Lagorce, Christine; Lugli, Alessandro; Zlobec, Inti; Hartmann, Arndt; Bifulco, Carlo; Nagtegaal, Iris D; Palmqvist, Richard; Masucci, Giuseppe V; Botti, Gerardo; Tatangelo, Fabiana; Delrio, Paolo; Maio, Michele; Laghi, Luigi; Grizzi, Fabio; Asslaber, Martin; D'Arrigo, Corrado; Vidal-Vanaclocha, Fernando; Zavadova, Eva; Chouchane, Lotfi; Ohashi, Pamela S; Hafezi-Bakhtiari, Sara; Wouters, Bradly G; Roehrl, Michael; Nguyen, Linh; Kawakami, Yutaka; Hazama, Shoichi; Okuno, Kiyotaka; Ogino, Shuji; Gibbs, Peter; Waring, Paul; Sato, Noriyuki; Torigoe, Toshihiko; Itoh, Kyogo; Patel, Prabhu S; Shukla, Shilin N; Wang, Yili; Kopetz, Scott; Sinicrope, Frank A; Scripcariu, Viorel; Ascierto, Paolo A; Marincola, Francesco M; Fox, Bernard A; Pagès, Franck

    2014-01-01

    The American Joint Committee on Cancer/Union Internationale Contre le Cancer (AJCC/UICC) TNM staging system provides the most reliable guidelines for the routine prognostication and treatment of colorectal carcinoma. This traditional tumour staging summarizes data on tumour burden (T), the presence of cancer cells in draining and regional lymph nodes (N) and evidence for distant metastases (M). However, it is now recognized that the clinical outcome can vary significantly among patients within the same stage. The current classification provides limited prognostic information and does not predict response to therapy. Multiple ways to classify cancer and to distinguish different subtypes of colorectal cancer have been proposed, including morphology, cell origin, molecular pathways, mutation status and gene expression-based stratification. These parameters rely on tumour-cell characteristics. Extensive literature has investigated the host immune response against cancer and demonstrated the prognostic impact of the in situ immune cell infiltrate in tumours. A methodology named ‘Immunoscore’ has been defined to quantify the in situ immune infiltrate. In colorectal cancer, the Immunoscore may add to the significance of the current AJCC/UICC TNM classification, since it has been demonstrated to be a prognostic factor superior to the AJCC/UICC TNM classification. An international consortium has been initiated to validate and promote the Immunoscore in routine clinical settings. The results of this international consortium may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune). © 2013 The Authors. Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland. PMID:24122236

  20. Differential CD44 expression patterns in primary brain tumours and brain metastases.

    PubMed Central

    Li, H.; Liu, J.; Hofmann, M.; Hamou, M. F.; de Tribolet, N.

    1995-01-01

    Splicing variants of CD44 (CD44v) are increasingly recognised as metastasis-promoting factors in rodent and some human cancers. However, the frequency for CD44v expression in human cancers and their metastases and the status of CD44v expression in low or non-metastatic tumours is still uncertain. To address this issue, we investigated CD44 expression patterns in brain metastases (BMTs) spread from more than ten organs and five types of primary brain tumours (PBTs) by Northern blot, reverse transcription-polymerase chain reaction (RT-PCR) and immunocytochemical analysis. The results demonstrated that all of the 56 PBTs examined express standard form of CD44 (CD44s) but none of them express CD44v. In contrast, 22 of 26 BMTs studied were found with CD44v expression. Our data thus present direct evidence of a general distribution of CD44 in BMTs but suggest that such expression is an extremely rare event in PBTs. Therefore, the presence or absence of CD44v expression may be related to high or low metastatic potential of human malignancies. Images Figure 2 Figure 1 PMID:7541233

  1. Diagnostic segregation of human brain tumours using Fourier-transform infrared and/or Raman spectroscopy coupled with discriminant analysis†

    PubMed Central

    Gajjar, Ketan; Heppenstall, Lara D.; Pang, Weiyi; Ashton, Katherine M.; Trevisan, Júlio; Patel, Imran I.; Llabjani, Valon; Stringfellow, Helen F.; Martin-Hirsch, Pierre L.; Dawson, Timothy; Martin, Francis L.

    2013-01-01

    The most common initial treatment received by patients with a brain tumour is surgical removal of the growth. Precise histopathological diagnosis of brain tumours is to some extent subjective. Furthermore, currently available diagnostic imaging techniques to delineate the excision border during cytoreductive surgery lack the required spatial precision to aid surgeons. We set out to determine whether infrared (IR) and/or Raman spectroscopy combined with multivariate analysis could be applied to discriminate between normal brain tissue and different tumour types (meningioma, glioma and brain metastasis) based on the unique spectral “fingerprints” of their biochemical composition. Formalin-fixed paraffin-embedded tissue blocks of normal brain and different brain tumours were de-waxed, mounted on low-E slides and desiccated before being analyzed using attenuated total reflection Fourier-transform IR (ATR-FTIR) and Raman spectroscopy. ATR-FTIR spectroscopy showed a clear segregation between normal and different tumour subtypes. Discrimination of tumour classes was also apparent with Raman spectroscopy. Further analysis of spectral data revealed changes in brain biochemical structure associated with different tumours. Decreased tentatively-assigned lipid-to-protein ratio was associated with increased tumour progression. Alteration in cholesterol esters-to-phenylalanine ratio was evident in grade IV glioma and metastatic tumours. The current study indicates that IR and/or Raman spectroscopy have the potential to provide a novel diagnostic approach in the accurate diagnosis of brain tumours and have potential for application in intra-operative diagnosis. PMID:24098310

  2. [WHO classification of tumours of soft tissue and bone 2013: the main changes compared to the 3rd edition].

    PubMed

    Zambo, Iva; Veselý, Karel

    2014-04-01

    In early 2013, the new classification of tumours of soft tissue and bones was released. This edition belongs to the fourth series of so-called Blue Books published under the auspices of the World Health Organisation (WHO). The current classification follows the previous third edition, from which it differs in several aspects. The vast majority of changes are related to the soft tissue tumour section, which was enriched with three new chapters, some entities or terms were removed, new diagnoses were introduced, and several tumours were reallocated to other categories. Albeit to a lesser extent, similar changes have occurred also in the classification of bone tumours. Compared to the previous edition, more detailed molecular and cytogenetic data were incorporated in the current issue. The rapidly increasing knowledge of the genetics of mesenchymal tumours allows us to make more accurate diagnoses as well as to better understand of the pathogenesis of these lesions. However, abundant molecular and cytogenetic data highlight an increasing problem of growing numbers of genetic overlaps even among quite different tumours. The coexistence of several grading systems of soft tissue tumours is another controversial issue mentioned in the recent WHO classification. The main advantages and limitations of the two most widely used grading systems are also discussed. PMID:24758500

  3. Highlights of Children with Cancer UK's Workshop on Drug Delivery in Paediatric Brain Tumours.

    PubMed

    Nailor, Audrey; Walker, David A; Jacques, Thomas S; Warren, Kathy E; Brem, Henry; Kearns, Pamela R; Greenwood, John; Penny, Jeffrey I; Pilkington, Geoffrey J; Carcaboso, Angel M; Fleischhack, Gudrun; Macarthur, Donald; Slavc, Irene; Meijer, Lisethe; Gill, Steven; Lowis, Stephen; van Vuurden, Dannis G; Pearl, Monica S; Clifford, Steven C; Morrissy, Sorana; Ivanov, Delyan P; Beccaria, Kévin; Gilbertson, Richard J; Straathof, Karin; Green, Jordan J; Smith, Stuart; Rahman, Ruman; Kilday, John-Paul

    2016-01-01

    The first Workshop on Drug Delivery in Paediatric Brain Tumours was hosted in London by the charity Children with Cancer UK. The goals of the workshop were to break down the barriers to treating central nervous system (CNS) tumours in children, leading to new collaborations and further innovations in this under-represented and emotive field. These barriers include the physical delivery challenges presented by the blood-brain barrier, the underpinning reasons for the intractability of CNS cancers, and the practical difficulties of delivering cancer treatment to the brains of children. Novel techniques for overcoming these problems were discussed, new models brought forth, and experiences compared. PMID:27110286

  4. Highlights of Children with Cancer UK’s Workshop on Drug Delivery in Paediatric Brain Tumours

    PubMed Central

    Nailor, Audrey; Walker, David A; Jacques, Thomas S; Warren, Kathy E; Brem, Henry; Kearns, Pamela R; Greenwood, John; Penny, Jeffrey I; Pilkington, Geoffrey J; Carcaboso, Angel M; Fleischhack, Gudrun; Macarthur, Donald; Slavc, Irene; Meijer, Lisethe; Gill, Steven; Lowis, Stephen; van Vuurden, Dannis G; Pearl, Monica S; Clifford, Steven C; Morrissy, Sorana; Ivanov, Delyan P; Beccaria, Kévin; Gilbertson, Richard J; Straathof, Karin; Green, Jordan J; Smith, Stuart; Rahman, Ruman; Kilday, John-Paul

    2016-01-01

    The first Workshop on Drug Delivery in Paediatric Brain Tumours was hosted in London by the charity Children with Cancer UK. The goals of the workshop were to break down the barriers to treating central nervous system (CNS) tumours in children, leading to new collaborations and further innovations in this under-represented and emotive field. These barriers include the physical delivery challenges presented by the blood–brain barrier, the underpinning reasons for the intractability of CNS cancers, and the practical difficulties of delivering cancer treatment to the brains of children. Novel techniques for overcoming these problems were discussed, new models brought forth, and experiences compared. PMID:27110286

  5. Local Kernel for Brains Classification in Schizophrenia

    NASA Astrophysics Data System (ADS)

    Castellani, U.; Rossato, E.; Murino, V.; Bellani, M.; Rambaldelli, G.; Tansella, M.; Brambilla, P.

    In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorso-lateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.

  6. Brain tumours and exposure to pesticides: a case–control study in southwestern France

    PubMed Central

    Provost, Dorothée; Cantagrel, Anne; Lebailly, Pierre; Jaffré, Anne; Loyant, Véronique; Loiseau, Hugues; Vital, Anne; Brochard, Patrick; Baldi, Isabelle

    2007-01-01

    Background Brain tumours are often disabling and rapidly lethal; their aetiology is largely unknown. Among potential risk factors, pesticides are suspected. Objective To examine the relationship between exposure to pesticides and brain tumours in adults in a population‐based case–control study in southwestern France. Methods Between May 1999 and April 2001, 221 incident cases of brain tumours and 442 individually matched controls selected from the general population were enrolled. Histories of occupational and environmental exposures, medical and lifestyle information were collected. A cumulative index of occupational exposure to pesticides was created, based on expert review of lifelong jobs and tasks. Separate analyses were performed for gliomas and meningiomas. Results A non‐statistically significant increase in risk was found for brain tumours when all types of occupational exposure to pesticides were considered (OR = 1.29, 95% CI 0.87 to 1.91) and slightly higher but still non‐statistically significant when gliomas were considered separately (OR = 1.47, 95% CI 0.81 to 2.66). In the highest quartile of the cumulative index, a significant association was found for brain tumours (OR = 2.16, 95% CI 1.10 to 4.23) and for gliomas (OR = 3.21, 95% CI 1.13 to 9.11), but not for meningiomas. A significant increase in risk was also seen for the treatment of home plants (OR = 2.24, 95% CI 1.16 to 4.30) owing to environmental exposure to pesticides. Conclusions These data suggest that a high level of occupational exposure to pesticides might be associated with an excess risk of brain tumours, and especially of gliomas. PMID:17537748

  7. Brain extraction based on locally linear representation-based classification.

    PubMed

    Huang, Meiyan; Yang, Wei; Jiang, Jun; Wu, Yao; Zhang, Yu; Chen, Wufan; Feng, Qianjin

    2014-05-15

    Brain extraction is an important procedure in brain image analysis. Although numerous brain extraction methods have been presented, enhancing brain extraction methods remains challenging because brain MRI images exhibit complex characteristics, such as anatomical variability and intensity differences across different sequences and scanners. To address this problem, we present a Locally Linear Representation-based Classification (LLRC) method for brain extraction. A novel classification framework is derived by introducing the locally linear representation to the classical classification model. Under this classification framework, a common label fusion approach can be considered as a special case and thoroughly interpreted. Locality is important to calculate fusion weights for LLRC; this factor is also considered to determine that Local Anchor Embedding is more applicable in solving locally linear coefficients compared with other linear representation approaches. Moreover, LLRC supplies a way to learn the optimal classification scores of the training samples in the dictionary to obtain accurate classification. The International Consortium for Brain Mapping and the Alzheimer's Disease Neuroimaging Initiative databases were used to build a training dataset containing 70 scans. To evaluate the proposed method, we used four publicly available datasets (IBSR1, IBSR2, LPBA40, and ADNI3T, with a total of 241 scans). Experimental results demonstrate that the proposed method outperforms the four common brain extraction methods (BET, BSE, GCUT, and ROBEX), and is comparable to the performance of BEaST, while being more accurate on some datasets compared with BEaST. PMID:24525169

  8. Magnetic iron compounds in the human brain: a comparison of tumour and hippocampal tissue

    PubMed Central

    Brem, Franziska; Hirt, Ann M; Winklhofer, Michael; Frei, Karl; Yonekawa, Yasuhiro; Wieser, Heinz-Gregor; Dobson, Jon

    2006-01-01

    Iron is a central element in the metabolism of normal and malignant cells. Abnormalities in iron and ferritin expression have been observed in many types of cancer. Interest in characterizing iron compounds in the human brain has increased due to advances in determining a relationship between excess iron accumulation and neurological and neurodegenerative diseases. In this work, four different magnetic methods have been employed to characterize the iron phases and magnetic properties of brain tumour (meningiomas) tissues and non-tumour hippocampal tissues. Four main magnetic components can be distinguished: the diamagnetic matrix, nearly paramagnetic blood, antiferromagnetic ferrihydrite cores of ferritin and ferrimagnetic magnetite and/or maghemite. For the first time, open hysteresis loops have been observed on human brain tissue at room temperature. The hysteresis properties indicate the presence of magnetite and/or maghemite particles that exhibit stable single-domain (SD) behaviour at room temperature. A significantly higher concentration of magnetically ordered magnetite and/or maghemite and a higher estimated concentration of heme iron was found in the meningioma samples. First-order reversal curve diagrams on meningioma tissue further show that the stable SD particles are magnetostatically interacting, implying high-local concentrations (clustering) of these particles in brain tumours. These findings suggest that brain tumour tissue contains an elevated amount of remanent iron oxide phases. PMID:17015303

  9. Classification of CT-brain slices based on local histograms

    NASA Astrophysics Data System (ADS)

    Avrunin, Oleg G.; Tymkovych, Maksym Y.; Pavlov, Sergii V.; Timchik, Sergii V.; Kisała, Piotr; Orakbaev, Yerbol

    2015-12-01

    Neurosurgical intervention is a very complicated process. Modern operating procedures based on data such as CT, MRI, etc. Automated analysis of these data is an important task for researchers. Some modern methods of brain-slice segmentation use additional data to process these images. Classification can be used to obtain this information. To classify the CT images of the brain, we suggest using local histogram and features extracted from them. The paper shows the process of feature extraction and classification CT-slices of the brain. The process of feature extraction is specialized for axial cross-section of the brain. The work can be applied to medical neurosurgical systems.

  10. X-ray fluorescence study of the concentration of selected trace and minor elements in human brain tumours

    NASA Astrophysics Data System (ADS)

    Wandzilak, Aleksandra; Czyzycki, Mateusz; Radwanska, Edyta; Adamek, Dariusz; Geraki, Kalotina; Lankosz, Marek

    2015-12-01

    Neoplastic and healthy brain tissues were analysed to discern the changes in the spatial distribution and overall concentration of elements using micro X-ray fluorescence spectroscopy. High-resolution distribution maps of minor and trace elements such as P, S, Cl, K, Ca, Fe, Cu and Zn made it possible to distinguish between homogeneous cancerous tissue and areas where some structures could be identified, such as blood vessels and calcifications. Concentrations of the elements in the selected homogeneous areas of brain tissue were compared between tumours with various malignancy grades and with the controls. The study showed a decrease in the average concentration of Fe, P, S and Ca in tissues with high grades of malignancy as compared to the control group, whereas the concentration of Zn in these tissues was increased. The changes in the concentration were found to be correlated with the tumour malignancy grade. The efficacy of micro X-ray fluorescence spectroscopy to distinguish between various types of cancer based on the concentrations of studied elements was confirmed by multivariate discriminant analysis. Our analysis showed that the most important elements for tissue classification are Cu, K, Fe, Ca, and Zn. This method made it possible to correctly classify histopathological types in 99.93% of the cases used to build the model and in as much as 99.16% of new cases.

  11. Automatic brain tumour detection and neovasculature assessment with multiseries MRI analysis.

    PubMed

    Szwarc, Pawel; Kawa, Jacek; Rudzki, Marcin; Pietka, Ewa

    2015-12-01

    In this paper a novel multi-stage automatic method for brain tumour detection and neovasculature assessment is presented. First, the brain symmetry is exploited to register the magnetic resonance (MR) series analysed. Then, the intracranial structures are found and the region of interest (ROI) is constrained within them to tumour and peritumoural areas using the Fluid Light Attenuation Inversion Recovery (FLAIR) series. Next, the contrast-enhanced lesions are detected on the basis of T1-weighted (T1W) differential images before and after contrast medium administration. Finally, their vascularisation is assessed based on the Regional Cerebral Blood Volume (RCBV) perfusion maps. The relative RCBV (rRCBV) map is calculated in relation to a healthy white matter, also found automatically, and visualised on the analysed series. Three main types of brain tumours, i.e. HG gliomas, metastases and meningiomas have been subjected to the analysis. The results of contrast enhanced lesions detection have been compared with manual delineations performed independently by two experts, yielding 64.84% sensitivity, 99.89% specificity and 71.83% Dice Similarity Coefficient (DSC) for twenty analysed studies of subjects with brain tumours diagnosed. PMID:26183648

  12. Evaluation of lactate detection using selective multiple quantum coherence in phantoms and brain tumours

    PubMed Central

    Harris, L M; Tunariu, N; Messiou, C; Hughes, J; Wallace, T; DeSouza, N M; Leach, M O; Payne, G S

    2015-01-01

    Lactate is a product of glucose metabolism. In tumour tissues, which exhibit enhanced glycolytic metabolism, lactate signals may be elevated, making lactate a potential useful tumour biomarker. Methods of lactate quantitation are complicated because of overlap between the lactate methyl doublet CH3 resonance and a lipid resonance at 1.3 ppm. This study presents the use of a selective homonuclear multiple quantum coherence transfer sequence (SelMQC-CSI), at 1.5 T, to better quantify lactate in the presence of lipids. Work performed on phantoms showed good lactate detection (49%) and lipid suppression (98%) efficiencies. To evaluate the method in the brain, the sequence was tested on a group of 23 patients with treated brain tumours, either glioma (N = 20) or secondary metastases in the brain (N = 3). Here it was proved to be of use in determining lactate concentrations in vivo. Lactate was clearly seen in SelMQC spectra of glioma, even in the presence of lipids, with high grade glioma (7.3 ± 1.9 mM, mean ± standard deviation) having higher concentrations than low grade glioma (1.9 ± 1.5 mM, p = 0.048). Lactate was not seen in secondary metastases in the brain. SelMQC-CSI is shown to be a useful technique for measuring lactate in tumours whose signals are otherwise contaminated by lipid. © 2015 The Authors NMR in Biomedicine Published by John Wiley & Sons Ltd. PMID:25586623

  13. Transient Global Amnesia and Brain Tumour: Chance Concurrence or Aetiological Association? Case Report and Systematic Literature Review

    PubMed Central

    Milburn-McNulty, Phil; Larner, Andrew J.

    2015-01-01

    We report a patient presenting with episodes of transient amnesia, some with features suggestive of transient global amnesia (TGA), and some more reminiscent of transient epileptic amnesia. Investigation with neuroimaging revealed an intrinsic lesion in the right amygdala, with features suggestive of low-grade neoplasia. We undertook a systematic review of the literature on TGA and brain tumour. Fewer than 20 cases were identified, some of which did not conform to the clinical diagnostic criteria for TGA. Hence, the concurrence of brain tumour and TGA is very rare and of doubtful aetiological relevance. In some brain tumour-associated cases, epilepsy may be masquerading as TGA. PMID:25802501

  14. Development of a positron probe for localization and excision of brain tumours during surgery

    NASA Astrophysics Data System (ADS)

    Bogalhas, F.; Charon, Y.; Duval, M.-A.; Lefebvre, F.; Palfi, S.; Pinot, L.; Siebert, R.; Ménard, L.

    2009-07-01

    The survival outcome of patients suffering from gliomas is directly linked to the complete surgical resection of the tumour. To help the surgeons to delineate precisely the boundaries of the tumour, we developed an intraoperative positron probe with background noise rejection capability. The probe was designed to be directly coupled to the excision tool such that detection and removal of the radiolabelled tumours could be simultaneous. The device consists of two exchangeable detection heads composed of clear and plastic scintillating fibres. Each head is coupled to an optic fibre bundle that exports the scintillating light to a photodetection and processing electronic module placed outside the operative wound. The background rejection method is based on a real-time subtraction technique. The measured probe sensitivity for 18F was 1.1 cps kBq-1 ml-1 for the small head and 3.4 cps kBq-1 ml-1 for the large head. The mean spatial resolution was 1.6 mm FWHM on the detector surface. The γ-ray rejection efficiency measured by realistic brain phantom modelling of the surgical cavity was 99.4%. This phantom also demonstrated the ability of the probe to detect tumour discs as small as 5 mm in diameter (20 mg) for tumour-to-background ratios higher than 3:1 and with an acquisition time around 4 s at each scanning step. These results indicate that our detector could be a useful complement to existing techniques for the accurate excision of brain tumour tissue and more generally to improve the efficiency of radio-guided cancer surgery.

  15. Relative survival of patients with non-malignant central nervous system tumours: a descriptive study by the Austrian Brain Tumour Registry

    PubMed Central

    Woehrer, A; Hackl, M; Waldhör, T; Weis, S; Pichler, J; Olschowski, A; Buchroithner, J; Maier, H; Stockhammer, G; Thomé, C; Haybaeck, J; Payer, F; von Campe, G; Kiefer, A; Würtz, F; Vince, G H; Sedivy, R; Oberndorfer, S; Marhold, F; Bordihn, K; Stiglbauer, W; Gruber-Mösenbacher, U; Bauer, R; Feichtinger, J; Reiner-Concin, A; Grisold, W; Marosi, C; Preusser, M; Dieckmann, K; Slavc, I; Gatterbauer, B; Widhalm, G; Haberler, C; Hainfellner, J A

    2014-01-01

    Background: Unlike malignant primary central nervous system (CNS) tumours outcome data on non-malignant CNS tumours are scarce. For patients diagnosed from 1996 to 2002 5-year relative survival of only 85.0% has been reported. We investigated this rate in a contemporary patient cohort to update information on survival. Methods: We followed a cohort of 3983 cases within the Austrian Brain Tumour Registry. All patients were newly diagnosed from 2005 to 2010 with a histologically confirmed non-malignant CNS tumour. Vital status, cause of death, and population life tables were obtained by 31 December 2011 to calculate relative survival. Results: Overall 5-year relative survival was 96.1% (95% CI 95.1–97.1%), being significantly lower in tumours of borderline (90.2%, 87.2–92.7%) than benign behaviour (97.4%, 96.3–98.3%). Benign tumour survival ranged from 86.8 for neurofibroma to 99.7% for Schwannoma; for borderline tumours survival rates varied from 83.2 for haemangiopericytoma to 98.4% for myxopapillary ependymoma. Cause of death was directly attributed to the CNS tumour in 39.6%, followed by other cancer (20.4%) and cardiovascular disease (15.8%). Conclusion: The overall excess mortality in patients with non-malignant CNS tumours is 5.5%, indicating a significant improvement in survival over the last decade. Still, the remaining adverse impact on survival underpins the importance of systematic registration of these tumours. PMID:24253501

  16. Spatial prior in SVM-based classification of brain images

    NASA Astrophysics Data System (ADS)

    Cuingnet, Rémi; Chupin, Marie; Benali, Habib; Colliot, Olivier

    2010-03-01

    This paper introduces a general framework for spatial prior in SVM-based classification of brain images based on Laplacian regularization. Most existing methods include spatial prior by adding a feature aggregation step before the SVM classification. The problem of the aggregation step is that the individual information of each feature is lost. Our framework enables to avoid this shortcoming by including the spatial prior directly in the SVM. We demonstrate that this framework can be used to derive embedded regularization corresponding to existing methods for classification of brain images and propose an efficient way to implement them. This framework is illustrated on the classification of MR images from 55 patients with Alzheimer's disease and 82 elderly controls selected from the ADNI database. The results demonstrate that the proposed algorithm enables introducing straightforward and anatomically consistent spatial prior into the classifier.

  17. Simple Fully Automated Group Classification on Brain fMRI

    SciTech Connect

    Honorio, J.; Goldstein, R.; Honorio, J.; Samaras, D.; Tomasi, D.; Goldstein, R.Z.

    2010-04-14

    We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.

  18. Multidisciplinary assessment of fitness to drive in brain tumour patients in southwestern Ontario: a grey matter

    PubMed Central

    Chan, E.; Louie, A.V.; Hanna, M.; Bauman, G.S.; Fisher, B.J.; Palma, D.A.; Rodrigues, G.B.; Sathya, A.; D’Souza, D.P.

    2013-01-01

    Background Neurocognitive impairments from brain tumours may interfere with the ability to drive safely. In 9 of 13 Canadian provinces and territories, physicians have a legal obligation to report patients who may be medically unfit to drive. To complicate matters, brain tumour patients are managed by a multidisciplinary team; the physician most responsible to make the report of unfitness is often not apparent. The objective of the present study was to determine the attitudes and reporting practices of physicians caring for these patients. Methods A 17-question survey distributed to physicians managing brain tumour patients elicited Respondent demographicsKnowledge about legislative requirementsExperience of reportingBarriers and attitudes to reporting Fisher exact tests were performed to assess differences in responses between family physicians (fps) and specialists. Results Of 467 physicians sent surveys, 194 responded (42%), among whom 81 (42%) were specialists and 113 (58%) were fps. Compared with the specialists, the fps were significantly less comfortable with reporting, less likely to consider reporting, less likely to have patients inquire about driving, and less likely to discuss driving implications. A lack of tools, concern for the patient–physician relationship, and a desire to preserve patient quality of life were the most commonly cited barriers in determining medical fitness of patients to drive. Conclusions Legal requirements to report medically unfit drivers put physicians in the difficult position of balancing patient autonomy and public safety. More comprehensive and definitive guidelines would be helpful in assisting physicians with this public health issue. PMID:23443064

  19. Intracavitary moderator balloon combined with 252Cf brachytherapy and boron neutron capture therapy, improving dosimetry in brain tumour and infiltrations

    PubMed Central

    Brandão, S F

    2015-01-01

    Objective: This article proposes a combination of californium-252 (252Cf) brachytherapy, boron neutron capture therapy (BNCT) and an intracavitary moderator balloon catheter applied to brain tumour and infiltrations. Methods: Dosimetric evaluations were performed on three protocol set-ups: 252Cf brachytherapy combined with BNCT (Cf-BNCT); Cf-BNCT with a balloon catheter filled with light water (LWB) and the same set-up with heavy water (HWB). Results: Cf-BNCT-HWB has presented dosimetric advantages to Cf-BNCT-LWB and Cf-BNCT in infiltrations at 2.0–5.0 cm from the balloon surface. However, Cf-BNCT-LWB has shown superior dosimetry up to 2.0 cm from the balloon surface. Conclusion: Cf-BNCT-HWB and Cf-BNCT-LWB protocols provide a selective dose distribution for brain tumour and infiltrations, mainly further from the 252Cf source, sparing the normal brain tissue. Advances in knowledge: Malignant brain tumours grow rapidly and often spread to adjacent brain tissues, leading to death. Improvements in brain radiation protocols have been continuously achieved; however, brain tumour recurrence is observed in most cases. Cf-BNCT-LWB and Cf-BNCT-HWB represent new modalities for selectively combating brain tumour infiltrations and metastasis. PMID:25927876

  20. Motor deficits correlate with resting state motor network connectivity in patients with brain tumours

    PubMed Central

    Mikell, Charles B.; Youngerman, Brett E.; Liston, Conor; Sisti, Michael B.; Bruce, Jeffrey N.; Small, Scott A.; McKhann, Guy M.

    2012-01-01

    While a tumour in or abutting primary motor cortex leads to motor weakness, how tumours elsewhere in the frontal or parietal lobes affect functional connectivity in a weak patient is less clear. We hypothesized that diminished functional connectivity in a distributed network of motor centres would correlate with motor weakness in subjects with brain masses. Furthermore, we hypothesized that interhemispheric connections would be most vulnerable to subtle disruptions in functional connectivity. We used task-free functional magnetic resonance imaging connectivity to probe motor networks in control subjects and patients with brain tumours (n = 22). Using a control dataset, we developed a method for automated detection of key nodes in the motor network, including the primary motor cortex, supplementary motor area, premotor area and superior parietal lobule, based on the anatomic location of the hand-motor knob in the primary motor cortex. We then calculated functional connectivity between motor network nodes in control subjects, as well as patients with and without brain masses. We used this information to construct weighted, undirected graphs, which were then compared to variables of interest, including performance on a motor task, the grooved pegboard. Strong connectivity was observed within the identified motor networks between all nodes bilaterally, and especially between the primary motor cortex and supplementary motor area. Reduced connectivity was observed in subjects with motor weakness versus subjects with normal strength (P < 0.001). This difference was driven mostly by decreases in interhemispheric connectivity between the primary motor cortices (P < 0.05) and between the left primary motor cortex and the right premotor area (P < 0.05), as well as other premotor area connections. In the subjects without motor weakness, however, performance on the grooved pegboard did not relate to interhemispheric connectivity, but rather was inversely

  1. USP11 regulates PML stability to control Notch-induced malignancy in brain tumours.

    PubMed

    Wu, Hsin-Chieh; Lin, Yu-Ching; Liu, Cheng-Hsin; Chung, Hsiang-Ching; Wang, Ya-Ting; Lin, Ya-Wen; Ma, Hsin-I; Tu, Pang-Hsien; Lawler, Sean E; Chen, Ruey-Hwa

    2014-01-01

    The promyelocytic leukaemia (PML) protein controls multiple tumour suppressive functions and is downregulated in diverse types of human cancers through incompletely characterized post-translational mechanisms. Here we identify USP11 as a PML regulator by RNAi screening. USP11 deubiquitinates and stabilizes PML, thereby counteracting the functions of PML ubiquitin ligases RNF4 and the KLHL20-Cul3 (Cullin 3)-Roc1 complex. We find that USP11 is transcriptionally repressed through a Notch/Hey1-dependent mechanism, leading to PML destabilization. In human glioma, Hey1 upregulation correlates with USP11 and PML downregulation and with high-grade malignancy. The Notch/Hey1-induced downregulation of USP11 and PML not only confers multiple malignant characteristics of aggressive glioma, including proliferation, invasiveness and tumour growth in an orthotopic mouse model, but also potentiates self-renewal, tumour-forming capacity and therapeutic resistance of patient-derived glioma-initiating cells. Our study uncovers a PML degradation mechanism through Notch/Hey1-induced repression of the PML deubiquitinase USP11 and suggests an important role for this pathway in brain tumour pathogenesis. PMID:24487962

  2. Early medical rehabilitation after neurosurgical treatment of malignant brain tumours in Slovenia

    PubMed Central

    Kos, Natasa; Kos, Boris

    2016-01-01

    Abstract Background The number of patients with malignant brain tumours is on the rise, but due to the novel treatment methods the survival rates are higher. Despite increased survival the consequences of tumour properties and treatment can have a significant negative effect on the patients’ quality of life. Providing timely and appropriate rehabilitation interventions is an important aspect of patient treatment and should be started immediately after surgery. The most important goal of rehabilitation is to prevent complications that could have a negative effect on the patients’ ability to function. Conclusions By using individually tailored early rehabilitation it is often possible to achieve the patients’ independence in mobility as well as in performing daily tasks before leaving the hospital. A more precise evaluation of the patients’ functional state after completing additional oncologic therapy should be performed to stratify the patients who should be directed to complex rehabilitation treatment. The chances of a good functional outcome in patients with malignant brain tumours could be increased with good early medical rehabilitation treatment. PMID:27247545

  3. Classification of brain tumors using MRI and MRS data

    NASA Astrophysics Data System (ADS)

    Wang, Qiang; Liacouras, Eirini Karamani; Miranda, Erickson; Kanamalla, Uday S.; Megalooikonomou, Vasileios

    2007-03-01

    We study the problem of classifying brain tumors as benign or malignant using information from magnetic resonance (MR) imaging and magnetic resonance spectroscopy (MRS) to assist in clinical diagnosis. The proposed approach consists of several steps including segmentation, feature extraction, feature selection, and classification model construction. Using an automated segmentation technique based on fuzzy connectedness we accurately outline the tumor mass boundaries in the MR images so that further analysis concentrates on these regions of interest (ROIs). We then apply a concentric circle technique on the ROIs to extract features that are utilized by the classification algorithms. To remove redundant features, we perform feature selection where only those features with discriminatory information (among classes) are used in the model building process. The involvement of MRS features further improves the classification accuracy of the model. Experimental results demonstrate the effectiveness of the proposed approach in classifying brain tumors in MR images.

  4. Gene expression-based classifications of fibroadenomas and phyllodes tumours of the breast.

    PubMed

    Vidal, Maria; Peg, Vicente; Galván, Patricia; Tres, Alejandro; Cortés, Javier; Ramón y Cajal, Santiago; Rubio, Isabel T; Prat, Aleix

    2015-06-01

    Fibroepithelial tumors (FTs) of the breast are a heterogeneous group of lesions ranging from fibroadenomas (FAD) to phyllodes tumors (PT) (benign, borderline, malignant). Further understanding of their molecular features and classification might be of clinical value. In this study, we analysed the expression of 105 breast cancer-related genes, including the 50 genes of the PAM50 intrinsic subtype predictor and 12 genes of the Claudin-low subtype predictor, in a panel of 75 FTs (34 FADs, 5 juvenile FADs, 20 benign PTs, 5 borderline PTs and 11 malignant PTs) with clinical follow-up. In addition, we compared the expression profiles of FTs with those of 14 normal breast tissues and 49 primary invasive ductal carcinomas (IDCs). Our results revealed that the levels of expression of all breast cancer-related genes can discriminate the various groups of FTs, together with normal breast tissues and IDCs (False Discovery Rate < 5%). Among FTs, the levels expression of proliferation-related genes (e.g. CCNB1 and MKI67) and mesenchymal/epithelial-related (e.g. CLDN3 and EPCAM) genes were found to be most discriminative. As expected, FADs showed the highest and lowest expression of epithelial- and proliferation-related genes, respectively, whereas malignant PTs showed the opposite expression pattern. Interestingly, the overall profile of benign PTs was found more similar to FADs and normal breast tissues than the rest of tumours, including juvenile FADs. Within the dataset of IDCs and normal breast tissues, the vast majority of FADs, juvenile FADs, benign PTs and borderline PTs were identified as Normal-like by intrinsic breast cancer subtyping, whereas 7 (63.6%) and 3 (27.3%) malignant PTs were identified as Claudin-low and Basal-like, respectively. Finally, we observed that the previously described PAM50 risk of relapse prognostic score better predicted outcome in FTs than the morphological classification, even within PTs-only. Our results suggest that classification of FTs

  5. Knowledge-based classification of neuronal fibers in entire brain.

    PubMed

    Xia, Yan; Turken, U; Whitfield-Gabrieli, Susan L; Gabrieli, John D

    2005-01-01

    This work presents a framework driven by parcellation of brain gray matter in standard normalized space to classify the neuronal fibers obtained from diffusion tensor imaging (DTI) in entire human brain. Classification of fiber bundles into groups is an important step for the interpretation of DTI data in terms of functional correlates of white matter structures. Connections between anatomically delineated brain regions that are considered to form functional units, such as a short-term memory network, are identified by first clustering fibers based on their terminations in anatomically defined zones of gray matter according to Talairach Atlas, and then refining these groups based on geometric similarity criteria. Fiber groups identified this way can then be interpreted in terms of their functional properties using knowledge of functional neuroanatomy of individual brain regions specified in standard anatomical space, as provided by functional neuroimaging and brain lesion studies. PMID:16685847

  6. Natural image classification driven by human brain activity

    NASA Astrophysics Data System (ADS)

    Zhang, Dai; Peng, Hanyang; Wang, Jinqiao; Tang, Ming; Xue, Rong; Zuo, Zhentao

    2016-03-01

    Natural image classification has been a hot topic in computer vision and pattern recognition research field. Since the performance of an image classification system can be improved by feature selection, many image feature selection methods have been developed. However, the existing supervised feature selection methods are typically driven by the class label information that are identical for different samples from the same class, ignoring with-in class image variability and therefore degrading the feature selection performance. In this study, we propose a novel feature selection method, driven by human brain activity signals collected using fMRI technique when human subjects were viewing natural images of different categories. The fMRI signals associated with subjects viewing different images encode the human perception of natural images, and therefore may capture image variability within- and cross- categories. We then select image features with the guidance of fMRI signals from brain regions with active response to image viewing. Particularly, bag of words features based on GIST descriptor are extracted from natural images for classification, and a sparse regression base feature selection method is adapted to select image features that can best predict fMRI signals. Finally, a classification model is built on the select image features to classify images without fMRI signals. The validation experiments for classifying images from 4 categories of two subjects have demonstrated that our method could achieve much better classification performance than the classifiers built on image feature selected by traditional feature selection methods.

  7. Management of electrolyte and fluid disorders after brain surgery for pituitary/suprasellar tumours.

    PubMed

    Edate, Sujata; Albanese, Assunta

    2015-01-01

    Disturbances in salt and water balances are relatively common in children after brain surgeries for suprasellar and pituitary tumours, presenting diagnostic and therapeutic challenges. Although hypernatraemia associated with central diabetes insipidus is commonly encountered, it is hyponatraemia (HN) that poses more of a diagnostic dilemma. The main differential diagnoses causing HN are the syndrome of inappropriate antidiuretic hormone secretion, marked by inappropriate retention of water, and cerebral salt wasting, characterized by polyuria and natriuresis. Diagnosis and management can be even more difficult when these conditions precede or coexist with each other. These diagnostic and therapeutic dilemmas are discussed in detail in this review. PMID:25677941

  8. Value of serial stereotactic biopsies and impedance monitoring in the treatment of deep brain tumours.

    PubMed Central

    Broggi, G; Franzini, A

    1981-01-01

    Thirty-five patients with deep brain tumours have been submitted to transtumoral stereotactic impedance monitoring and serial biopsy. The direct examination of the biopsy samples confirmed the presumptive clinical and neuroradiological diagnosis in 25 patients, but in 10 patients the histological diagnosis differed from the presumptive one. In this second group the treatment was changed as a result of the histological findings. Stereotactic biopsy avoided the risks of "blind" management. The technique, the indications and the diagnostic advantages of stereotactic biopsy are reported with two illustrative cases. Images PMID:7021770

  9. Hyperbaric oxygen as an adjunctive therapy in treatment of malignancies, including brain tumours.

    PubMed

    Stępień, Katarzyna; Ostrowski, Robert P; Matyja, Ewa

    2016-09-01

    Hyperbaric oxygen (HBO) therapy is widely used as an adjunctive treatment for various pathological states, predominantly related to hypoxic and/or ischaemic conditions. It also holds promise as an approach to overcoming the problem of oxygen deficiency in the poorly oxygenated regions of the neoplastic tissue. Occurrence of local hypoxia within the central areas of solid tumours is one of the major issues contributing to ineffective medical treatment. However, in anti-cancer therapy, HBO alone gives a limited curative effect and is typically not applied by itself. More often, HBO is used as an adjuvant treatment along with other therapeutic modalities, such as radio- and chemotherapy. This review outlines the existing data regarding the medical use of HBO in cancer treatment, with a particular focus on the use of HBO in the treatment of brain tumours. We conclude that the administration of HBO can provide many clinical benefits in the treatment of tumours, including management of highly malignant gliomas. Applied immediately before irradiation, it is safe and well tolerated by patients, causing rare and limited side effects. The results obtained with a combination of HBO/radiotherapy protocol proved to be especially favourable compared to radiation treatment alone. HBO can also increase the cytostatic effect of certain drugs, which may render standard chemotherapy more effective. The currently available data support the legitimacy of conducting further research on the use of HBO in the treatment of malignancies. PMID:27485098

  10. Classification of Traumatic Brain Injury for Targeted Therapies

    PubMed Central

    Saatman, Kathryn E.; Duhaime, Ann-Christine; Bullock, Ross; Maas, Andrew I.R.; Valadka, Alex

    2008-01-01

    Abstract The heterogeneity of traumatic brain injury (TBI) is considered one of the most significant barriers to finding effective therapeutic interventions. In October, 2007, the National Institute of Neurological Disorders and Stroke, with support from the Brain Injury Association of America, the Defense and Veterans Brain Injury Center, and the National Institute of Disability and Rehabilitation Research, convened a workshop to outline the steps needed to develop a reliable, efficient and valid classification system for TBI that could be used to link specific patterns of brain and neurovascular injury with appropriate therapeutic interventions. Currently, the Glasgow Coma Scale (GCS) is the primary selection criterion for inclusion in most TBI clinical trials. While the GCS is extremely useful in the clinical management and prognosis of TBI, it does not provide specific information about the pathophysiologic mechanisms which are responsible for neurological deficits and targeted by interventions. On the premise that brain injuries with similar pathoanatomic features are likely to share common pathophysiologic mechanisms, participants proposed that a new, multidimensional classification system should be developed for TBI clinical trials. It was agreed that preclinical models were vital in establishing pathophysiologic mechanisms relevant to specific pathoanatomic types of TBI and verifying that a given therapeutic approach improves outcome in these targeted TBI types. In a clinical trial, patients with the targeted pathoanatomic injury type would be selected using an initial diagnostic entry criterion, including their severity of injury. Coexisting brain injury types would be identified and multivariate prognostic modeling used for refinement of inclusion/exclusion criteria and patient stratification. Outcome assessment would utilize endpoints relevant to the targeted injury type. Advantages and disadvantages of currently available diagnostic, monitoring, and

  11. The brain MRI classification problem from wavelets perspective

    NASA Astrophysics Data System (ADS)

    Bendib, Mohamed M.; Merouani, Hayet F.; Diaba, Fatma

    2015-02-01

    Haar and Daubechies 4 (DB4) are the most used wavelets for brain MRI (Magnetic Resonance Imaging) classification. The former is simple and fast to compute while the latter is more complex and offers a better resolution. This paper explores the potential of both of them in performing Normal versus Pathological discrimination on the one hand, and Multiclassification on the other hand. The Whole Brain Atlas is used as a validation database, and the Random Forest (RF) algorithm is employed as a learning approach. The achieved results are discussed and statistically compared.

  12. Unsupervised classification of operator workload from brain signals

    NASA Astrophysics Data System (ADS)

    Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

    2016-06-01

    Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects’ error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

  13. Brain tumours and cigarette smoking: analysis of the INTERPHONE Canada case–control study

    PubMed Central

    2014-01-01

    Background There is conflicting evidence regarding the associations between cigarette smoking and glioma or meningioma. Our purpose is to provide further evidence on these possible associations. Methods We conducted a set of case–control studies in three Canadian cities, Montreal, Ottawa and Vancouver. The study included 166 subjects with glioma, 93 subjects with meningioma, and 648 population-based controls. A lifetime history of cigarette smoking was collected and various smoking indices were computed. Multivariable logistic regression was used to estimate odds ratios (ORs) between smoking and each of the two types of brain tumours. Results Adjusted ORs between smoking and each type of brain tumour were not significantly elevated for all smokers combined or for smokers with over 15 pack-years ((packs / day) x years) accumulated. We tested for interactions between smoking and several sociodemographic variables; the interaction between smoking and education on glioma risk was significant, with smoking showing an elevated OR among subjects with lower education and an OR below unity among subjects with higher education. Conclusion Except for an unexplained and possibly artefactual excess risk in one population subgroup, we found little or no evidence of an association between smoking and either glioma or meningioma. PMID:24972852

  14. Image-guided microbeam irradiation to brain tumour bearing mice using a carbon nanotube X-ray source array

    PubMed Central

    Zhang, Lei; Yuan, Hong; Burk, Laurel M; Inscoe, Christy R; Hadsell, Michael J; Chtcheprov, Pavel; Lee, Yueh Z; Lu, Jianping; Chang, Sha; Zhou, Otto

    2014-01-01

    Microbeam radiation therapy (MRT) is a promising experimental and preclinical radiotherapy method for cancer treatment. Synchrotron based MRT experiments have shown that spatially fractionated microbeam radiation has the unique capability of preferentially eradicating tumour cells while sparing normal tissue in brain tumour bearing animal models. We recently demonstrated the feasibility of generating orthovoltage microbeam radiation with an adjustable microbeam width using a carbon nanotube based X-ray source array. Here we report the preliminary results from our efforts in developing an image guidance procedure for the targeted delivery of the narrow microbeams to the small tumour region in the mouse brain. Magnetic resonance imaging was used for tumour identification, and on-board X-ray radiography was used for imaging of landmarks without contrast agents. The two images were aligned using 2D rigid body image registration to determine the relative position of the tumour with respect to a landmark. The targeting accuracy and consistency were evaluated by first irradiating a group of mice inoculated with U87 human glioma brain tumours using the present protocol and then determining the locations of the microbeam radiation tracks using γ-H2AX immunofluorescence staining. The histology results showed that among 14 mice irradiated, 11 received the prescribed number of microbeams on the targeted tumour, with an average localization accuracy of 454 μm measured directly from the histology (537 μm if measured from the registered histological images). Two mice received one of the three prescribed microbeams on the tumour site. One mouse was excluded from the analysis due to tissue staining errors. PMID:24556798

  15. Image-guided microbeam irradiation to brain tumour bearing mice using a carbon nanotube x-ray source array.

    PubMed

    Zhang, Lei; Yuan, Hong; Burk, Laurel M; Inscoe, Christy R; Hadsell, Michael J; Chtcheprov, Pavel; Lee, Yueh Z; Lu, Jianping; Chang, Sha; Zhou, Otto

    2014-03-01

    Microbeam radiation therapy (MRT) is a promising experimental and preclinical radiotherapy method for cancer treatment. Synchrotron based MRT experiments have shown that spatially fractionated microbeam radiation has the unique capability of preferentially eradicating tumour cells while sparing normal tissue in brain tumour bearing animal models. We recently demonstrated the feasibility of generating orthovoltage microbeam radiation with an adjustable microbeam width using a carbon nanotube based x-ray source array. Here we report the preliminary results from our efforts in developing an image guidance procedure for the targeted delivery of the narrow microbeams to the small tumour region in the mouse brain. Magnetic resonance imaging was used for tumour identification, and on-board x-ray radiography was used for imaging of landmarks without contrast agents. The two images were aligned using 2D rigid body image registration to determine the relative position of the tumour with respect to a landmark. The targeting accuracy and consistency were evaluated by first irradiating a group of mice inoculated with U87 human glioma brain tumours using the present protocol and then determining the locations of the microbeam radiation tracks using γ-H2AX immunofluorescence staining. The histology results showed that among 14 mice irradiated, 11 received the prescribed number of microbeams on the targeted tumour, with an average localization accuracy of 454 µm measured directly from the histology (537 µm if measured from the registered histological images). Two mice received one of the three prescribed microbeams on the tumour site. One mouse was excluded from the analysis due to tissue staining errors. PMID:24556798

  16. Image-guided microbeam irradiation to brain tumour bearing mice using a carbon nanotube x-ray source array

    NASA Astrophysics Data System (ADS)

    Zhang, Lei; Yuan, Hong; Burk, Laurel M.; Inscoe, Christy R.; Hadsell, Michael J.; Chtcheprov, Pavel; Lee, Yueh Z.; Lu, Jianping; Chang, Sha; Zhou, Otto

    2014-03-01

    Microbeam radiation therapy (MRT) is a promising experimental and preclinical radiotherapy method for cancer treatment. Synchrotron based MRT experiments have shown that spatially fractionated microbeam radiation has the unique capability of preferentially eradicating tumour cells while sparing normal tissue in brain tumour bearing animal models. We recently demonstrated the feasibility of generating orthovoltage microbeam radiation with an adjustable microbeam width using a carbon nanotube based x-ray source array. Here we report the preliminary results from our efforts in developing an image guidance procedure for the targeted delivery of the narrow microbeams to the small tumour region in the mouse brain. Magnetic resonance imaging was used for tumour identification, and on-board x-ray radiography was used for imaging of landmarks without contrast agents. The two images were aligned using 2D rigid body image registration to determine the relative position of the tumour with respect to a landmark. The targeting accuracy and consistency were evaluated by first irradiating a group of mice inoculated with U87 human glioma brain tumours using the present protocol and then determining the locations of the microbeam radiation tracks using γ-H2AX immunofluorescence staining. The histology results showed that among 14 mice irradiated, 11 received the prescribed number of microbeams on the targeted tumour, with an average localization accuracy of 454 µm measured directly from the histology (537 µm if measured from the registered histological images). Two mice received one of the three prescribed microbeams on the tumour site. One mouse was excluded from the analysis due to tissue staining errors.

  17. Detection of comorbidities and synchronous primary tumours via thoracic radiography and abdominal ultrasonography and their influence on treatment outcome in dogs with soft tissue sarcomas, primary brain tumours and intranasal tumours.

    PubMed

    Bigio Marcello, A; Gieger, T L; Jiménez, D A; Granger, L Abbigail

    2015-12-01

    Canine soft tissue sarcomas (STS), primary brain tumours and intranasal tumours are commonly treated with radiotherapy (RT). Given the low metastatic potential of these tumours, recommendations regarding imaging tests as staging are variable among institutions. The purpose of our study was to describe thoracic radiographic and abdominal ultrasonographic findings in dogs with these neoplasms and to investigate association of abnormal findings with alterations in recommended treatment. Medical records from 101 dogs, each having thoracic radiographs and abdominal ultrasound performed as part of their staging, were reviewed. In 98 of 101 (97%), imaging abnormalities were detected, 27% of which were further investigated with fine needle aspiration cytology or biopsy. Nine percent of the detected abnormalities were considered serious comorbidities that altered treatment recommendations, including 3 (3%) which were confirmed as synchronous primary neoplasms. These findings may influence recommendations regarding the decision to perform thoracic radiographs and abdominal ultrasound prior to initiation of RT. PMID:23968175

  18. Analysis of fluid in cysts accompanying various primary and metastatic brain tumours: proteins, lactate and pH.

    PubMed

    Lohle, P N; Wurzer, H A; Seelen, P J; Kingma, L M; Go, K G

    1998-01-01

    There is a growing interest in cystic lesions of the brain. By examining the cyst content of brain tumours more insight into the pathogenesis of cyst formation has been found. In this study, 39 samples of cyst fluid of 34 patients with a cyst accompanying a brain tumour were collected and studied biochemically regarding their protein content, lactate and pH. In this study we investigated the relation between the grade of malignancy and the lactate-concentration and the discrepancy between the high levels of lactate in cysts and their alkaline environment. The results of the measurements of the concentrations of albumin, immunoglobulines (IgG, IgA, IgM) and alpha 2-macroglobulin in cysts compared to those in sera suggest that cyst formation associated with tumour is based upon a disruption of the blood-brain barrier with exudation of plasma proteins into the brain parenchyma resulting in accumulation of fluid (oedema) and eventually in formation of a cyst. There appears to be a positive relation between the grade of malignancy and the concentration of lactate in the cysts with a significant 2-fold increase in lactate concentration in malignant tumour cysts compared to the more benign tumour cysts (p < 0.001) probably on account of aerobic glycolysis with production of lactate by the tumour. The measured pH values in the cysts were above normal, resulting in a discrepancy of the high levels of lactate in the cyst with the alkaline environment and this suggests efflux of H(+)-ions by a Na/H exchange mechanism to compensate for the change of pH. PMID:9522902

  19. Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis.

    PubMed

    Su, Zhengyu; Zeng, Wei; Wang, Yalin; Lu, Zhong-Lin; Gu, Xianfeng

    2015-01-01

    Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory. By Poincare uniformization theorem, all shapes can be conformally deformed to one of the three canonical spaces: the unit sphere, the Euclidean plane or the hyperbolic plane. The uniformization map will distort the surface area elements. The area-distortion factor gives a probability measure on the canonical uniformization space. All the probability measures on a Riemannian manifold form the Wasserstein space. Given any 2 probability measures, there is a unique optimal mass transport map between them, the transportation cost defines the Wasserstein distance between them. Wasserstein distance gives a Riemannian metric for the Wasserstein space. It intrinsically measures the dissimilarities between shapes and thus has the potential for shape classification. To the best of our knowledge, this is the first. work to introduce the optimal mass transport map to general Riemannian manifolds. The method is based on geodesic power Voronoi diagram. Comparing to the conventional methods, our approach solely depends on Riemannian metrics and is invariant under rigid motions and scalings, thus it intrinsically measures shape distance. Experimental results on classifying brain cortical surfaces with different intelligence quotients demonstrated the efficiency and efficacy of our method. PMID:26221691

  20. Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis

    PubMed Central

    Su, Zhengyu; Zeng, Wei; Wang, Yalin; Lu, Zhong-Lin; Gu, Xianfeng

    2015-01-01

    Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory. By Poincare uniformization theorem, all shapes can be conformally deformed to one of the three canonical spaces: the unit sphere, the Euclidean plane or the hyperbolic plane. The uniformization map will distort the surface area elements. The area-distortion factor gives a probability measure on the canonical uniformization space. All the probability measures on a Riemannian manifold form the Wasserstein space. Given any 2 probability measures, there is a unique optimal mass transport map between them, the transportation cost defines the Wasserstein distance between them. Wasserstein distance gives a Riemannian metric for the Wasserstein space. It intrinsically measures the dissimilarities between shapes and thus has the potential for shape classification. To the best of our knowledge, this is the first work to introduce the optimal mass transport map to general Riemannian manifolds. The method is based on geodesic power Voronoi diagram. Comparing to the conventional methods, our approach solely depends on Riemannian metrics and is invariant under rigid motions and scalings, thus it intrinsically measures shape distance. Experimental results on classifying brain cortical surfaces with different intelligence quotients demonstrated the efficiency and efficacy of our method. PMID:26221691

  1. Apoptosis induced in vivo by photodynamic therapy in normal brain and intracranial tumour tissue

    PubMed Central

    Lilge, L; Portnoy, M; Wilson, B C

    2000-01-01

    The apoptotic response of normal brain and intracranial VX2 tumour following photodynamic therapy (PDT) mediated by 5 different photosensitizers (Photofrin, 5-aminolaevulinic acid (ALA)-induced protoporphyrin IX (PpIX), chloroaluminium phthalocyanine (AlCIPc), Tin Ethyl Etiopurpurin (SnET 2), and meta-tetra(hydroxyphenyl)chlorin (m THPC)) was evaluated following a previous analysis which investigated the necrotic tissue response to PDT at 24 h post treatment. Free DNA ends, produced by internucleosomal DNA cleavage in apoptotic cells, were stained using a TUNEL (terminal deoxynucleotidyl transferase (TdT)-mediated dUTP nick-end labelling) assay. Confocal laser scanning microscopy (CLSM) was used to quantify the local incidence of apoptosis and determine its spatial distribution throughout the brain. The incidence of apoptosis was confirmed by histopathology, which demonstrated cell shrinkage, pyknosis and karyorrhexis. At 24 h post PDT, AlClPc did not cause any detectable apoptosis, while the other photosensitizers produced varying numbers of apoptotic cells near the region of coagulative necrosis. The apoptotic response did not appear to be related to photosensitizer dose. These results suggest that at this time point, a minimal and fairly localized apoptotic effect is produced in brain tissues, the extent of which depends largely on the particular photosensitizer. © 2000 Cancer Research Campaign PMID:10993661

  2. Apoptosis induced in vivo by photodynamic therapy in normal brain and intracranial tumour tissue.

    PubMed

    Lilge, L; Portnoy, M; Wilson, B C

    2000-10-01

    The apoptotic response of normal brain and intracranial VX2 tumour following photodynamic therapy (PDT) mediated by 5 different photosensitizers (Photofrin, 5-aminolaevulinic acid (ALA)-induced protoporphyrin IX (PpIX), chloroaluminium phthalocyanine (AlCIPc), Tin Ethyl Etiopurpurin (SnET(2)), and meta -tetra(hydroxyphenyl)chlorin (m THPC)) was evaluated following a previous analysis which investigated the necrotic tissue response to PDT at 24 h post treatment. Free DNA ends, produced by internucleosomal DNA cleavage in apoptotic cells, were stained using a TUNEL (terminal deoxynucleotidyl transferase (TdT)-mediated dUTP nick-end labelling) assay. Confocal laser scanning microscopy (CLSM) was used to quantify the local incidence of apoptosis and determine its spatial distribution throughout the brain. The incidence of apoptosis was confirmed by histopathology, which demonstrated cell shrinkage, pyknosis and karyorrhexis. At 24 h post PDT, AlClPc did not cause any detectable apoptosis, while the other photosensitizers produced varying numbers of apoptotic cells near the region of coagulative necrosis. The apoptotic response did not appear to be related to photosensitizer dose. These results suggest that at this time point, a minimal and fairly localized apoptotic effect is produced in brain tissues, the extent of which depends largely on the particular photosensitizer. PMID:10993661

  3. Leukaemia, brain tumours and exposure to extremely low frequency magnetic fields: cohort study of Swiss railway employees

    PubMed Central

    Röösli, Martin; Lörtscher, Manfred; Egger, Matthias; Pfluger, Dominik; Schreier, Nadja; Lörtscher, Emanuel; Locher, Peter; Spoerri, Adrian; Minder, Christoph

    2007-01-01

    Aims To investigate the relationship between extremely low frequency magnetic field (ELF‐MF) exposure and mortality from leukaemia and brain tumour in a cohort of Swiss railway workers. Methods 20 141 Swiss railway employees with 464 129 person‐years of follow‐up between 1972 and 2002 were studied. Mortality rates for leukaemia and brain tumour of highly exposed train drivers (21 μT average annual exposure) were compared with medium and low exposed occupational groups (i.e. station masters with an average exposure of 1 μT). In addition, individual cumulative exposure was calculated from on‐site measurements and modelling of past exposures. Results The hazard ratio (HR) for leukaemia mortality of train drivers was 1.43 (95% CI 0.74 to 2.77) compared with station masters. For myeloid leukaemia the HR of train drivers was 4.74 (95% CI 1.04 to 21.60) and for Hodgkin's disease 3.29 (95% CI 0.69 to 15.63). Lymphoid leukaemia, non‐Hodgkin's disease and brain tumour mortality were not associated with magnetic field exposure. Concordant results were obtained from analyses based on individual cumulative exposure. Conclusions Some evidence of an exposure–response association was found for myeloid leukaemia and Hodgkin's disease, but not for other haematopoietic and lymphatic malignancies and brain tumours. PMID:17525094

  4. A structural and functional magnetic resonance imaging dataset of brain tumour patients

    PubMed Central

    Pernet, Cyril R.; Gorgolewski, Krzysztof J.; Job, Dominic; Rodriguez, David; Whittle, Ian; Wardlaw, Joanna

    2016-01-01

    We collected high resolution structural (T1, T2, DWI) and several functional (BOLD T2*) MRI data in 22 patients with different types of brain tumours. Functional imaging protocols included a motor task, a verb generation task, a word repetition task and resting state. Imaging data are complemented by demographics (age, sex, handedness, and pathology), behavioural results to motor and cognitive tests and direct cortical electrical stimulation data (pictures of stimulation sites with outcomes) performed during surgery. Altogether, these data are suited to test functional imaging methods for single subject analyses, in particular methods that focus on locating eloquent cortical areas, critical functional and/or structural network hubs, and predict patient status based on imaging data (presurgical mapping). PMID:26836205

  5. Radioisotope scanning of brain, liver, lung and bone with a note on tumour localizing agents

    PubMed Central

    Lavender, J. P.

    1973-01-01

    Radioisotopic scanning of brain, liver, lungs and the skeleton is briefly reviewed with a survey of recent developments of clinical significance. In brain scanning neoplasm detection rates of greater than 90% are claimed. The true figure is probably 70-80%. Autopsy data shows a number of false negatives, particularly with vascular lesions. Attempts to make scanning more specific in differentiating neoplasm from vascular lesions by rapid sequence blood flow studies are reviewed. In liver scanning by means of colloids again high success rate is claimed but small metastases are frequently missed and the false negative scan rate is probably quite high. Lung scanning still has its main place in investigating pulmonary embolic disease. Ventilation studies using Xenon 133 are useful, particularly combined with perfusion studies. The various radiopharmaceuticals for use in bone scanning are reviewed. The appearance of technetium labelled phosphate compounds will probably allow much wider use of total skeletal scanning. Research into tumour localizing agents continues, the most recent and interesting being Gallium citrate and labelled bleomycin. Neither agent is predictable however although Gallium may have a place in Hodgkins disease and bronchogenic neoplasm and both may have a place in the detection of cerebral tumours. ImagesFig. 1Fig. 2Fig. 3p452-bFig. 3bFig. 4Fig. 5Fig. 5bFig. 6Fig. 7Fig. 8Fig. 9Fig. 10Fig. 11Fig. 12Fig. 12c & 12dFig. 13Fig. 13 b,c,dFig. 14Fig. 14bFig. 15Fig. 15bFig. 16Fig. 17Fig. 18 PMID:4602127

  6. Multi-fractal detrended texture feature for brain tumor classification

    NASA Astrophysics Data System (ADS)

    Reza, Syed M. S.; Mays, Randall; Iftekharuddin, Khan M.

    2015-03-01

    We propose a novel non-invasive brain tumor type classification using Multi-fractal Detrended Fluctuation Analysis (MFDFA) [1] in structural magnetic resonance (MR) images. This preliminary work investigates the efficacy of the MFDFA features along with our novel texture feature known as multifractional Brownian motion (mBm) [2] in classifying (grading) brain tumors as High Grade (HG) and Low Grade (LG). Based on prior performance, Random Forest (RF) [3] is employed for tumor grading using two different datasets such as BRATS-2013 [4] and BRATS-2014 [5]. Quantitative scores such as precision, recall, accuracy are obtained using the confusion matrix. On an average 90% precision and 85% recall from the inter-dataset cross-validation confirm the efficacy of the proposed method.

  7. Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation

    PubMed Central

    Beare, Richard J.; Chen, Jian; Kelly, Claire E.; Alexopoulos, Dimitrios; Smyser, Christopher D.; Rogers, Cynthia E.; Loh, Wai Y.; Matthews, Lillian G.; Cheong, Jeanie L. Y.; Spittle, Alicia J.; Anderson, Peter J.; Doyle, Lex W.; Inder, Terrie E.; Seal, Marc L.; Thompson, Deanne K.

    2016-01-01

    Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks' gestation) acquired at 30 weeks' corrected gestational age (n = 5), coronal T2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5) and axial T2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T2-weighted images of preterm infants (born <30 weeks' gestation) acquired shortly after birth (n = 12), preterm infants acquired at term-equivalent age (n = 12), and healthy term-born infants (born ≥38 weeks' gestation) acquired within the first 9 days of life (n = 12). For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for the cortical gray

  8. Directed progression brain networks in Alzheimer's disease: properties and classification.

    PubMed

    Friedman, Eric J; Young, Karl; Asif, Danial; Jutla, Inderjit; Liang, Michael; Wilson, Scott; Landsberg, Adam S; Schuff, Norbert

    2014-06-01

    This article introduces a new approach in brain connectomics aimed at characterizing the temporal spread in the brain of pathologies like Alzheimer's disease (AD). The main instrument is the development of "directed progression networks" (DPNets), wherein one constructs directed edges between nodes based on (weakly) inferred directions of the temporal spreading of the pathology. This stands in contrast to many previously studied brain networks where edges represent correlations, physical connections, or functional progressions. In addition, this is one of a few studies showing the value of using directed networks in the study of AD. This article focuses on the construction of DPNets for AD using longitudinal cortical thickness measurements from magnetic resonance imaging data. The network properties are then characterized, providing new insights into AD progression, as well as novel markers for differentiating normal cognition (NC) and AD at the group level. It also demonstrates the important role of nodal variations for network classification (i.e., the significance of standard deviations, not just mean values of nodal properties). Finally, the DPNets are utilized to classify subjects based on their global network measures using a variety of data-mining methodologies. In contrast to most brain networks, these DPNets do not show high clustering and small-world properties. PMID:24901258

  9. Challenges in providing culturally-competent care to patients with metastatic brain tumours and their families.

    PubMed

    Longo, Lianne; Slater, Serena

    2014-01-01

    Being diagnosed with a metastatic brain tumour can be devastating as it is characterized by very low cure rates, as well as significant morbidity and mortality. Given the poor life expectancy and progressive disability that ensues, patients and family members experience much turmoil, which includes losses that bring about changes to family roles, routines and relationships. Crisis and conflict are common during such major disruptions to a family system, as individual members attempt to make sense of the illness experience based on cultural and spiritual beliefs, past experiences and personal philosophies. It is imperative health care providers strive towards increased awareness and knowledge of how culture affects the overall experience of illness and death in order to help create a mutually satisfactory care plan. Providing culturally-competent care entails the use of proper communication skills to facilitate the exploration of patient and family perspectives and allows for mutual decision making. A case study will illustrate the challenges encountered in providing culturally-competent care to a woman with brain cancer and her family. As the patient's health declined, the family entered into a state of crisis where communication between family members and health care professionals was strained; leading to conflict and sub-optimal outcomes. This paper will address the ethical dilemma of providing culturally-competent care when a patient's safety is at risk, and the nursing implications of upholding best practices in the context of differing beliefs and priorities. PMID:25265763

  10. Increasing rates of brain tumours in the Swedish national inpatient register and the causes of death register.

    PubMed

    Hardell, Lennart; Carlberg, Michael

    2015-04-01

    Radiofrequency emissions in the frequency range 30 kHz-300 GHz were evaluated to be Group 2B, i.e., "possibly", carcinogenic to humans by the International Agency for Research on Cancer (IARC) at WHO in May 2011. The Swedish Cancer Register has not shown increasing incidence of brain tumours in recent years and has been used to dismiss epidemiological evidence on a risk. In this study we used the Swedish National Inpatient Register (IPR) and Causes of Death Register (CDR) to further study the incidence comparing with the Cancer Register data for the time period 1998-2013 using joinpoint regression analysis. In the IPR we found a joinpoint in 2007 with Annual Percentage Change (APC) +4.25%, 95% CI +1.98, +6.57% during 2007-2013 for tumours of unknown type in the brain or CNS. In the CDR joinpoint regression found one joinpoint in 2008 with APC during 2008-2013 +22.60%, 95% CI +9.68, +37.03%. These tumour diagnoses would be based on clinical examination, mainly CT and/or MRI, but without histopathology or cytology. No statistically significant increasing incidence was found in the Swedish Cancer Register during these years. We postulate that a large part of brain tumours of unknown type are never reported to the Cancer Register. Furthermore, the frequency of diagnosis based on autopsy has declined substantially due to a general decline of autopsies in Sweden adding further to missing cases. We conclude that the Swedish Cancer Register is not reliable to be used to dismiss results in epidemiological studies on the use of wireless phones and brain tumour risk. PMID:25854296

  11. Classification and Epidemiology of Mammary Tumours in Pet Rabbits (Oryctolagus cuniculus).

    PubMed

    Baum, B; Hewicker-Trautwein, M

    2015-05-01

    Mammary tumours are common in pet rabbits; however, published studies are predominantly derived from laboratory and meat rabbits. This study reports basic data on type and location of 119 separate tumours from 109 pet rabbits. The animals were aged 2-14 years (mean 5.5 years) and all 90 rabbits of known gender were female. Cranial and caudal mammary glands were affected equally. The majority of lesions (n = 105) were classified as carcinomas with 32 tubular, 16 papillary, 12 tubulopapillary, 11 solid, nine adenosquamous, nine comedo type, five complex, four ductal, three cribriform, three anaplastic and one spindle -cell carcinoma. Twelve percent of the lesions were benign, with eight intraductal papillary adenomas, three simple tubular adenomas and one complex adenoma. One non-neoplastic lesion was found in the form of cystic duct ectasia. PMID:25840882

  12. Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification

    PubMed Central

    Nawa, Norberto E.; Ando, Hiroshi

    2015-01-01

    Multivariate pattern analysis (MVPA) has been successfully employed to advance our understanding of where and how information regarding different mental states is represented in the human brain, bringing new insights into how these states come to fruition, and providing a promising complement to the mass-univariate approach. Here, we employed MVPA to classify whole-brain activity patterns occurring in single fMRI scans, in order to retrieve binary answers from experiment participants. Five healthy volunteers performed two types of mental task while in the MRI scanner: counting down numbers and recalling positive autobiographical events. Data from these runs were used to train individual machine learning based classifiers that predicted which mental task was being performed based on the voxel-based brain activity patterns. On a different day, the same volunteers reentered the scanner and listened to six statements (e.g., “the month you were born is an odd number”), and were told to countdown numbers if the statement was true (yes) or recall positive events otherwise (no). The previously trained classifiers were then used to assign labels (yes/no) to the scans collected during the 24-second response periods following each one of the statements. Mean classification accuracies at the single scan level were in the range of 73.6 to 80.8%, significantly above chance for all participants. When applying a majority vote on the scans within each response period, i.e., the most frequent label (yes/no) in the response period becomes the answer to the previous statement, 5.0 to 5.8 sentences, out of 6, were correctly classified in each one of the runs, on average. These results indicate that binary answers can be retrieved from whole-brain activity patterns, suggesting that MVPA provides an alternative way to establish basic communication with unresponsive patients when other techniques are not successful. PMID:26778992

  13. Risk of brain tumours in relation to estimated RF dose from mobile phones: results from five Interphone countries

    PubMed Central

    Armstrong, B K; Bowman, J D; Giles, G G; Hours, M; Krewski, D; McBride, M; Parent, M E; Sadetzki, S; Woodward, A; Brown, J; Chetrit, A; Figuerola, J; Hoffmann, C; Jarus-Hakak, A; Montestruq, L; Nadon, L; Richardson, L; Villegas, R; Vrijheid, M

    2011-01-01

    Objectives The objective of this study was to examine the associations of brain tumours with radio frequency (RF) fields from mobile phones. Methods Patients with brain tumour from the Australian, Canadian, French, Israeli and New Zealand components of the Interphone Study, whose tumours were localised by neuroradiologists, were analysed. Controls were matched on age, sex and region and allocated the ‘tumour location’ of their matched case. Analyses included 553 glioma and 676 meningioma cases and 1762 and 1911 controls, respectively. RF dose was estimated as total cumulative specific energy (TCSE; J/kg) absorbed at the tumour's estimated centre taking into account multiple RF exposure determinants. Results ORs with ever having been a regular mobile phone user were 0.93 (95% CI 0.73 to 1.18) for glioma and 0.80 (95% CI 0.66 to 0.96) for meningioma. ORs for glioma were below 1 in the first four quintiles of TCSE but above 1 in the highest quintile, 1.35 (95% CI 0.96 to 1.90). The OR increased with increasing TCSE 7+ years before diagnosis (p-trend 0.01; OR 1.91, 95% CI 1.05 to 3.47 in the highest quintile). A complementary analysis in which 44 glioma and 135 meningioma cases in the most exposed area of the brain were compared with gliomas and meningiomas located elsewhere in the brain showed increased ORs for tumours in the most exposed part of the brain in those with 10+ years of mobile phone use (OR 2.80, 95% CI 1.13 to 6.94 for glioma). Patterns for meningioma were similar, but ORs were lower, many below 1.0. Conclusions There were suggestions of an increased risk of glioma in long-term mobile phone users with high RF exposure and of similar, but apparently much smaller, increases in meningioma risk. The uncertainty of these results requires that they be replicated before a causal interpretation can be made. PMID:21659469

  14. Brain tissue segmentation in 4D CT using voxel classification

    NASA Astrophysics Data System (ADS)

    van den Boom, R.; Oei, M. T. H.; Lafebre, S.; Oostveen, L. J.; Meijer, F. J. A.; Steens, S. C. A.; Prokop, M.; van Ginneken, B.; Manniesing, R.

    2012-02-01

    A method is proposed to segment anatomical regions of the brain from 4D computer tomography (CT) patient data. The method consists of a three step voxel classification scheme, each step focusing on structures that are increasingly difficult to segment. The first step classifies air and bone, the second step classifies vessels and the third step classifies white matter, gray matter and cerebrospinal fluid. As features the time averaged intensity value and the temporal intensity change value were used. In each step, a k-Nearest-Neighbor classifier was used to classify the voxels. Training data was obtained by placing regions of interest in reconstructed 3D image data. The method has been applied to ten 4D CT cerebral patient data. A leave-one-out experiment showed consistent and accurate segmentation results.

  15. Brain connectivity and novel network measures for Alzheimer's disease classification.

    PubMed

    Prasad, Gautam; Joshi, Shantanu H; Nir, Talia M; Toga, Arthur W; Thompson, Paul M

    2015-01-01

    We compare a variety of different anatomic connectivity measures, including several novel ones, that may help in distinguishing Alzheimer's disease (AD) patients from controls. We studied diffusion-weighted magnetic resonance imaging from 200 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We first evaluated measures derived from connectivity matrices based on whole-brain tractography; next, we studied additional network measures based on a novel flow-based measure of brain connectivity, computed on a dense 3-dimensional lattice. Based on these 2 kinds of connectivity matrices, we computed a variety of network measures. We evaluated the measures' ability to discriminate disease with a repeated, stratified 10-fold cross-validated classifier, using support vector machines, a supervised learning algorithm. We tested the relative importance of different combinations of features based on the accuracy, sensitivity, specificity, and feature ranking of the classification of 200 people into normal healthy controls and people with early or late mild cognitive impairment or AD. PMID:25264345

  16. Double-labelling immunohistochemistry for MGMT and a “cocktail” of non-tumourous elements is a reliable, quick and easy technique for inferring methylation status in glioblastomas and other primary brain tumours

    PubMed Central

    2013-01-01

    Background Our aim was to develop a new protocol for MGMT immunohistochemistry with good agreement between observers and good correlation with molecular genetic tests of tumour methylation. We examined 40 primary brain tumours (30 glioblastomas and 10 oligodendroglial tumours) with our new technique, namely double-labelling immunohistochemistry for MGMT and a "cocktail" of non-tumour antigens (CD34, CD45 and CD68). We compared the results with single-labelling immunohistochemistry for MGMT and methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA, a recognised molecular genetic technique which we applied as the gold-standard for the methylation status). Results Double-labelling immunohistochemistry for MGMT produced a visual separation of tumourous and non-tumourous elements on the same histological slide, making it quick and easy to determine whether tumour cell nuclei were MGMT-positive or MGMT-negative (and thereby infer the methylation status of the tumour). We found good agreement between observers (kappa 0.76) and within observer (kappa 0.84). Furthermore, double-labelling showed good specificity (80%), sensitivity (73.33%), positive predictive value (PPV, 83.33%) and negative predictive value (NPV, 68.75%) compared to MS-MLPA. Double-labelling was quicker and easier to assess than single-labelling and it outperformed quantitative computerised image analysis of MGMT single-labelling in terms of sensitivity, specificity, PPV and NPV. Conclusions Double-labelling immunohistochemistry for MGMT and a cocktail of non-tumourous elements provides a "one look" method for determining whether tumour cell nuclei are MGMT-positive or MGMT-negative. This can be used to infer the methylation status of the tumour. There is good observer agreement and good specificity, sensitivity, PPV and NPV compared to a molecular gold-standard. PMID:24252243

  17. The effect of combining two echo times in automatic brain tumor classification by MRS.

    PubMed

    García-Gómez, Juan M; Tortajada, Salvador; Vidal, César; Julià-Sapé, Margarida; Luts, Jan; Moreno-Torres, Angel; Van Huffel, Sabine; Arús, Carles; Robles, Montserrat

    2008-11-01

    (1)H MRS is becoming an accurate, non-invasive technique for initial examination of brain masses. We investigated if the combination of single-voxel (1)H MRS at 1.5 T at two different (TEs), short TE (PRESS or STEAM, 20-32 ms) and long TE (PRESS, 135-136 ms), improves the classification of brain tumors over using only one echo TE. A clinically validated dataset of 50 low-grade meningiomas, 105 aggressive tumors (glioblastoma and metastasis), and 30 low-grade glial tumors (astrocytomas grade II, oligodendrogliomas and oligoastrocytomas) was used to fit predictive models based on the combination of features from short-TEs and long-TE spectra. A new approach that combines the two consecutively was used to produce a single data vector from which relevant features of the two TE spectra could be extracted by means of three algorithms: stepwise, reliefF, and principal components analysis. Least squares support vector machines and linear discriminant analysis were applied to fit the pairwise and multiclass classifiers, respectively. Significant differences in performance were found when short-TE, long-TE or both spectra combined were used as input. In our dataset, to discriminate meningiomas, the combination of the two TE acquisitions produced optimal performance. To discriminate aggressive tumors from low-grade glial tumours, the use of short-TE acquisition alone was preferable. The classifier development strategy used here lends itself to automated learning and test performance processes, which may be of use for future web-based multicentric classifier development studies. PMID:18759382

  18. In vivo nuclear magnetic resonance spectroscopy of a transplanted brain tumour.

    PubMed Central

    Koeze, T. H.; Lantos, P. L.; Iles, R. A.; Gordon, R. E.

    1984-01-01

    In vivo nuclear magnetic resonance 31P spectroscopy was used to demonstrate different patterns of high energy phosphate metabolism in a group of malignant tumours of glial origin. In some of the more malignant tumours a decrease in adenylate energy charge was found. This was associated with a decline in phosphocreatine and an increase in sugar phosphate and inorganic phosphorus. PMID:6704312

  19. Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study

    PubMed Central

    Pearce, Mark S; Salotti, Jane A; Little, Mark P; McHugh, Kieran; Lee, Choonsik; Kim, Kwang Pyo; Howe, Nicola L; Ronckers, Cecile M; Rajaraman, Preetha; Craft, Alan W; Parker, Louise; de González, Amy Berrington

    2012-01-01

    Summary Background Although CT scans are very useful clinically, potential cancer risks exist from associated ionising radiation, in particular for children who are more radiosensitive than adults. We aimed to assess the excess risk of leukaemia and brain tumours after CT scans in a cohort of children and young adults. Methods In our retrospective cohort study, we included patients without previous cancer diagnoses who were first examined with CT in National Health Service (NHS) centres in England, Wales, or Scotland (Great Britain) between 1985 and 2002, when they were younger than 22 years of age. We obtained data for cancer incidence, mortality, and loss to follow-up from the NHS Central Registry from Jan 1, 1985, to Dec 31, 2008. We estimated absorbed brain and red bone marrow doses per CT scan in mGy and assessed excess incidence of leukaemia and brain tumours cancer with Poisson relative risk models. To avoid inclusion of CT scans related to cancer diagnosis, follow-up for leukaemia began 2 years after the first CT and for brain tumours 5 years after the first CT. Findings During follow-up, 74 of 178 604 patients were diagnosed with leukaemia and 135 of 176 587 patients were diagnosed with brain tumours. We noted a positive association between radiation dose from CT scans and leukaemia (excess relative risk [ERR] per mGy 0·036, 95% CI 0·005–0·120; p=0·0097) and brain tumours (0·023, 0·010–0·049; p<0·0001). Compared with patients who received a dose of less than 5 mGy, the relative risk of leukaemia for patients who received a cumulative dose of at least 30 mGy (mean dose 51·13 mGy) was 3·18 (95% CI 1·46–6·94) and the relative risk of brain cancer for patients who received a cumulative dose of 50–74 mGy (mean dose 60·42 mGy) was 2·82 (1·33–6·03). Interpretation Use of CT scans in children to deliver cumulative doses of about 50 mGy might almost triple the risk of leukaemia and doses of about 60 mGy might triple the risk of brain

  20. Multiple instance learning for classification of dementia in brain MRI.

    PubMed

    Tong, Tong; Wolz, Robin; Gao, Qinquan; Guerrero, Ricardo; Hajnal, Joseph V; Rueckert, Daniel

    2014-07-01

    Machine learning techniques have been widely used to detect morphological abnormalities from structural brain magnetic resonance imaging data and to support the diagnosis of neurological diseases such as dementia. In this paper, we propose to use a multiple instance learning (MIL) method in an application for the detection of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI). In our work, local intensity patches are extracted as features. However, not all the patches extracted from patients with dementia are equally affected by the disease and some of them may not be characteristic of morphology associated with the disease. Therefore, there is some ambiguity in assigning disease labels to these patches. The problem of the ambiguous training labels can be addressed by weakly supervised learning techniques such as MIL. A graph is built for each image to exploit the relationships among the patches and then to solve the MIL problem. The constructed graphs contain information about the appearances of patches and the relationships among them, which can reflect the inherent structures of images and aids the classification. Using the baseline MR images of 834 subjects from the ADNI study, the proposed method can achieve a classification accuracy of 89% between AD patients and healthy controls, and 70% between patients defined as stable MCI and progressive MCI in a leave-one-out cross validation. Compared with two state-of-the-art methods using the same dataset, the proposed method can achieve similar or improved results, providing an alternative framework for the detection and prediction of neurodegenerative diseases. PMID:24858570

  1. Essential problems in the interpretation of epidemiologic evidence for an association between mobile phone use and brain tumours

    NASA Astrophysics Data System (ADS)

    Kundi, Michael

    2010-11-01

    Due to the close proximity of a mobile phone to the head when placing a call, concerns have been raised that exposure from microwaves during mobile phone use may exert adverse health effects and, in particular, may increase the risk of brain tumours. In response to these concerns epidemiological studies have been conducted, most applying the case-control design. While epidemiology can provide decisive evidence for an association between an exposure and a disease fundamental problems arise if exposure is short compared to the natural history of the disease. For brain tumours latencies of decades have been implicated making special considerations about potential effects of exposures necessary that commence during an already growing tumour. It is shown that measures of disease risk like odds ratios and relative risks can under such circumstances not be interpreted as indicators of a long term effect on incidences in the exposed population. Besides this problem, the issues of a suitable exposure metric and the selection of endpoints are unresolved. It is shown that the solution of these problems affords knowledge about the mechanism of action by which exposure increases the risk of manifest disease.

  2. Potential of anti-cancer therapy based on anti-miR-155 oligonucleotides in glioma and brain tumours.

    PubMed

    Poltronieri, Palmiro; D'Urso, Pietro I; Mezzolla, Valeria; D'Urso, Oscar F

    2013-01-01

    MicroRNAs are aberrantly expressed in many cancers and can exert tumour-suppressive or oncogenic functions. As oncomirs promote growth of cancer cells and support survival during chemotherapy, thus microRNA-silencing therapies could be a valuable approach to be associated with anticancer drugs and chemotherapy treatments. miR-155 microRNA was found overexpressed in different types of cancer, such as leukaemias (PML, B-cell lymphomas), lung cancer and glioblastoma. GABA-A receptor downregulation was found correlated with glioma grading, with decreasing levels associated with higher grade of malignancies. A relationship between knock-down of miR-155 and re-expression of GABRA 1 protein in vivo was recently individuated. This finding has implication on the effectiveness of RNA-silencing approaches against miR-155 with the scope to control proliferation and signalling pathways regulated by GABA-A receptor. Applying microRNAs for treatment of brain tumours poses several problems, and fields to be solved are mainly the passage of the brain-blood barrier and the targeted delivery to specific cell types. Glioblastoma multiforme cells bud off microvesicles that deliver cytoplasmic contents to nearby cells. Thus, the exploitation of these mechanisms to deliver antagomir therapeutics targeting microvescicles in the brain could take the lead in the near future in the treatment for brain cancers in substitution of invasive surgical intervention. PMID:22834637

  3. Influence of size of regions of interest on the measurement of uptake of 123I-alpha-methyl tyrosine by brain tumours.

    PubMed

    Kuwert, T; Morgenroth, C; Woesler, B; Matheja, P; Palkovic, S; Vollet, B; Schäfers, M; Wassmann, H; Schober, O

    1996-07-01

    The aim of this study was to assess the influence of variations in the size of regions of interest (ROIs) on uptake values in brain tumours of L-3-iodine-123-alpha-methyl tyrosine (IMT). In addition, we attempted to establish the influence of size of ROIs on levels of significance assessing differences in mean IMT uptake between high-grade and low-grade tumours. Relative IMT uptake was determined in 19 patients with brain tumours using a MULTISPECT 3 triple-headed camera. Reconstructed image resolution was 14 mm at FWHM. Ten of the subjects suffered from high-grade gliomas (WHO grade III or IV) and nine from benign brain tumours, including eight patients with low-grade gliomas (WHO grade II). ROIs were defined by selecting those pixels within the tumour that exhibited uptake values above predefined threshold values. Using threshold values of 100, 95, 90, 85 and 80% of the mean, transaxial ROI size was approximately 0.1, 2.8, 4.3, 6.2 and 8.8 cm2, respectively. Over this range, mean, IMT uptake values decreased significantly from 2.4 to 1.9. High-grade tumours exhibited significantly higher IMT uptake than low-grade tumours at each of the threshold values. The corresponding levels of significance calculated using the Mann-Whitney U-test were between 0.01 and 0.02. Although IMT uptake values in brain tumours are significantly dependent on ROI size, levels of significance assessing differences in IMT uptake between high-grade and low-grade tumours are relatively insensitive to variations in this parameter. PMID:8843121

  4. Kinetic analysis of novel mono- and multivalent VHH-fragments and their application for molecular imaging of brain tumours

    PubMed Central

    Iqbal, U; Trojahn, U; Albaghdadi, H; Zhang, J; O'Connor-McCourt, M; Stanimirovic, D; Tomanek, B; Sutherland, G; Abulrob, A

    2010-01-01

    Background and purpose: The overexpression of epidermal growth factor receptor (EGFR) and its mutated variant EGFRvIII occurs in 50% of glioblastoma multiforme. We developed antibody fragments against EGFR/EGFRvIII for molecular imaging and/or therapeutic targeting applications. Experimental approach: An anti–EGFR/EGFRvIII llama single-domain antibody (EG2) and two higher valency format constructs, bivalent EG2-hFc and pentavalent V2C-EG2 sdAbs, were analysed in vitro for their binding affinities using surface plasmon resonance and cell binding studies, and in vivo using pharmacokinetic, biodistribution, optical imaging and fluorescent microscopy studies. Key results: Kinetic binding analyses by surface plasmon resonance revealed intrinsic affinities of 55 nM and 97 nM for the monovalent EG2 to immobilized extracellular domains of EGFR and EGFRvIII, respectively, and a 10- to 600-fold increases in apparent affinities for the multivalent binders, V2C-EG2 and EG2-hFc, respectively. In vivo pharmacokinetic and biodistribution studies in mice revealed plasma half-lives for EG2, V2C-EG2 and EG2-hFc of 41 min, 80 min and 12.5 h, respectively, as well as a significantly higher retention of EG2-hFc compared to the other two constructs in EGFR/EGFRvIII-expressing orthotopic brain tumours, resulting in the highest signal in the tumour region in optical imaging studies. Time domain volumetric optical imaging fusion with high-resolution micro-computed tomography of microvascular brain network confirmed EG2-hFc selective accumulation/retention in anatomically defined tumour regions. Conclusions: Single domain antibodies can be optimized for molecular imaging applications by methods that improve their apparent affinity and prolong plasma half-life and, at the same time, preserve their ability to penetrate tumour parenchyma. PMID:20590596

  5. Cerebral ganglioglio-neuroblastoma: an unusual brain tumour of the neuron series.

    PubMed Central

    Dastur, D K

    1982-01-01

    The pathology of an unusual intracranial neuroectodermal tumour of the neuron series in described and its possible histogenesis discussed. The tumour, in a child aged 5 years with an enlarged head since infancy, presented as a large solid intra-cerebral mass. Histological examination showed four types of cells; (i) the stroma, forming the bulk of the tumour, was astrocytomatous; (ii) lobules of ill defined cells bearing small circular nuclei, representing immature neuroblasts: (iii) the same of other lobules containing neurons in various stages of development; and (iv) dense clusters of cells with hyperchromatic nuclei attempting rosettes, representing an overtly malignant neuroblastoma. This tumour was designated "ganglioglio-neuroblastoma" and probably originated from a slow growing ganglioglioma. Images PMID:7069425

  6. The importance of consistency in the classification of malignant tumours, illustrated by oral cancer material.

    PubMed

    Voss, R

    1985-08-01

    To evaluate the best treatment for the cancer patient, comparisons are often made between groups who have received different therapy. Such studies may be carried out within one hospital, but results from several hospitals may also be compared. It is therefore of the utmost importance that the groups/materials are selected according to the same criteria and classified and analysed by the same system and methods respectively. To illustrate this point, 125 cases of oral squamous cell carcinoma were classified according to two different systems, i.e. TNM 1973 and TNM 1978, but otherwise the material was similarly analyzed. The survival curves for stage I, II, III and IV78 were quite different from the corresponding curves of the 1973 system. The universal use of a simple, consistent classification system is recommended, and the effort to develop and improve the TNM system should continue. PMID:3860593

  7. Penetration and intracellular uptake of poly(glycerol-adipate) nanoparticles into three-dimensional brain tumour cell culture models.

    PubMed

    Meng, Weina; Garnett, Martin C; Walker, David A; Parker, Terence L

    2016-03-01

    Nanoparticle (NP) drug delivery systems may potentially enhance the efficacy of therapeutic agents. It is difficult to characterize many important properties of NPs in vivo and therefore attempts have been made to use realistic in vitro multicellular spheroids instead. In this paper, we have evaluated poly(glycerol-adipate) (PGA) NPs as a potential drug carrier for local brain cancer therapy. Various three-dimensional (3-D) cell culture models have been used to investigate the delivery properties of PGA NPs. Tumour cells in 3-D culture showed a much higher level of endocytic uptake of NPs than a mixed normal neonatal brain cell population. Differences in endocytic uptake of NPs in 2-D and 3-D models strongly suggest that it is very important to use in vitro 3-D cell culture models for evaluating this parameter. Tumour penetration of NPs is another important parameter which could be studied in 3-D cell models. The penetration of PGA NPs through 3-D cell culture varied between models, which will therefore require further study to develop useful and realistic in vitro models. Further use of 3-D cell culture models will be of benefit in the future development of new drug delivery systems, particularly for brain cancers which are more difficult to study in vivo. PMID:26568330

  8. A multinational case-control study on childhood brain tumours, anthropogenic factors, birth characteristics and prenatal exposures: A validation of interview data.

    PubMed

    Vienneau, Danielle; Infanger, Denis; Feychting, Maria; Schüz, Joachim; Schmidt, Lisbeth Samsø; Poulsen, Aslak Harbo; Tettamanti, Giorgio; Klæboe, Lars; Kuehni, Claudia E; Tynes, Tore; Von der Weid, Nicolas; Lannering, Birgitta; Röösli, Martin

    2016-02-01

    Little is known about the aetiology of childhood brain tumours. We investigated anthropometric factors (birth weight, length, maternal age), birth characteristics (e.g. vacuum extraction, preterm delivery, birth order) and exposures during pregnancy (e.g. maternal: smoking, working, dietary supplement intake) in relation to risk of brain tumour diagnosis among 7-19 year olds. The multinational case-control study in Denmark, Sweden, Norway and Switzerland (CEFALO) included interviews with 352 (participation rate=83.2%) eligible cases and 646 (71.1%) population-based controls. Interview data were complemented with data from birth registries and validated by assessing agreement (Cohen's Kappa). We used conditional logistic regression models matched on age, sex and geographical region (adjusted for maternal age and parental education) to explore associations between birth factors and childhood brain tumour risk. Agreement between interview and birth registry data ranged from moderate (Kappa=0.54; worked during pregnancy) to almost perfect (Kappa=0.98; birth weight). Neither anthropogenic factors nor birth characteristics were associated with childhood brain tumour risk. Maternal vitamin intake during pregnancy was indicative of a protective effect (OR 0.75, 95%-CI: 0.56-1.01). No association was seen for maternal smoking during pregnancy or working during pregnancy. We found little evidence that the considered birth factors were related to brain tumour risk among children and adolescents. PMID:26625087

  9. Paediatric head CT scan and subsequent risk of malignancy and benign brain tumour: a nation-wide population-based cohort study

    PubMed Central

    Huang, W-Y; Muo, C-H; Lin, C-Y; Jen, Y-M; Yang, M-H; Lin, J-C; Sung, F-C; Kao, C-H

    2014-01-01

    Background: To evaluate the possible association between paediatric head computed tomography (CT) examination and increased subsequent risk of malignancy and benign brain tumour. Methods: In the exposed cohort, 24 418 participants under 18 years of age, who underwent head CT examination between 1998 and 2006, were identified from the Taiwan National Health Insurance Research Database (NHIRD). Patients were followed up until a diagnosis of malignant disease or benign brain tumour, withdrawal from the National Health Insurance (NHI) system, or at the end of 2008. Results: The overall risk was not significantly different in the two cohorts (incidence rate=36.72 per 100 000 person-years in the exposed cohort, 28.48 per 100 000 person-years in the unexposed cohort, hazard ratio (HR)=1.29, 95% confidence interval (CI)=0.90–1.85). The risk of benign brain tumour was significantly higher in the exposed cohort than in the unexposed cohort (HR=2.97, 95% CI=1.49–5.93). The frequency of CT examination showed strong correlation with the subsequent overall risk of malignancy and benign brain tumour. Conclusions: We found that paediatric head CT examination was associated with an increased incidence of benign brain tumour. A large-scale study with longer follow-up is necessary to confirm this result. PMID:24569470

  10. Pharmaco-thermodynamics of deuterium-induced oedema in living rat brain via 1H2O MRI: implications for boron neutron capture therapy of malignant brain tumours

    NASA Astrophysics Data System (ADS)

    Medina, Daniel C.; Li, Xin; Springer, Charles S., Jr.

    2005-05-01

    In addition to its common usage as a tracer in metabolic and physiological studies, deuterium possesses anti-tumoural activity and confers protection against γ-irradiation. A more recent interest in deuterium emanates from the search for alternatives capable of improving neutron penetrance whilst reducing healthy tissue radiation dose deposition in boron neutron capture therapy of malignant brain tumours. Despite this potential clinical application, deuterium induces brain oedema, which is detrimental to neutron capture therapy. In this study, five adult male rats were titrated with deuterated drinking water while brain oedema was monitored via water proton magnetic resonance imaging. This report concludes that deuterium, as well as deuterium-induced brain oedema, possesses a uniform brain bio-distribution. At a steady-state blood fluid deuteration value of 16%, when the deuterium isotope fraction in drinking water was 25%, a mean oedematous volume change of 9 ± 2% (p-value <0.001) was observed in the rat brain—this may account for neurological and behavioural abnormalities found in mammals drinking highly deuterated water. In addition to characterizing the pharmaco-thermodynamics of deuterium-induced oedema, this report also estimates the impact of oedema on thermal neutron enhancement and effective dose reduction factors using simple linear transport calculations. While body fluid deuteration enhances thermal neutron flux penetrance and reduces dose deposition, oedema has the opposite effect because it increases the volume of interest, e.g., the brain volume. Thermal neutron enhancement and effective dose reduction factors could be reduced by as much as ~10% in the presence of a 9% water volume increase (oedema). All three authors have contributed equally to this work.

  11. Mutation and deletion analysis of GFRα-1, encoding the co-receptor for the GDNF/RET complex, in human brain tumours

    PubMed Central

    Gimm, O; Gössling, A; Marsh, D J; Dahia, P L M; Mulligan, L M; Deimling, A von; Eng, C

    1999-01-01

    Glial cell line-derived neurotrophic factor (GDNF) plays a key role in the control of vertebrate neuron survival and differentiation in both the central and peripheral nervous systems. GDNF preferentially binds to GFRα-1 which then interacts with the receptor tyrosine kinase RET. We investigated a panel of 36 independent cases of mainly advanced sporadic brain tumours for the presence of mutations in GDNF and GFRα-1. No mutations were found in the coding region of GDNF. We identified six previously described GFRα-1 polymorphisms, two of which lead to an amino acid change. In 15 of 36 brain tumours, all polymorphic variants appeared to be homozygous. Of these 15 tumours, one also had a rare, apparently homozygous, sequence variant at codon 361. Because of the rarity of the combination of homozygous sequence variants, analysis for hemizygous deletion was pursued in the 15 samples and loss of heterozygosity was found in 11 tumours. Our data suggest that intragenic point mutations of GDNF or GFRα-1 are not a common aetiologic event in brain tumours. However, either deletion of GFRα-1 and/or nearby genes may contribute to the pathogenesis of these tumours. © 1999 Cancer Research Campaign PMID:10408842

  12. Assessing Occupational Exposure to Chemicals in an International Epidemiological Study of Brain Tumours

    PubMed Central

    van Tongeren, Martie

    2013-01-01

    The INTEROCC project is a multi-centre case–control study investigating the risk of developing brain cancer due to occupational chemical and electromagnetic field exposures. To estimate chemical exposures, the Finnish Job Exposure Matrix (FINJEM) was modified to improve its performance in the INTEROCC study and to address some of its limitations, resulting in the development of the INTEROCC JEM. An international team of occupational hygienists developed a crosswalk between the Finnish occupational codes used in FINJEM and the International Standard Classification of Occupations 1968 (ISCO68). For ISCO68 codes linked to multiple Finnish codes, weighted means of the exposure estimates were calculated. Similarly, multiple ISCO68 codes linked to a single Finnish code with evidence of heterogeneous exposure were refined. One of the key time periods in FINJEM (1960–1984) was split into two periods (1960–1974 and 1975–1984). Benzene exposure estimates in early periods were modified upwards. The internal consistency of hydrocarbon exposures and exposures to engine exhaust fumes was improved. Finally, exposure to polycyclic aromatic hydrocarbon and benzo(a)pyrene was modified to include the contribution from second-hand smoke. The crosswalk ensured that the FINJEM exposure estimates could be applied to the INTEROCC study subjects. The modifications generally resulted in an increased prevalence of exposure to chemical agents. This increased prevalence of exposure was not restricted to the lowest categories of cumulative exposure, but was seen across all levels for some agents. Although this work has produced a JEM with important improvements compared to FINJEM, further improvements are possible with the expansion of agents and additional external data. PMID:23467593

  13. Real-time classification of activated brain areas for fMRI-based human-brain-interfaces

    NASA Astrophysics Data System (ADS)

    Moench, Tobias; Hollmann, Maurice; Grzeschik, Ramona; Mueller, Charles; Luetzkendorf, Ralf; Baecke, Sebastian; Luchtmann, Michael; Wagegg, Daniela; Bernarding, Johannes

    2008-03-01

    Functional MR imaging (fMRI) enables to detect different activated brain areas according to the performed tasks. However, data are usually evaluated after the experiment, which prohibits intra-experiment optimization or more sophisticated applications such as biofeedback experiments. Using a human-brain-interface (HBI), subjects are able to communicate with external programs, e.g. to navigate through virtual scenes, or to experience and modify their own brain activation. These applications require the real-time analysis and classification of activated brain areas. Our paper presents first results of different strategies for real-time pattern analysis and classification realized within a flexible experiment control system that enables the volunteers to move through a 3D virtual scene in real-time using finger tapping tasks, and alternatively only thought-based tasks.

  14. Computerised cognitive training in acquired brain injury: A systematic review of outcomes using the International Classification of Functioning (ICF).

    PubMed

    Sigmundsdottir, Linda; Longley, Wendy A; Tate, Robyn L

    2016-10-01

    Computerised cognitive training (CCT) is an increasingly popular intervention for people experiencing cognitive symptoms. This systematic review evaluated the evidence for CCT in adults with acquired brain injury (ABI), focusing on how outcome measures used reflect efficacy across components of the International Classification of Functioning, Disability and Health. Database searches were conducted of studies investigating CCT to treat cognitive symptoms in adult ABI. Scientific quality was rated using the PEDro-P and RoBiNT Scales. Ninety-six studies met the criteria. Most studies examined outcomes using measures of mental functions (93/96, 97%); fewer studies included measures of activities/participation (41/96, 43%) or body structures (8/96, 8%). Only 14 studies (15%) provided Level 1 evidence (randomised controlled trials with a PEDro-P score ≥ 6/10), with these studies suggesting strong evidence for CCT improving processing speed in multiple sclerosis (MS) and moderate evidence for improving memory in MS and brain tumour populations. There is a large body of research examining the efficacy of CCT, but relatively few Level 1 studies and evidence is largely limited to body function outcomes. The routine use of outcome measures of activities/participation would provide more meaningful evidence for the efficacy of CCT. The use of body structure outcome measures (e.g., neuroimaging) is a newly emerging area, with potential to increase understanding of mechanisms of action for CCT. PMID:26965034

  15. In vitro inhibition of human malignant brain tumour cell line proliferation by anti-urokinase-type plasminogen activator monoclonal antibodies.

    PubMed Central

    Abaza, M. S.; Shaban, F. A.; Narayan, R. K.; Atassi, M. Z.

    1998-01-01

    A brain tumour-associated marker, urokinase (UK), was investigated using rabbit anti-UK polyclonal and murine anti-UK monoclonal antibodies, which were prepared by immunization with low molecular weight UK (LMW-UK) and high molecular weight urokinase (HMW-UK) synthetic peptide respectively. The polyclonal antibody cross-reacted with both LMW-UK and HMW-UK, whereas the murine MAbs were specific for HMW-UK. These immunological probes were used to study urokinase in glioma extracts, tissues, sera and cell lines that had been prepared from primary cultures of freshly dissected gliomas. Radioimmunoassays showed that glioma extracts had much higher level (5- to 44-fold) of UK than normal human brain extracts. This result was confirmed by immunoblotting of electrophoresis gels of glioma and human brain extracts. Immunohistochemical study using anti-UK MAb demonstrated much higher levels of UK in glioma tissue than normal brain tissue. Immunohistochemical study using anti-UK MAbs localized UK on the cell surface of glioma cells. Anti-UK MAbs inhibited the proliferation of AA cell lines and GB cell lines (50% to > 90%) and exerted minor effects (< or = 20%) on normal human liver, intestine and lymphocyte cell lines. Taken together, these results suggest that anti-UK MAbs may have therapeutic potential for human gliomas and cancer metastasis. Images Figure 2 Figure 3 PMID:9862567

  16. The Sum of Tumour-to-Brain Ratios Improves the Accuracy of Diagnosing Gliomas Using 18F-FET PET

    PubMed Central

    Zyromska, Agnieszka; Wisniewski, Tomasz; Harat, Aleksandra; Lopatto, Rita; Furtak, Jacek

    2015-01-01

    Gliomas are common brain tumours, but obtaining tissue for definitive diagnosis can be difficult. There is, therefore, interest in the use of non-invasive methods to diagnose and grade the disease. Although positron emission tomography (PET) with 18F-fluorethyltyrosine (18F-FET) can be used to differentiate between low-grade (LGG) and high-grade (HGG) gliomas, the optimal parameters to measure and their cut-points have yet to be established. We therefore assessed the value of single and dual time-point acquisition of 18F-FET PET parameters to differentiate between primary LGGs (n = 22) and HGGs (n = 24). PET examination was considered positive for glioma if the metabolic activity was 1.6-times higher than that of background (contralateral) brain, and maximum tissue-brain ratios (TBRmax) were calculated 10 and 60 min after isotope administration with their sums and differences calculated from individual time-point values. Using a threshold-based method, the overall sensitivity of PET was 97%. Several analysed parameters were significantly different between LGGs and HGGs. However, in a receiver operating characteristics analysis, TBR sum had the best diagnostic accuracy of 87% and sensitivity, specificity, and positive and negative predictive values of 100%, 72.7%, 80%, and 100%, respectively. 18F-FET PET is valuable for the non-invasive determination of glioma grade, especially when dual time-point metrics are used. TBR sum shows the greatest accuracy, sensitivity, and negative predictive value for tumour grade differentiation and is a simple method to implement. However, the cut-off may differ between institutions and calibration strategies would be useful. PMID:26468649

  17. Brain Decoding-Classification of Hand Written Digits from fMRI Data Employing Bayesian Networks.

    PubMed

    Yargholi, Elahe'; Hossein-Zadeh, Gholam-Ali

    2016-01-01

    We are frequently exposed to hand written digits 0-9 in today's modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain-computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of functional Magnetic Resonance Imaging (fMRI) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25-30. Results in different brain lobes (frontal, occipital, parietal, and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor, and language processing), but the most relevant one to the task was language processing network according to the voxel selection. PMID:27468261

  18. Comparison of visual and ROI-based brain tumour grading using 18F-FDG PET: ROC analyses.

    PubMed

    Meyer, P T; Schreckenberger, M; Spetzger, U; Meyer, G F; Sabri, O; Setani, K S; Zeggel, T; Buell, U

    2001-02-01

    Several studies have suggested that the use of simple visual interpretation criteria for the investigation of brain tumours by positron emission tomography with fluorine-18 fluorodeoxyglucose (FDG-PET) might be similarly or even more accurate than quantitative or semi-quantitative approaches. We investigated this hypothesis by comparing the accuracy of FDG-PET brain tumour grading using a proposed six-step visual grading scale (VGS; applied by three independent observers unaware of the clinical history and the results of histopathology) and three different region of interest (ROI) ratios (maximal tumour uptake compared with contralateral tissue [Tu/Tis], grey matter [Tu/GM] and white matter [Tu/WM]). The patient population comprised 47 patients suffering from 17 benign (7 gliomas of grade II, 10 non-gliomatous tumours) and 30 malignant (23 gliomas of grade III-IV, 7 non-gliomatous tumours) tumours. The VGS results were highly correlated with the different ROI ratios (R=0.91 for Tu/GM, R=0.82 for Tu/WM, and R=0.79 for Tu/Tis), and high inter-observer agreement was achieved (kappa=0.63, 0.76 and 0.81 for the three observers). The mean ROI ratios and VGS readings of gliomatous and non-gliomatous lesions were not significantly different. For all measures, high-grade lesions showed significantly higher FDG uptake than low-grade lesions (P<0.005 to P<0.0001, depending on the measure used). Nominal logistic regressions and receiver operating characteristic (ROC) analyses were used to calculate cut-off values to differentiate low- from high-grade lesions. The predicted (by ROC) diagnostic sensitivity/specificity of the different tests (cut-off ratios shown in parentheses) were: Tu/GM: 0.87/0.85 (0.7), Tu/WM: 0.93/0.80 (1.3). Tu/Tis: 0.80/0.80 (0.8) and VGS: 0.84/0.95 (uptake < GM, but > WM). The VGS yielded the highest Az (+/-SE) value (i.e. area under the ROC curve as a measure of predicted accuracy), 0.97+/-0.03, which showed a strong tendency towards being significantly

  19. Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition

    PubMed Central

    Zhan, Liang; Liu, Yashu; Wang, Yalin; Zhou, Jiayu; Jahanshad, Neda; Ye, Jieping; Thompson, Paul M.

    2015-01-01

    Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease. PMID:26257601

  20. Word pair classification during imagined speech using direct brain recordings.

    PubMed

    Martin, Stephanie; Brunner, Peter; Iturrate, Iñaki; Millán, José Del R; Schalk, Gerwin; Knight, Robert T; Pasley, Brian N

    2016-01-01

    People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70-150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58%; p < 0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (mean = 89% and 86%, respectively; p < 0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications. PMID:27165452

  1. Word pair classification during imagined speech using direct brain recordings

    PubMed Central

    Martin, Stephanie; Brunner, Peter; Iturrate, Iñaki; Millán, José del R.; Schalk, Gerwin; Knight, Robert T.; Pasley, Brian N.

    2016-01-01

    People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70–150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58%; p < 0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (mean = 89% and 86%, respectively; p < 0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications. PMID:27165452

  2. Brain tumours at 7T MRI compared to 3T—contrast effect after half and full standard contrast agent dose: initial results

    PubMed Central

    Noebauer-Huhmann, Iris-Melanie; Szomolanyi, P.; Kronnerwetter, C.; Widhalm, G.; Weber, M.; Nemec, S.; Juras, V.; Ladd, M. E.; Prayer, D.; Trattnig, S.

    2015-01-01

    Objectives To compare the contrast agent effect of a full dose and half the dose of gadobenate dimeglumine in brain tumours at 7 Tesla (7T) MR versus 3 Tesla (3T). Methods Ten patients with primary brain tumours or metastases were examined. Signal intensities were assessed in the lesion and normal brain. Tumour-to-brain contrast and lesion enhancement were calculated. Additionally, two independent readers subjectively graded the image quality and artefacts. Results The enhanced mean tumour-to-brain contrast and lesion enhancement were significantly higher at 7T than at 3T for both half the dose (91.8±45.8 vs. 43.9±25.3 [p=0.010], 128.1±53.7 vs. 75.5±32.4 [p=0.004]) and the full dose (129.2±50.9 vs. 66.6±33.1 [p=0.002], 165.4±54.2 vs. 102.6±45.4 [p=0.004]). Differences between dosages at each field strength were also significant. Lesion enhancement was higher with half the dose at 7T than with the full dose at 3T (p=.037), while the tumour-to-brain contrast was not significantly different. Subjectively, contrast enhancement, visibility, and lesion delineation were better at 7T and with the full dose. All parameters were rated as good, at the least. Conclusion Half the routine contrast agent dose at 7T provided higher lesion enhancement than the full dose at 3T which indicates the possibility of dose reduction at 7T. PMID:25194707

  3. Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

    PubMed Central

    Rajesh Sharma, R.; Marikkannu, P.

    2015-01-01

    A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic resonance imaging (MRI) modality outperforms towards diagnosing brain abnormalities like brain tumor, multiple sclerosis, hemorrhage, and many more. The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the MRI of brain under two classes of lesion, benign and malignant. The design approach was initially preprocessed using 3D Gaussian filter. Based on VOI (volume of interest) of the image, features were extracted using 3D volumetric Square Centroid Lines Gray Level Distribution Method (SCLGM) along with 3D run length and cooccurrence matrix. The optimal features are selected using the proposed refined gravitational search algorithm (RGSA). Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002). The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods. PMID:26509188

  4. Brain Decoding-Classification of Hand Written Digits from fMRI Data Employing Bayesian Networks

    PubMed Central

    Yargholi, Elahe'; Hossein-Zadeh, Gholam-Ali

    2016-01-01

    We are frequently exposed to hand written digits 0–9 in today's modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain–computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of functional Magnetic Resonance Imaging (fMRI) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25–30. Results in different brain lobes (frontal, occipital, parietal, and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor, and language processing), but the most relevant one to the task was language processing network according to the voxel selection. PMID:27468261

  5. Brain tumor classification and segmentation using sparse coding and dictionary learning.

    PubMed

    Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

    2016-08-01

    This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods. PMID:26351901

  6. Object categories specific brain activity classification with simultaneous EEG-fMRI.

    PubMed

    Ahmad, Rana Fayyaz; Malik, Aamir Saeed; Kamel, Nidal; Reza, Faruque

    2015-08-01

    Any kind of visual information is encoded in terms of patterns of neural activity occurring inside the brain. Decoding neural patterns or its classification is a challenging task. Functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) are non-invasive neuroimaging modalities to capture the brain activity pattern in term of images and electric potential respectively. To get higher spatiotemporal resolution of human brain from these two complementary neuroimaging modalities, simultaneous EEG-fMRI can be helpful. In this paper, we proposed a framework for classifying the brain activity patterns with simultaneous EEG-fMRI. We have acquired five human participants' data with simultaneous EEG-fMRI by showing different object categories. Further, combined analysis of EEG and fMRI data was carried out. Extracted information through combine analysis is passed to support vector machine (SVM) classifier for classification purpose. We have achieved better classification accuracy using simultaneous EEG-fMRI i.e., 81.8% as compared to fMRI data standalone. This shows that multimodal neuroimaging can improve the classification accuracy of brain activity patterns as compared to individual modalities reported in literature. PMID:26736635

  7. Extracting regional brain patterns for classification of neurodegenerative diseases

    NASA Astrophysics Data System (ADS)

    Pulido, Andrea; Rueda, Andrea; Romero, Eduardo

    2013-11-01

    In structural Magnetic Resonance Imaging (MRI), neurodegenerative diseases generally present complex brain patterns that can be correlated with di erent clinical onsets of this pathologies. An objective method that aims to determine both global and local changes is not usually available in clinical practice, thus the interpretation of these images is strongly dependent on the radiologist's skills. In this paper, we propose a strategy which interprets the brain structure using a framework that highlights discriminant brain patterns for neurodegenerative diseases. This is accomplished by combining a probabilistic learning technique, which identi es and groups regions with similar visual features, with a visual saliency method that exposes relevant information within each region. The association of such patterns with a speci c disease is herein evaluated in a classi cation task, using a dataset including 80 Alzheimer's disease (AD) patients and 76 healthy subjects (NC). Preliminary results show that the proposed method reaches a maximum classi cation accuracy of 81.39%.

  8. Multiple instance learning for classification of dementia in brain MRI.

    PubMed

    Tong, Tong; Wolz, Robin; Gao, Qinquan; Hajnal, Joseph V; Rueckert, Daniel

    2013-01-01

    Machine learning techniques have been widely used to support the diagnosis of neurological diseases such as dementia. Recent approaches utilize local intensity patterns within patches to derive voxelwise grading measures of disease. However, the relationships among these patches are usually ignored. In addition, there is some ambiguity in assigning disease labels to the extracted patches. Not all of the patches extracted from patients with dementia are characteristic of morphology associated with disease. In this paper, we propose to use a multiple instance learning method to address the problem of assigning training labels to the patches. In addition, a graph is built for each image to exploit the relationships among these patches, which aids the classification work. We illustrate the proposed approach in an application for the detection of Alzheimer's disease (AD): Using the baseline MR images of 834 subjects from the ADNI study, the proposed method can achieve a classification accuracy of 88.8% between AD patients and healthy controls, and 69.6% between patients with stable Mild Cognitive Impairment (MCI) and progressive MCI. These results compare favourably with state-of-the-art classification methods. PMID:24579190

  9. Biodegradable interstitial release polymer loading a novel small molecule targeting Axl receptor tyrosine kinase and reducing brain tumour migration and invasion

    PubMed Central

    Yen, S-Y; Chen, S-R; Hsieh, J; Li, Y-S; Chuang, S-E; Chuang, H-M; Huang, M-H; Lin, S-Z; Harn, H-J; Chiou, T-W

    2016-01-01

    Glioblastoma multiforme (GBM) is the most common and aggressive brain tumour. The neoplasms are difficult to resect entirely because of their highly infiltration property and leading to the tumour edge is unclear. Gliadel wafer has been used as an intracerebral drug delivery system to eliminate the residual tumour. However, because of its local low concentration and short diffusion distance, patient survival improves non-significantly. Axl is an essential regulator in cancer metastasis and patient survival. In this study, we developed a controlled-release polyanhydride polymer loading a novel small molecule, n-butylidenephthalide (BP), which is not only increasing local drug concentration and extending its diffusion distance but also reducing tumour invasion, mediated by reducing Axl expression. First, we determined that BP inhibited the expression of Axl in a dose- and time-dependent manner and reduced the migratory and invasive capabilities of GBM cells. In addition, BP downregulated matrix metalloproteinase activity, which is involved in cancer cell invasion. Furthermore, we demonstrated that BP regulated Axl via the extracellular signal-regulated kinases pathway. Epithelial-to-mesenchymal transition (EMT) is related to epithelial cells in the invasive migratory mesenchymal cells that underlie cancer progression; we demonstrated that BP reduced the expression of EMT-related genes. Furthermore, we used the overexpression of Axl in GBM cells to prove that Axl is a crucial target in the inhibition of GBM EMT, migration and invasion. In an in vivo study, we demonstrated that BP inhibited tumour growth and suppressed Axl expression in a dose-dependent manner according to a subcutaneous tumour model. Most importantly, in an intracranial tumour model with BP wafer in situ treatment, we demonstrated that the BP wafer not only significantly increased the survival rate but also decreased Axl expression, and inhibited tumour invasion. These results contribute to the

  10. From genotypes to phenotypes: classification of the tumour profiles for different variants of the cadherin adhesion pathway

    NASA Astrophysics Data System (ADS)

    Ramis-Conde, Ignacio; Drasdo, Dirk

    2012-06-01

    The E-cadherin adhesive profile expressed by a tumour is a characterization of the intracellular and intercellular protein interactions that control cell-cell adhesion. Within the intracellular proteins that determine the tumour adhesive profile, Src and PI3 are two essentials to initiate the formation of the E-cadherin adhesion complex. On the other hand, Src has also the capability of disrupting the β-catenin-E-cadherin complex and down-regulating cell-cell adhesion. In this paper, using a multi-scale mathematical model, we study the role of each of these proteins in the adhesive profile and invasive properties of the tumour. To do this, we create three versions of an intracellular model that explains the interplay between the proteins E-cadherin, β-catenin, Src and PI3; and we couple them to the strength of the cell-cell adhesion forces within an individual-cell-based model. The simulation results show how the tumour profile and its aggressive potential may change depending on the intrinsic characteristics of the protein pathways, and how these pathways may influence the early stages of cancer invasion. Our major findings may be summarized as follows. (1) Intermediate levels of Src synthesis rates generate the least invasive tumour phenotype. (2) Conclusions drawn from findings obtained from the intracellular molecular dynamics (here cadherin-catenin binding complexes) to the multi-cellular invasive potential of a tumour may be misleading or erroneous. The conclusions should be validated in a multi-cellular context on timescales relevant for population growth. (3) Monoclonal populations of more cohesive cells with otherwise equal properties tend to grow slower. (4) Less cohesive cells tend to outcompete more cohesive cells. (5) Less cohesive cells have a larger probability of invasion as migration forces can more easily outbalance cohesive forces.

  11. 77 FR 16925 - Medical Devices; Neurological Devices; Classification of the Near Infrared Brain Hematoma Detector

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-03-23

    ... HUMAN SERVICES Food and Drug Administration 21 CFR Part 882 Medical Devices; Neurological Devices; Classification of the Near Infrared Brain Hematoma Detector AGENCY: Food and Drug Administration, HHS. ACTION: Final rule. SUMMARY: The Food and Drug Administration (FDA) is classifying the Near Infrared (NIR)...

  12. Random Forest Classification of Depression Status Based On Subcortical Brain Morphometry Following Electroconvulsive Therapy

    PubMed Central

    Wade, Benjamin S.C.; Joshi, Shantanu H.; Pirnia, Tara; Leaver, Amber M.; Woods, Roger P.; Thompson, Paul M.; Espinoza, Randall; Narr, Katherine L.

    2015-01-01

    Disorders of the central nervous system are often accompanied by brain abnormalities detectable with MRI. Advances in biomedical imaging and pattern detection algorithms have led to classification methods that may help diagnose and track the progression of a brain disorder and/or predict successful response to treatment. These classification systems often use high-dimensional signals or images, and must handle the computational challenges of high dimensionality as well as complex data types such as shape descriptors. Here, we used shape information from subcortical structures to test a recently developed feature-selection method based on regularized random forests to 1) classify depressed subjects versus controls, and 2) patients before and after treatment with electroconvulsive therapy. We subsequently compared the classification performance of high-dimensional shape features with traditional volumetric measures. Shape-based models outperformed simple volumetric predictors in several cases, highlighting their utility as potential automated alternatives for establishing diagnosis and predicting treatment response. PMID:26413200

  13. Improvement effect on the depth-dose distribution by CSF drainage and air infusion of a tumour-removed cavity in boron neutron capture therapy for malignant brain tumours

    NASA Astrophysics Data System (ADS)

    Sakurai, Yoshinori; Ono, Koji; Miyatake, Shin-ichi; Maruhashi, Akira

    2006-03-01

    Boron neutron capture therapy (BNCT) without craniotomy for malignant brain tumours was started using an epi-thermal neutron beam at the Kyoto University Reactor in June 2002. We have tried some techniques to overcome the treatable-depth limit in BNCT. One of the effective techniques is void formation utilizing a tumour-removed cavity. The tumorous part is removed by craniotomy about 1 week before a BNCT treatment in our protocol. Just before the BNCT irradiation, the cerebro-spinal fluid (CSF) in the tumour-removed cavity is drained out, air is infused to the cavity and then the void is made. This void improves the neutron penetration, and the thermal neutron flux at depth increases. The phantom experiments and survey simulations modelling the CSF drainage and air infusion of the tumour-removed cavity were performed for the size and shape of the void. The advantage of the CSF drainage and air infusion is confirmed for the improvement in the depth-dose distribution. From the parametric surveys, it was confirmed that the cavity volume had good correlation with the improvement effect, and the larger effect was expected as the cavity volume was larger.

  14. OP04QUANTITATIVE MEASUREMENT OF BLOOD FLOW IN PAEDIATRIC BRAIN TUMOURS - A COMPARATIVE STUDY OF DYNAMIC SUSCEPTIBILITY CONTRAST AND MULTI-TIMEPOINT ARTERIAL SPIN LABEL IMAGING

    PubMed Central

    Abernethy, L.J.; Vidyasagar, R.; Pizer, B.L.; Mallucci, C.L.; Avula, S.; Parkes, L.M.

    2014-01-01

    INTRODUCTION: Arterial spin labeling (ASL) is a MR technique that allows for noninvasive quantification of cerebral blood flow (CBF). This technique, predominately used in research, has seen significant technical developments in the last few years that have led to more clinical applications. Currently, the main MR method used to provide perfusion measures in brain tumours is dynamic susceptibility contrast (DSC). DSC traces the signal changes caused by the transit of a bolus of gadolinium contrast agent. ASL has the advantage of not requiring bolus injection of contrast. We have performed a comparative study of DSC and multi-timepoint ASL in paediatric brain tumours (PBT). METHOD: Data from a total of 19 PBT patients (mean age: 9 ± 5 years; 10 females, 9 males) were included in the analyses for this study. Data used were from first presentation scans performed before any surgical intervention. Comparisons of the quantitative measures of CBF and blood arrival time between the two techniques were carried out to test the feasibility of ASL to provide useful quantification measures of CBF in PBT. RESULTS: DSC measurements of tumour blood flow showed a significant decrease in flow in comparison with normal brain, but this is not seen with ASL. There was a strong correlation between ASL and DSC measures of blood flow in normal brain (r = 0.65, p = 0.009), but not in tumour blood flow (r = 0.33, p = 0.2). CONCLUSION: This study demonstrates the feasibility and potential utility of ASL as a non-invasive technique for measuring blood flow in PBT. However, there is a discrepancy between ASL and DSC measures, that may be due to leakage of gadolinium contrast, reflecting the abnormal characteristics of tumour blood vessels in PBT.

  15. Real-time support vector classification and feedback of multiple emotional brain states.

    PubMed

    Sitaram, Ranganatha; Lee, Sangkyun; Ruiz, Sergio; Rana, Mohit; Veit, Ralf; Birbaumer, Niels

    2011-05-15

    An important question that confronts current research in affective neuroscience as well as in the treatment of emotional disorders is whether it is possible to determine the emotional state of a person based on the measurement of brain activity alone. Here, we first show that an online support vector machine (SVM) can be built to recognize two discrete emotional states, such as happiness and disgust from fMRI signals, in healthy individuals instructed to recall emotionally salient episodes from their lives. We report the first application of real-time head motion correction, spatial smoothing and feature selection based on a new method called Effect mapping. The classifier also showed robust prediction rates in decoding three discrete emotional states (happiness, disgust and sadness) in an extended group of participants. Subjective reports ascertained that participants performed emotion imagery and that the online classifier decoded emotions and not arbitrary states of the brain. Offline whole brain classification as well as region-of-interest classification in 24 brain areas previously implicated in emotion processing revealed that the frontal cortex was critically involved in emotion induction by imagery. We also demonstrate an fMRI-BCI based on real-time classification of BOLD signals from multiple brain regions, for each repetition time (TR) of scanning, providing visual feedback of emotional states to the participant for potential applications in the clinical treatment of dysfunctional affect. PMID:20692351

  16. L-Phenylalanine preloading reduces the (10)B(n, α)(7)Li dose to the normal brain by inhibiting the uptake of boronophenylalanine in boron neutron capture therapy for brain tumours.

    PubMed

    Watanabe, Tsubasa; Tanaka, Hiroki; Fukutani, Satoshi; Suzuki, Minoru; Hiraoka, Masahiro; Ono, Koji

    2016-01-01

    Boron neutron capture therapy (BNCT) is a cellular-level particle radiation therapy that combines the selective delivery of boron compounds to tumour tissue with neutron irradiation. Previously, high doses of one of the boron compounds used for BNCT, L-BPA, were found to reduce the boron-derived irradiation dose to the central nervous system. However, injection with a high dose of L-BPA is not feasible in clinical settings. We aimed to find an alternative method to improve the therapeutic efficacy of this therapy. We examined the effects of oral preloading with various analogues of L-BPA in a xenograft tumour model and found that high-dose L-phenylalanine reduced the accumulation of L-BPA in the normal brain relative to tumour tissue. As a result, the maximum irradiation dose in the normal brain was 19.2% lower in the L-phenylalanine group relative to the control group. This study provides a simple strategy to improve the therapeutic efficacy of conventional boron compounds for BNCT for brain tumours and the possibility to widen the indication of BNCT to various kinds of other tumours. PMID:26455769

  17. Efficient multilevel brain tumor segmentation with integrated bayesian model classification.

    PubMed

    Corso, J J; Sharon, E; Dube, S; El-Saden, S; Sinha, U; Yuille, A

    2008-05-01

    We present a new method for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main contribution of the paper is a Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm, and apply the technique to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes. The computationally efficient method runs orders of magnitude faster than current state-of-the-art techniques giving comparable or improved results. Our quantitative results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of glioblastoma multiforme brain tumor. PMID:18450536

  18. A Role for the Malignant Brain Tumour (MBT) Domain Protein LIN-61 in DNA Double-Strand Break Repair by Homologous Recombination

    PubMed Central

    Johnson, Nicholas M.; Lemmens, Bennie B. L. G.; Tijsterman, Marcel

    2013-01-01

    Malignant brain tumour (MBT) domain proteins are transcriptional repressors that function within Polycomb complexes. Some MBT genes are tumour suppressors, but how they prevent tumourigenesis is unknown. The Caenorhabditis elegans MBT protein LIN-61 is a member of the synMuvB chromatin-remodelling proteins that control vulval development. Here we report a new role for LIN-61: it protects the genome by promoting homologous recombination (HR) for the repair of DNA double-strand breaks (DSBs). lin-61 mutants manifest numerous problems associated with defective HR in germ and somatic cells but remain proficient in meiotic recombination. They are hypersensitive to ionizing radiation and interstrand crosslinks but not UV light. Using a novel reporter system that monitors repair of a defined DSB in C. elegans somatic cells, we show that LIN-61 contributes to HR. The involvement of this MBT protein in HR raises the possibility that MBT–deficient tumours may also have defective DSB repair. PMID:23505385

  19. Lipopolysaccharide induces expression of tumour necrosis factor alpha in rat brain: inhibition by methylprednisolone and by rolipram

    PubMed Central

    Buttini, M; Mir, A; Appel, K; Wiederhold, K H; Limonta, S; Gebicke-Haerter, P J; Boddeke, H W G M

    1997-01-01

    We have investigated the effects of the phosphodiesterase (PDE) type IV inhibitor rolipram and of the glucocorticoid methylprednisolone on the induction of tumour necrosis factor alpha (TNF-α) mRNA and protein in brains of rats after peripheral administration of lipopolysaccharide (LPS).After intravenous administration of LPS, a similar time-dependent induction of both TNF-α mRNA and protein was observed in rat brain. Peak mRNA and protein levels were found 7 h after administration of LPS.In situ hybridization experiments with a specific antisense TNF-α riboprobe suggested that the cells responsible for TNF-α production in the brain were microglia.Intraperitoneal administration of methylprednisolone inhibited the induction of TNF-α protein in a dose-dependent manner. A maximal inhibition of TNF-α protein production by 42.9±10.2% was observed at a dose regimen consisting of two injections of each 30 mg kg−1 methylprednisolone.Intraperitoneal administration of rolipram also inhibited the induction of TNF-α protein in a dose-dependent manner. The maximal inhibition of TNF-α protein production was 96.1±12.2% and was observed at a dose regimen of three separate injections of each 3 mg kg−1 rolipram.In situ hybridization experiments showed that the level of TNF-α mRNA induced in rat brain by LPS challenge was reduced by intraperitoneal administration of methylprednisolone (2×15 mg kg−1) and of rolipram (3×3 mg kg−1).We suggest that peripheral administration of LPS induces a time-dependent expression of TNF-α in rat brain, presumably in microglial cells, and that methylprednisolone and rolipram inhibit LPS-induced expression of TNF-α in these cells via a decrease of TNF-α mRNA stability and/or TNF-α gene transcription. PMID:9421299

  20. Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

    NASA Astrophysics Data System (ADS)

    Jamal, Wasifa; Das, Saptarshi; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

    2014-08-01

    Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

  1. Challenges relating to solid tumour brain metastases in clinical trials, part 1: patient population, response, and progression. A report from the RANO group.

    PubMed

    Lin, Nancy U; Lee, Eudocia Q; Aoyama, Hidefumi; Barani, Igor J; Baumert, Brigitta G; Brown, Paul D; Camidge, D Ross; Chang, Susan M; Dancey, Janet; Gaspar, Laurie E; Harris, Gordon J; Hodi, F Stephen; Kalkanis, Steven N; Lamborn, Kathleen R; Linskey, Mark E; Macdonald, David R; Margolin, Kim; Mehta, Minesh P; Schiff, David; Soffietti, Riccardo; Suh, John H; van den Bent, Martin J; Vogelbaum, Michael A; Wefel, Jeffrey S; Wen, Patrick Y

    2013-09-01

    Therapeutic outcomes for patients with brain metastases need to improve. A critical review of trials specifically addressing brain metastases shows key issues that could prevent acceptance of results by regulatory agencies, including enrolment of heterogeneous groups of patients and varying definitions of clinical endpoints. Considerations specific to disease, modality, and treatment are not consistently addressed. Additionally, the schedule of CNS imaging and consequences of detection of new or progressive brain metastases in trials mainly exploring the extra-CNS activity of systemic drugs are highly variable. The Response Assessment in Neuro-Oncology (RANO) working group is an independent, international, collaborative effort to improve the design of trials in patients with brain tumours. In this two-part series, we review the state of clinical trials of brain metastases and suggest a consensus recommendation for the development of criteria for future clinical trials. PMID:23993384

  2. Investigating machine learning techniques for MRI-based classification of brain neoplasms

    PubMed Central

    Kanas, Vasileios G.; Davatzikos, Christos

    2015-01-01

    Purpose Diagnosis and characterization of brain neoplasms appears of utmost importance for therapeutic management. The emerging of imaging techniques, such as Magnetic Resonance (MR) imaging, gives insight into pathology, while the combination of several sequences from conventional and advanced protocols (such as perfusion imaging) increases the diagnostic information. To optimally combine the multiple sources and summarize the information into a distinctive set of variables however remains difficult. The purpose of this study is to investigate machine learning algorithms that automatically identify the relevant attributes and are optimal for brain tumor differentiation. Methods Different machine learning techniques are studied for brain tumor classification based on attributes extracted from conventional and perfusion MRI. The attributes, calculated from neoplastic, necrotic, and edematous regions of interest, include shape and intensity characteristics. Attributes subset selection is performed aiming to remove redundant attributes using two filtering methods and a wrapper approach, in combination with three different search algorithms (Best First, Greedy Stepwise and Scatter). The classification frameworks are implemented using the WEKA software. Results The highest average classification accuracy assessed by leave-one-out (LOO) cross-validation on 101 brain neoplasms was achieved using the wrapper evaluator in combination with the Best First search algorithm and the KNN classifier and reached 96.9% when discriminating metastases from gliomas and 94.5% when discriminating high-grade from low-grade neoplasms. Conclusions A computer-assisted classification framework is developed and used for differential diagnosis of brain neoplasms based on MRI. The framework can achieve higher accuracy than most reported studies using MRI. PMID:21516321

  3. An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

    PubMed

    Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan

    2015-01-01

    A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the

  4. An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification

    PubMed Central

    Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan

    2015-01-01

    A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients’ benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the

  5. Integrative genomic analyses identify LIN28 and OLIG2 as markers of survival and metastatic potential in childhood central nervous system primitive neuro-ectodermal brain tumours

    PubMed Central

    Picard, Daniel; Miller, Suzanne; Hawkins, Cynthia E; Bouffet, Eric; Rogers, Hazel A; Chan, Tiffany SY; Kim, Seung-Ki; Ra, Young-Shin; Fangusaro, Jason; Korshunov, Andrey; Toledano, Helen; Nakamura, Hideo; Hayden, James T; Chan, Jennifer; Lafay-Cousin, Lucie; Hu, Ping X; Fan, Xing; Muraszko, Karin M; Pomeroy, Scott L; Lau, Ching C; Ng, Ho-Keung; Jones, Chris; Meter, Timothy Van; Clifford, Steven C; Eberhart, Charles; Gajjar, Amar; Pfister, Stefan M; Grundy, Richard G; Huang, Annie

    2013-01-01

    Background Childhood Central Nervous System Primitive Neuro-Ectodermal brain Tumours (CNS-PNETs) are highly aggressive brain tumours for which molecular features and best therapeutic strategy remains unknown. We interrogated a large cohort of these rare tumours in order to identify molecular markers that will enhance clinical management of CNS-PNET. Methods Transcriptional and copy number profiles from primary hemispheric CNS-PNETs were examined using clustering, gene and pathways enrichment analyses to discover tumour sub-groups and group-specific molecular markers. Immuno-histochemical and/or gene expression analyses were used to validate and examine the clinical significance of novel sub-group markers in 123 primary CNS-PNETs. Findings Three molecular sub-groups of CNS-PNETs distinguished by primitive neural (Group 1), oligo-neural (Group 2) and mesenchymal lineage (Group 3) gene expression signature were identified. Tumour sub-groups exhibited differential expression of cell lineage markers, LIN28 and OLIG2, and correlated with distinct demographics, survival and metastatic incidence. Group 1 tumours affected primarily younger females; male: female ratios were respectively 0.61 (median age 2.9 years; 95% CI: 2.4–5.2; p≤ 0.005), 1.25 (median age 7.9 years; 95% CI: 6–9.7) and 1.63 (median age 5.9 years; 95% CI: 4.9–7.8) for group 1, 2 and 3 patients. Overall outcome was poorest in group 1 patients which had a median survival of 0.8 years (95% CI: 0.47–1.2; p=0.019) as compared to 1.8 years (95% CI: 1.4–2.3) and 4.3 years; (95% CI: 0.82–7.8) respectively for group 2 and 3 patients. Group 3 tumours had the highest incidence of metastases at diagnosis; M0: M+ ratio were respectively 0.9 and 3.9 for group 3, versus group 1 and 2 tumours combined (p=0.037). Interpretation LIN28 and OLIG2 represent highly promising, novel diagnostic and prognostic molecular markers for CNS PNET that warrants further evaluation in prospective clinical trials. PMID:22691720

  6. Non-target adjacent stimuli classification improves performance of classical ERP-based brain computer interface

    NASA Astrophysics Data System (ADS)

    Ceballos, G. A.; Hernández, L. F.

    2015-04-01

    Objective. The classical ERP-based speller, or P300 Speller, is one of the most commonly used paradigms in the field of Brain Computer Interfaces (BCI). Several alterations to the visual stimuli presentation system have been developed to avoid unfavorable effects elicited by adjacent stimuli. However, there has been little, if any, regard to useful information contained in responses to adjacent stimuli about spatial location of target symbols. This paper aims to demonstrate that combining the classification of non-target adjacent stimuli with standard classification (target versus non-target) significantly improves classical ERP-based speller efficiency. Approach. Four SWLDA classifiers were trained and combined with the standard classifier: the lower row, upper row, right column and left column classifiers. This new feature extraction procedure and the classification method were carried out on three open databases: the UAM P300 database (Universidad Autonoma Metropolitana, Mexico), BCI competition II (dataset IIb) and BCI competition III (dataset II). Main results. The inclusion of the classification of non-target adjacent stimuli improves target classification in the classical row/column paradigm. A gain in mean single trial classification of 9.6% and an overall improvement of 25% in simulated spelling speed was achieved. Significance. We have provided further evidence that the ERPs produced by adjacent stimuli present discriminable features, which could provide additional information about the spatial location of intended symbols. This work promotes the searching of information on the peripheral stimulation responses to improve the performance of emerging visual ERP-based spellers.

  7. Classification of normal and pathological aging processes based on brain MRI morphology measures

    NASA Astrophysics Data System (ADS)

    Perez-Gonzalez, J. L.; Yanez-Suarez, O.; Medina-Bañuelos, V.

    2014-03-01

    Reported studies describing normal and abnormal aging based on anatomical MRI analysis do not consider morphological brain changes, but only volumetric measures to distinguish among these processes. This work presents a classification scheme, based both on size and shape features extracted from brain volumes, to determine different aging stages: healthy control (HC) adults, mild cognitive impairment (MCI), and Alzheimer's disease (AD). Three support vector machines were optimized and validated for the pair-wise separation of these three classes, using selected features from a set of 3D discrete compactness measures and normalized volumes of several global and local anatomical structures. Our analysis show classification rates of up to 98.3% between HC and AD; of 85% between HC and MCI and of 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indexes to classify different aging stages.

  8. Epidemiology of childhood brain tumours in Yorkshire, UK, 1974-95: geographical distribution and changing patterns of occurrence.

    PubMed Central

    McKinney, P. A.; Parslow, R. C.; Lane, S. A.; Bailey, C. C.; Lewis, I.; Picton, S.; Cartwright, R. A.

    1998-01-01

    From a high-quality population-based register of children with cancer, 455 cases diagnosed with central nervous system (CNS) tumours were analysed to examine patterns of occurrence and geographical distribution. There was a significant increase of 1.8% (95% CI 0.5-3.1, P < 0.01) in average annual incidence for all CNS tumours, mainly accounted for by a 3.1% rise (95% CI 0.1-6.1, P < 0.05) in primitive neuroectodermal tumours (PNETs) over the 22-year period 1974-95. These increases were not explained by an increase in the proportion of histologically verified tumours. In the most recent time period (1986-95), astrocytomas occurred more commonly than previously in 0 to 4-year olds. Geographical differences in incidence were evident at a large scale, between counties, for all tumours and astrocytomas, with lower rates in the most urbanized areas. At the level of census district and electoral wards, no association between incidence of CNS tumours and socioeconomic group, person-based population density or ethnicity was observed using Poisson regression modelling. Based on small-scale census geography, the patterns of distribution of CNS tumours do not suggest strong associations with geographical determinants of risk. This study finds a rising incidence of all CNS tumours and particularly primitive neuroectodermal tumours and shows that astrocytomas appear to be occurring at a younger age, most probably because of improved diagnosis with non-invasive technology. PMID:9764594

  9. Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition

    PubMed Central

    Cheng, Jun; Huang, Wei; Cao, Shuangliang; Yang, Ru; Yang, Wei; Yun, Zhaoqiang; Wang, Zhijian; Feng, Qianjin

    2015-01-01

    Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI. PMID:26447861

  10. Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards

    PubMed Central

    Plitt, Mark; Barnes, Kelly Anne; Martin, Alex

    2014-01-01

    Objectives Autism spectrum disorders (ASD) are diagnosed based on early-manifesting clinical symptoms, including markedly impaired social communication. We assessed the viability of resting-state functional MRI (rs-fMRI) connectivity measures as diagnostic biomarkers for ASD and investigated which connectivity features are predictive of a diagnosis. Methods Rs-fMRI scans from 59 high functioning males with ASD and 59 age- and IQ-matched typically developing (TD) males were used to build a series of machine learning classifiers. Classification features were obtained using 3 sets of brain regions. Another set of classifiers was built from participants' scores on behavioral metrics. An additional age and IQ-matched cohort of 178 individuals (89 ASD; 89 TD) from the Autism Brain Imaging Data Exchange (ABIDE) open-access dataset (http://fcon_1000.projects.nitrc.org/indi/abide/) were included for replication. Results High classification accuracy was achieved through several rs-fMRI methods (peak accuracy 76.67%). However, classification via behavioral measures consistently surpassed rs-fMRI classifiers (peak accuracy 95.19%). The class probability estimates, P(ASD|fMRI data), from brain-based classifiers significantly correlated with scores on a measure of social functioning, the Social Responsiveness Scale (SRS), as did the most informative features from 2 of the 3 sets of brain-based features. The most informative connections predominantly originated from regions strongly associated with social functioning. Conclusions While individuals can be classified as having ASD with statistically significant accuracy from their rs-fMRI scans alone, this method falls short of biomarker standards. Classification methods provided further evidence that ASD functional connectivity is characterized by dysfunction of large-scale functional networks, particularly those involved in social information processing. PMID:25685703

  11. Identification of primary tumors of brain metastases by SIMCA classification of IR spectroscopic images.

    PubMed

    Krafft, Christoph; Shapoval, Larysa; Sobottka, Stephan B; Geiger, Kathrin D; Schackert, Gabriele; Salzer, Reiner

    2006-07-01

    Brain metastases are secondary intracranial lesions which occur more frequently than primary brain tumors. The four most abundant types of brain metastasis originate from primary tumors of lung cancer, colorectal cancer, breast cancer and renal cell carcinoma. As metastatic cells contain the molecular information of the primary tissue cells and IR spectroscopy probes the molecular fingerprint of cells, IR spectroscopy based methods constitute a new approach to determine the origin of brain metastases. IR spectroscopic images of 4 by 4 mm2 tissue areas were recorded in transmission mode by a FTIR imaging spectrometer coupled to a focal plane array detector. Unsupervised cluster analysis revealed variances within each cryosection. Selected clusters of five IR images with known diagnoses trained a supervised classification model based on the algorithm soft independent modeling of class analogies (SIMCA). This model was applied to distinguish normal brain tissue from brain metastases and to identify the primary tumor of brain metastases in 15 independent IR images. All specimens were assigned to the correct tissue class. This proof-of-concept study demonstrates that IR spectroscopy can complement established methods such as histopathology or immunohistochemistry for diagnosis. PMID:16787638

  12. Automatic classification of lung tumour heterogeneity according to a visual-based score system in dynamic contrast enhanced CT sequences

    NASA Astrophysics Data System (ADS)

    Bevilacqua, Alessandro; Baiocco, Serena

    2016-03-01

    Computed tomography (CT) technologies have been considered for a long time as one of the most effective medical imaging tools for morphological analysis of body parts. Contrast Enhanced CT (CE-CT) also allows emphasising details of tissue structures whose heterogeneity, inspected through visual analysis, conveys crucial information regarding diagnosis and prognosis in several clinical pathologies. Recently, Dynamic CE-CT (DCE-CT) has emerged as a promising technique to perform also functional hemodynamic studies, with wide applications in the oncologic field. DCE-CT is based on repeated scans over time performed after intravenous administration of contrast agent, in order to study the temporal evolution of the tracer in 3D tumour tissue. DCE-CT pushes towards an intensive use of computers to provide automatically quantitative information to be used directly in clinical practice. This requires that visual analysis, representing the gold-standard for CT image interpretation, gains objectivity. This work presents the first automatic approach to quantify and classify the lung tumour heterogeneities based on DCE-CT image sequences, so as it is performed through visual analysis by experts. The approach developed relies on the spatio-temporal indices we devised, which also allow exploiting temporal data that enrich the knowledge of the tissue heterogeneity by providing information regarding the lesion status.

  13. Automatic segmentation and classification of human brain image based on a fuzzy brain atlas

    NASA Astrophysics Data System (ADS)

    Tan, Ou; Jia, Chunguang; Duan, Huilong; Lu, Weixue

    1998-09-01

    It is difficult to automatically segment and classify tomograph images of actual patient's brain. Therefore, many interactive operations are performed. It is very time consuming and its precision is much depended on the user. In this paper, we combine a brain atlas and 3D fuzzy image segmentation into the image matching. It can not only find out the precise boundary of anatomic structure but also save time of the interactive operation. At first, the anatomic information of atlas is mapped into tomograph images of actual brain with a two step image matching method. Then, based on the mapping result, a 3D fuzzy structure mask is calculated. With the fuzzy information of anatomic structure, a new method of fuzzy clustering based on genetic algorithm is used to segment and classify the real brain image. There is only a minimum requirement of interaction in the whole process, including removing the skull and selecting some intrinsic point pairs.

  14. Wireless brain-machine interface using EEG and EOG: brain wave classification and robot control

    NASA Astrophysics Data System (ADS)

    Oh, Sechang; Kumar, Prashanth S.; Kwon, Hyeokjun; Varadan, Vijay K.

    2012-04-01

    A brain-machine interface (BMI) links a user's brain activity directly to an external device. It enables a person to control devices using only thought. Hence, it has gained significant interest in the design of assistive devices and systems for people with disabilities. In addition, BMI has also been proposed to replace humans with robots in the performance of dangerous tasks like explosives handling/diffusing, hazardous materials handling, fire fighting etc. There are mainly two types of BMI based on the measurement method of brain activity; invasive and non-invasive. Invasive BMI can provide pristine signals but it is expensive and surgery may lead to undesirable side effects. Recent advances in non-invasive BMI have opened the possibility of generating robust control signals from noisy brain activity signals like EEG and EOG. A practical implementation of a non-invasive BMI such as robot control requires: acquisition of brain signals with a robust wearable unit, noise filtering and signal processing, identification and extraction of relevant brain wave features and finally, an algorithm to determine control signals based on the wave features. In this work, we developed a wireless brain-machine interface with a small platform and established a BMI that can be used to control the movement of a robot by using the extracted features of the EEG and EOG signals. The system records and classifies EEG as alpha, beta, delta, and theta waves. The classified brain waves are then used to define the level of attention. The acceleration and deceleration or stopping of the robot is controlled based on the attention level of the wearer. In addition, the left and right movements of eye ball control the direction of the robot.

  15. The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses

    PubMed Central

    2010-01-01

    Background Proton Magnetic Resonance (MR) Spectroscopy (MRS) is a widely available technique for those clinical centres equipped with MR scanners. Unlike the rest of MR-based techniques, MRS yields not images but spectra of metabolites in the tissues. In pathological situations, the MRS profile changes and this has been particularly described for brain tumours. However, radiologists are frequently not familiar to the interpretation of MRS data and for this reason, the usefulness of decision-support systems (DSS) in MRS data analysis has been explored. Results This work presents the INTERPRET DSS version 3.0, analysing the improvements made from its first release in 2002. Version 3.0 is aimed to be a program that 1st, can be easily used with any new case from any MR scanner manufacturer and 2nd, improves the initial analysis capabilities of the first version. The main improvements are an embedded database, user accounts, more diagnostic discrimination capabilities and the possibility to analyse data acquired under additional data acquisition conditions. Other improvements include a customisable graphical user interface (GUI). Most diagnostic problems included have been addressed through a pattern-recognition based approach, in which classifiers based on linear discriminant analysis (LDA) were trained and tested. Conclusions The INTERPRET DSS 3.0 allows radiologists, medical physicists, biochemists or, generally speaking, any person with a minimum knowledge of what an MR spectrum is, to enter their own SV raw data, acquired at 1.5 T, and to analyse them. The system is expected to help in the categorisation of MR Spectra from abnormal brain masses. PMID:21114820

  16. Brain tumour differentiation: rapid stratified serum diagnostics via attenuated total reflection Fourier-transform infrared spectroscopy.

    PubMed

    Hands, James R; Clemens, Graeme; Stables, Ryan; Ashton, Katherine; Brodbelt, Andrew; Davis, Charles; Dawson, Timothy P; Jenkinson, Michael D; Lea, Robert W; Walker, Carol; Baker, Matthew J

    2016-05-01

    The ability to diagnose cancer rapidly with high sensitivity and specificity is essential to exploit advances in new treatments to lead significant reductions in mortality and morbidity. Current cancer diagnostic tests observing tissue architecture and specific protein expression for specific cancers suffer from inter-observer variability, poor detection rates and occur when the patient is symptomatic. A new method for the detection of cancer using 1 μl of human serum, attenuated total reflection-Fourier transform infrared spectroscopy and pattern recognition algorithms is reported using a 433 patient dataset (3897 spectra). To the best of our knowledge, we present the largest study on serum mid-infrared spectroscopy for cancer research. We achieve optimum sensitivities and specificities using a Radial Basis Function Support Vector Machine of between 80.0 and 100 % for all strata and identify the major spectral features, hence biochemical components, responsible for the discrimination within each stratum. We assess feature fed-SVM analysis for our cancer versus non-cancer model and achieve 91.5 and 83.0 % sensitivity and specificity respectively. We demonstrate the use of infrared light to provide a spectral signature from human serum to detect, for the first time, cancer versus non-cancer, metastatic cancer versus organ confined, brain cancer severity and the organ of origin of metastatic disease from the same sample enabling stratified diagnostics depending upon the clinical question asked. PMID:26874961

  17. Supervised, Multivariate, Whole-Brain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia Research

    PubMed Central

    Janousova, Eva; Montana, Giovanni; Kasparek, Tomas; Schwarz, Daniel

    2016-01-01

    We examined how penalized linear discriminant analysis with resampling, which is a supervised, multivariate, whole-brain reduction technique, can help schizophrenia diagnostics and research. In an experiment with magnetic resonance brain images of 52 first-episode schizophrenia patients and 52 healthy controls, this method allowed us to select brain areas relevant to schizophrenia, such as the left prefrontal cortex, the anterior cingulum, the right anterior insula, the thalamus, and the hippocampus. Nevertheless, the classification performance based on such reduced data was not significantly better than the classification of data reduced by mass univariate selection using a t-test or unsupervised multivariate reduction using principal component analysis. Moreover, we found no important influence of the type of imaging features, namely local deformations or gray matter volumes, and the classification method, specifically linear discriminant analysis or linear support vector machines, on the classification results. However, we ascertained significant effect of a cross-validation setting on classification performance as classification results were overestimated even though the resampling was performed during the selection of brain imaging features. Therefore, it is critically important to perform cross-validation in all steps of the analysis (not only during classification) in case there is no external validation set to avoid optimistically biasing the results of classification studies. PMID:27610072

  18. Supervised, Multivariate, Whole-Brain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia Research.

    PubMed

    Janousova, Eva; Montana, Giovanni; Kasparek, Tomas; Schwarz, Daniel

    2016-01-01

    We examined how penalized linear discriminant analysis with resampling, which is a supervised, multivariate, whole-brain reduction technique, can help schizophrenia diagnostics and research. In an experiment with magnetic resonance brain images of 52 first-episode schizophrenia patients and 52 healthy controls, this method allowed us to select brain areas relevant to schizophrenia, such as the left prefrontal cortex, the anterior cingulum, the right anterior insula, the thalamus, and the hippocampus. Nevertheless, the classification performance based on such reduced data was not significantly better than the classification of data reduced by mass univariate selection using a t-test or unsupervised multivariate reduction using principal component analysis. Moreover, we found no important influence of the type of imaging features, namely local deformations or gray matter volumes, and the classification method, specifically linear discriminant analysis or linear support vector machines, on the classification results. However, we ascertained significant effect of a cross-validation setting on classification performance as classification results were overestimated even though the resampling was performed during the selection of brain imaging features. Therefore, it is critically important to perform cross-validation in all steps of the analysis (not only during classification) in case there is no external validation set to avoid optimistically biasing the results of classification studies. PMID:27610072

  19. Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue

    NASA Astrophysics Data System (ADS)

    Sams, Michael; Silye, Rene; Göhring, Janett; Muresan, Leila; Schilcher, Kurt; Jacak, Jaroslaw

    2014-01-01

    We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.

  20. Fusing in vivo and ex vivo NMR sources of information for brain tumor classification

    NASA Astrophysics Data System (ADS)

    Croitor-Sava, A. R.; Martinez-Bisbal, M. C.; Laudadio, T.; Piquer, J.; Celda, B.; Heerschap, A.; Sima, D. M.; Van Huffel, S.

    2011-11-01

    In this study we classify short echo-time brain magnetic resonance spectroscopic imaging (MRSI) data by applying a model-based canonical correlation analyses algorithm and by using, as prior knowledge, multimodal sources of information coming from high-resolution magic angle spinning (HR-MAS), MRSI and magnetic resonance imaging. The potential and limitations of fusing in vivo and ex vivo nuclear magnetic resonance sources to detect brain tumors is investigated. We present various modalities for multimodal data fusion, study the effect and the impact of using multimodal information for classifying MRSI brain glial tumors data and analyze which parameters influence the classification results by means of extensive simulation and in vivo studies. Special attention is drawn to the possibility of considering HR-MAS data as a complementary dataset when dealing with a lack of MRSI data needed to build a classifier. Results show that HR-MAS information can have added value in the process of classifying MRSI data.

  1. CAVIAR: CLASSIFICATION VIA AGGREGATED REGRESSION AND ITS APPLICATION IN CLASSIFYING OASIS BRAIN DATABASE.

    PubMed

    Chen, Ting; Rangarajan, Anand; Vemuri, Baba C

    2010-04-14

    This paper presents a novel classification via aggregated regression algorithm - dubbed CAVIAR - and its application to the OASIS MRI brain image database. The CAVIAR algorithm simultaneously combines a set of weak learners based on the assumption that the weight combination for the final strong hypothesis in CAVIAR depends on both the weak learners and the training data. A regularization scheme using the nearest neighbor method is imposed in the testing stage to avoid overfitting. A closed form solution to the cost function is derived for this algorithm. We use a novel feature - the histogram of the deformation field between the MRI brain scan and the atlas which captures the structural changes in the scan with respect to the atlas brain - and this allows us to automatically discriminate between various classes within OASIS [1] using CAVIAR. We empirically show that CAVIAR significantly increases the performance of the weak classifiers by showcasing the performance of our technique on OASIS. PMID:21151847

  2. Predict or classify: The deceptive role of time-locking in brain signal classification.

    PubMed

    Rusconi, Marco; Valleriani, Angelo

    2016-01-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal. PMID:27320688

  3. Predict or classify: The deceptive role of time-locking in brain signal classification

    NASA Astrophysics Data System (ADS)

    Rusconi, Marco; Valleriani, Angelo

    2016-06-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

  4. Classification of mathematics deficiency using shape and scale analysis of 3D brain structures

    NASA Astrophysics Data System (ADS)

    Kurtek, Sebastian; Klassen, Eric; Gore, John C.; Ding, Zhaohua; Srivastava, Anuj

    2011-03-01

    We investigate the use of a recent technique for shape analysis of brain substructures in identifying learning disabilities in third-grade children. This Riemannian technique provides a quantification of differences in shapes of parameterized surfaces, using a distance that is invariant to rigid motions and re-parameterizations. Additionally, it provides an optimal registration across surfaces for improved matching and comparisons. We utilize an efficient gradient based method to obtain the optimal re-parameterizations of surfaces. In this study we consider 20 different substructures in the human brain and correlate the differences in their shapes with abnormalities manifested in deficiency of mathematical skills in 106 subjects. The selection of these structures is motivated in part by the past links between their shapes and cognitive skills, albeit in broader contexts. We have studied the use of both individual substructures and multiple structures jointly for disease classification. Using a leave-one-out nearest neighbor classifier, we obtained a 62.3% classification rate based on the shape of the left hippocampus. The use of multiple structures resulted in an improved classification rate of 71.4%.

  5. Predict or classify: The deceptive role of time-locking in brain signal classification

    PubMed Central

    Rusconi, Marco; Valleriani, Angelo

    2016-01-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal. PMID:27320688

  6. Fast and accurate water content and T2* mapping in brain tumours localised with FET-PET

    NASA Astrophysics Data System (ADS)

    Oros-Peusquens, A.-M.; Keil, F.; Langen, K. J.; Herzog, H.; Stoffels, G.; Weiss, C.; Shah, N. J.

    2014-01-01

    The availability of combined MR-PET scanners opens new opportunities for the characterisation of tumour environment. In this study, water content and relaxation properties of glioblastoma were investigated in five patients using advanced MRI. The region containing metabolically active tumour tissue was defined by simultaneously measured FET-PET uptake. The mean value of water content in tumour tissue - obtained noninvasively with high precision and accuracy for the first time - amounted to 84.5%, similar to the value for normal grey matter. Constancy of water content contrasted with a large variability of T2* values in tumour tissue, qualitatively related to the magnetic inhomogeneity of tissue created by blood vessels and/or microbleeds. The quantitative MRI protocol takes 71/2 > min of measurement time and is proposed for extended clinical use.

  7. Computational Classification Approach to Profile Neuron Subtypes from Brain Activity Mapping Data

    PubMed Central

    Li, Meng; Zhao, Fang; Lee, Jason; Wang, Dong; Kuang, Hui; Tsien, Joe Z.

    2015-01-01

    The analysis of cell type-specific activity patterns during behaviors is important for better understanding of how neural circuits generate cognition, but has not been well explored from in vivo neurophysiological datasets. Here, we describe a computational approach to uncover distinct cell subpopulations from in vivo neural spike datasets. This method, termed “inter-spike-interval classification-analysis” (ISICA), is comprised of four major steps: spike pattern feature-extraction, pre-clustering analysis, clustering classification, and unbiased classification-dimensionality selection. By using two key features of spike dynamic - namely, gamma distribution shape factors and a coefficient of variation of inter-spike interval - we show that this ISICA method provides invariant classification for dopaminergic neurons or CA1 pyramidal cell subtypes regardless of the brain states from which spike data were collected. Moreover, we show that these ISICA-classified neuron subtypes underlie distinct physiological functions. We demonstrate that the uncovered dopaminergic neuron subtypes encoded distinct aspects of fearful experiences such as valence or value, whereas distinct hippocampal CA1 pyramidal cells responded differentially to ketamine-induced anesthesia. This ISICA method should be useful to better data mining of large-scale in vivo neural datasets, leading to novel insights into circuit dynamics associated with cognitions. PMID:26212360

  8. Asynchronous P300 classification in a reactive brain-computer interface during an outlier detection task

    NASA Astrophysics Data System (ADS)

    Krumpe, Tanja; Walter, Carina; Rosenstiel, Wolfgang; Spüler, Martin

    2016-08-01

    Objective. In this study, the feasibility of detecting a P300 via an asynchronous classification mode in a reactive EEG-based brain-computer interface (BCI) was evaluated. The P300 is one of the most popular BCI control signals and therefore used in many applications, mostly for active communication purposes (e.g. P300 speller). As the majority of all systems work with a stimulus-locked mode of classification (synchronous), the field of applications is limited. A new approach needs to be applied in a setting in which a stimulus-locked classification cannot be used due to the fact that the presented stimuli cannot be controlled or predicted by the system. Approach. A continuous observation task requiring the detection of outliers was implemented to test such an approach. The study was divided into an offline and an online part. Main results. Both parts of the study revealed that an asynchronous detection of the P300 can successfully be used to detect single events with high specificity. It also revealed that no significant difference in performance was found between the synchronous and the asynchronous approach. Significance. The results encourage the use of an asynchronous classification approach in suitable applications without a potential loss in performance.

  9. Joint Time-Frequency-Space Classification of EEG in a Brain-Computer Interface Application

    NASA Astrophysics Data System (ADS)

    Molina, Gary N. Garcia; Ebrahimi, Touradj; Vesin, Jean-Marc

    2003-12-01

    Brain-computer interface is a growing field of interest in human-computer interaction with diverse applications ranging from medicine to entertainment. In this paper, we present a system which allows for classification of mental tasks based on a joint time-frequency-space decorrelation, in which mental tasks are measured via electroencephalogram (EEG) signals. The efficiency of this approach was evaluated by means of real-time experimentations on two subjects performing three different mental tasks. To do so, a number of protocols for visualization, as well as training with and without feedback, were also developed. Obtained results show that it is possible to obtain good classification of simple mental tasks, in view of command and control, after a relatively small amount of training, with accuracies around 80%, and in real time.

  10. Clinical features of gastroenteropancreatic tumours

    PubMed Central

    Czarnywojtek, Agata; Bączyk, Maciej; Ziemnicka, Katarzyna; Fischbach, Jakub; Wrotkowska, Elżbieta; Ruchała, Marek

    2015-01-01

    Gastroenteropancreatic (GEP) endocrine tumours (carcinoids and pancreatic islet cell tumours) are composed of multipotent neuroendocrine cells that exhibit a unique ability to produce, store, and secrete biologically active substances and cause distinct clinical syndromes. The classification of GEP tumours as functioning or non-functioning is based on the presence of symptoms that accompany these syndromes secondary to the secretion of hormones, neuropeptides and/or neurotransmitters (functioning tumours). Non-functioning tumours are considered to be neoplasms of neuroendocrine differentiation that are not associated with obvious symptoms attributed to the hypersecretion of metabolically active substances. However, a number of these tumours are either capable of producing low levels of such substances, which can be detected by immunohistochemistry but are insufficient to cause symptoms related to a clinical syndrome, or alternatively, they may secrete substances that are either metabolically inactive or inappropriately processed. In some cases, GEP tumours are not associated with the production of any hormone or neurotransmitter. Both functioning and non-functioning tumours can also produce symptoms due to mass effects compressing vital surrounding structures. Gastroenteropancreatic tumours are usually classified further according to the anatomic site of origin: foregut (including respiratory tract, thymus, stomach, duodenum, and pancreas), midgut (including small intestine, appendix, and right colon), and hindgut (including transverse colon, sigmoid, and rectum). Within these subgroups the biological and clinical characteristics of the tumours vary considerably, but this classification is still in use because a significant number of previous studies, mainly observational, have used it extensively. PMID:26516377

  11. Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks.

    PubMed

    Reddick, W E; Glass, J O; Cook, E N; Elkin, T D; Deaton, R J

    1997-12-01

    We present a fully automated process for segmentation and classification of multispectral magnetic resonance (MR) images. This hybrid neural network method uses a Kohonen self-organizing neural network for segmentation and a multilayer backpropagation neural network for classification. To separate different tissue types, this process uses the standard T1-, T2-, and PD-weighted MR images acquired in clinical examinations. Volumetric measurements of brain structures, relative to intracranial volume, were calculated for an index transverse section in 14 normal subjects (median age 25 years; seven male, seven female). This index slice was at the level of the basal ganglia, included both genu and splenium of the corpus callosum, and generally, showed the putamen and lateral ventricle. An intraclass correlation of this automated segmentation and classification of tissues with the accepted standard of radiologist identification for the index slice in the 14 volunteers demonstrated coefficients (ri) of 0.91, 0.95, and 0.98 for white matter, gray matter, and ventricular cerebrospinal fluid (CSF), respectively. An analysis of variance for estimates of brain parenchyma volumes in five volunteers imaged five times each demonstrated high intrasubject reproducibility with a significance of at least p < 0.05 for white matter, gray matter, and white/gray partial volumes. The population variation, across 14 volunteers, demonstrated little deviation from the averages for gray and white matter, while partial volume classes exhibited a slightly higher degree of variability. This fully automated technique produces reliable and reproducible MR image segmentation and classification while eliminating intra- and interobserver variability. PMID:9533591

  12. Neuropsychological assessment of individuals with brain tumor: comparison of approaches used in the classification of impairment.

    PubMed

    Dwan, Toni Maree; Ownsworth, Tamara; Chambers, Suzanne; Walker, David G; Shum, David H K

    2015-01-01

    Approaches to classifying neuropsychological impairment after brain tumor vary according to testing level (individual tests, domains, or global index) and source of reference (i.e., norms, controls, and pre-morbid functioning). This study aimed to compare rates of impairment according to different classification approaches. Participants were 44 individuals (57% female) with a primary brain tumor diagnosis (mean age = 45.6 years) and 44 matched control participants (59% female, mean age = 44.5 years). All participants completed a test battery that assesses pre-morbid IQ (Wechsler adult reading test), attention/processing speed (digit span, trail making test A), memory (Hopkins verbal learning test-revised, Rey-Osterrieth complex figure-recall), and executive function (trail making test B, Rey-Osterrieth complex figure copy, controlled oral word association test). Results indicated that across the different sources of reference, 86-93% of participants were classified as impaired at a test-specific level, 61-73% were classified as impaired at a domain-specific level, and 32-50% were classified as impaired at a global level. Rates of impairment did not significantly differ according to source of reference (p > 0.05); however, at the individual participant level, classification based on estimated pre-morbid IQ was often inconsistent with classification based on the norms or controls. Participants with brain tumor performed significantly poorer than matched controls on tests of neuropsychological functioning, including executive function (p = 0.001) and memory (p < 0.001), but not attention/processing speed (p > 0.05). These results highlight the need to examine individuals' performance across a multi-faceted neuropsychological test battery to avoid over- or under-estimation of impairment. PMID:25815271

  13. A comparative study of feature extraction and blind source separation of independent component analysis (ICA) on childhood brain tumour 1H magnetic resonance spectra.

    PubMed

    Hao, Jie; Zou, Xin; Wilson, Martin P; Davies, Nigel P; Sun, Yu; Peet, Andrew C; Arvanitis, Theodoros N

    2009-10-01

    Independent component analysis (ICA) has the potential of determining automatically the metabolite signals which make up MR spectra. However, the reliability with which this is accomplished and the optimal approach for investigating in vivo MRS have not been determined. Furthermore, the properties of ICA in brain tumour MRS with respect to dataset size and data quality have not been systematically explored. The two common techniques for applying ICA, blind source separation (BSS) and feature extraction (FE) were examined in this study using simulated data and the findings confirmed on patient data. Short echo time (TE 30 ms), low and high field (1.5 and 3 T) in vivo brain tumour MR spectra of childhood astrocytoma, ependymoma and medulloblastoma were generated by using a quantum mechanical simulator with ten metabolite and lipid components. Patient data (TE 30 ms, 1.5 T) were acquired from children with brain tumours. ICA of simulated data shows that individual metabolite components can be extracted from a set of MRS data. The BSS method generates independent components with a closer correlation to the original metabolite and lipid components than the FE method when the number of spectra in the dataset is small. The experiments also show that stable results are achieved with 300 MRS at an SNR equal to 10. The FE method is relatively insensitive to different ranges of full width at half maximum (FWHM) (from 0 to 3 Hz), whereas the BSS method degrades on increasing the range of FWHM. The peak frequency variations do not affect the results within the range of +/-0.08 ppm for the FE method, and +/-0.05 ppm for the BSS method. When the methods were applied to the patient dataset, results consistent with the synthesized experiments were obtained. PMID:19431141

  14. Classification

    ERIC Educational Resources Information Center

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  15. Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.

    PubMed

    Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V; Robles, Montserrat; Aparici, F; Martí-Bonmatí, L; García-Gómez, Juan M

    2015-01-01

    Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

  16. Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

    PubMed Central

    Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

    2015-01-01

    Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

  17. Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity.

    PubMed

    Rashid, Barnaly; Arbabshirani, Mohammad R; Damaraju, Eswar; Cetin, Mustafa S; Miller, Robyn; Pearlson, Godfrey D; Calhoun, Vince D

    2016-07-01

    Recently, functional network connectivity (FNC, defined as the temporal correlation among spatially distant brain networks) has been used to examine the functional organization of brain networks in various psychiatric illnesses. Dynamic FNC is a recent extension of the conventional FNC analysis that takes into account FNC changes over short periods of time. While such dynamic FNC measures may be more informative about various aspects of connectivity, there has been no detailed head-to-head comparison of the ability of static and dynamic FNC to perform classification in complex mental illnesses. This paper proposes a framework for automatic classification of schizophrenia, bipolar and healthy subjects based on their static and dynamic FNC features. Also, we compare cross-validated classification performance between static and dynamic FNC. Results show that the dynamic FNC significantly outperforms the static FNC in terms of predictive accuracy, indicating that features from dynamic FNC have distinct advantages over static FNC for classification purposes. Moreover, combining static and dynamic FNC features does not significantly improve the classification performance over the dynamic FNC features alone, suggesting that static FNC does not add any significant information when combined with dynamic FNC for classification purposes. A three-way classification methodology based on static and dynamic FNC features discriminates individual subjects into appropriate diagnostic groups with high accuracy. Our proposed classification framework is potentially applicable to additional mental disorders. PMID:27118088

  18. Threshold selection for classification of MR brain images by clustering method

    SciTech Connect

    Moldovanu, Simona; Obreja, Cristian; Moraru, Luminita

    2015-12-07

    Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

  19. Threshold selection for classification of MR brain images by clustering method

    NASA Astrophysics Data System (ADS)

    Moldovanu, Simona; Obreja, Cristian; Moraru, Luminita

    2015-12-01

    Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

  20. The significance of electron spin resonance of the ascorbic acid radical in freeze dried human brain tumours and oedematous or normal periphery.

    PubMed Central

    Mueller, H. W.; Tannert, S.

    1986-01-01

    The ESR spectrum, attributed to the ascorbic acid (ascorbyl) radical and obtained by exposing freeze dried material to air, can not be used as proof for the occurrence of in vivo free radical reactions. Depending on the method of freeze drying, the content of blood or hemolyzed blood is the dominant factor in creating higher than normal ESR signals in brain or related tissue. These findings explain why the signal, though larger in many human brain tumours than in their surroundings, is not indicative of malignancy. No differences are seen between oedematous and normal tissue. The ascorbyl radical is definitely not stable in aqueous solution, which indicates that fresh tissue sections can also not be used to study in vivo radicals by ESR. PMID:3008800

  1. An experimental environment for the production, exchange and discussion of fused radiology images, for the management of patients with residual brain tumour disease.

    PubMed

    Sakellaropoulos, G C; Kagadis, G C; Karystianos, C; Karnabatidis, D; Constantoyannis, C; Nikiforidis, G C

    2003-06-01

    The present work aims to display the use of groupware as a tool for better management of the available resources (human, computing and imaging) within the University Hospital of Patras, Greece for the task of managing patients with postoperative residual brain tumour. Emphasis is given to the additional information that can be revealed and taken into account from novel image processing techniques, developed by our group, and the central role of the Medical Physicist in the groupware. Fused images, produced by the combination of CT, MR and SPECT representations of the brain, contain both anatomical and functional information and comprise a new representation of reality. Medical experts, unfamiliar with this new representation, form a groupware for the task of interpreting them and providing better services to the patient. Groupware procedures, facilitated by modern network technology, bring experts' tacit knowledge to the surface and facilitate its exchange. PMID:14692590

  2. Voxel-based discriminant map classification on brain ventricles for Alzheimer's disease

    NASA Astrophysics Data System (ADS)

    Wang, Jingnan; de Haan, Gerard; Unay, Devrim; Soldea, Octavian; Ekin, Ahmet

    2009-02-01

    One major hallmark of the Alzheimer's disease (AD) is the loss of neurons in the brain. In many cases, medical experts use magnetic resonance imaging (MRI) to qualitatively measure the neuronal loss by the shrinkage or enlargement of the structures-of-interest. Brain ventricle is one of the popular choices. It is easily detectable in clinical MR images due to the high contrast of the cerebro-spinal fluid (CSF) with the rest of the parenchyma. Moreover, atrophy in any periventricular structure will directly lead to ventricle enlargement. For quantitative analysis, volume is the common choice. However, volume is a gross measure and it cannot capture the entire complexity of the anatomical shape. Since most existing shape descriptors are complex and difficult-to-reproduce, more straightforward and robust ways to extract ventricle shape features are preferred in the diagnosis. In this paper, a novel ventricle shape based classification method for Alzheimer's disease has been proposed. Training process is carried out to generate two probability maps for two training classes: healthy controls (HC) and AD patients. By subtracting the HC probability map from the AD probability map, we get a 3D ventricle discriminant map. Then a matching coefficient has been calculated between each training subject and the discriminant map. An adjustable cut-off point of the matching coefficients has been drawn for the two classes. Generally, the higher the cut-off point that has been drawn, the higher specificity can be achieved. However, it will result in relatively lower sensitivity and vice versa. The benchmarked results against volume based classification show that the area under the ROC curves for our proposed method is as high as 0.86 compared with only 0.71 for volume based classification method.

  3. Tumours of the soft (mesenchymal) tissues.

    PubMed

    Weiss, E

    1974-01-01

    This is a classification of tumours of fibrous tissue, fat, muscle, blood and lymph vessels, and mast cells, irrespective of the region of the body in which they arise. Tumours of fibrous tissue are divided into fibroma, fibrosarcoma (including "canine haemangiopericytoma"), other sarcomas, equine sarcoid, and various tumour-like lesions. The histological appearance of the tumours is described and illustrated with photographs. PMID:4371740

  4. Generation of brain tumours in mice by Cre-mediated recombination of neural progenitors in situ with the tamoxifen metabolite endoxifen.

    PubMed

    Benedykcinska, Anna; Ferreira, Andreia; Lau, Joanne; Broni, Jessica; Richard-Loendt, Angela; Henriquez, Nico V; Brandner, Sebastian

    2016-02-01

    Targeted cell- or region-specific gene recombination is widely used in the functional analysis of genes implicated in development and disease. In the brain, targeted gene recombination has become a mainstream approach to study neurodegeneration or tumorigenesis. The use of the Cre-loxP system to study tumorigenesis in the adult central nervous system (CNS) can be limited, when the promoter (such as GFAP) is also transiently expressed during development, which can result in the recombination of progenies of different lineages. Engineering of transgenic mice expressing Cre recombinase fused to a mutant of the human oestrogen receptor (ER) allows the circumvention of transient developmental Cre expression by inducing recombination in the adult organism. The recombination of loxP sequences occurs only in the presence of tamoxifen. Systemic administration of tamoxifen can, however, exhibit toxicity and might also recombine unwanted cell populations if the promoter driving Cre expression is active at the time of tamoxifen administration. Here, we report that a single site-specific injection of an active derivative of tamoxifen successfully activates Cre recombinase and selectively recombines tumour suppressor genes in neural progenitor cells of the subventricular zone in mice, and we demonstrate its application in a model for the generation of intrinsic brain tumours. PMID:26704996

  5. Generation of brain tumours in mice by Cre-mediated recombination of neural progenitors in situ with the tamoxifen metabolite endoxifen

    PubMed Central

    Benedykcinska, Anna; Ferreira, Andreia; Lau, Joanne; Broni, Jessica; Richard-Loendt, Angela; Henriquez, Nico V.; Brandner, Sebastian

    2016-01-01

    ABSTRACT Targeted cell- or region-specific gene recombination is widely used in the functional analysis of genes implicated in development and disease. In the brain, targeted gene recombination has become a mainstream approach to study neurodegeneration or tumorigenesis. The use of the Cre-loxP system to study tumorigenesis in the adult central nervous system (CNS) can be limited, when the promoter (such as GFAP) is also transiently expressed during development, which can result in the recombination of progenies of different lineages. Engineering of transgenic mice expressing Cre recombinase fused to a mutant of the human oestrogen receptor (ER) allows the circumvention of transient developmental Cre expression by inducing recombination in the adult organism. The recombination of loxP sequences occurs only in the presence of tamoxifen. Systemic administration of tamoxifen can, however, exhibit toxicity and might also recombine unwanted cell populations if the promoter driving Cre expression is active at the time of tamoxifen administration. Here, we report that a single site-specific injection of an active derivative of tamoxifen successfully activates Cre recombinase and selectively recombines tumour suppressor genes in neural progenitor cells of the subventricular zone in mice, and we demonstrate its application in a model for the generation of intrinsic brain tumours. PMID:26704996

  6. New tissue priors for improved automated classification of subcortical brain structures on MRI.

    PubMed

    Lorio, S; Fresard, S; Adaszewski, S; Kherif, F; Chowdhury, R; Frackowiak, R S; Ashburner, J; Helms, G; Weiskopf, N; Lutti, A; Draganski, B

    2016-04-15

    Despite the constant improvement of algorithms for automated brain tissue classification, the accurate delineation of subcortical structures using magnetic resonance images (MRI) data remains challenging. The main difficulties arise from the low gray-white matter contrast of iron rich areas in T1-weighted (T1w) MRI data and from the lack of adequate priors for basal ganglia and thalamus. The most recent attempts to obtain such priors were based on cohorts with limited size that included subjects in a narrow age range, failing to account for age-related gray-white matter contrast changes. Aiming to improve the anatomical plausibility of automated brain tissue classification from T1w data, we have created new tissue probability maps for subcortical gray matter regions. Supported by atlas-derived spatial information, raters manually labeled subcortical structures in a cohort of healthy subjects using magnetization transfer saturation and R2* MRI maps, which feature optimal gray-white matter contrast in these areas. After assessment of inter-rater variability, the new tissue priors were tested on T1w data within the framework of voxel-based morphometry. The automated detection of gray matter in subcortical areas with our new probability maps was more anatomically plausible compared to the one derived with currently available priors. We provide evidence that the improved delineation compensates age-related bias in the segmentation of iron rich subcortical regions. The new tissue priors, allowing robust detection of basal ganglia and thalamus, have the potential to enhance the sensitivity of voxel-based morphometry in both healthy and diseased brains. PMID:26854557

  7. New tissue priors for improved automated classification of subcortical brain structures on MRI☆

    PubMed Central

    Lorio, S.; Fresard, S.; Adaszewski, S.; Kherif, F.; Chowdhury, R.; Frackowiak, R.S.; Ashburner, J.; Helms, G.; Weiskopf, N.; Lutti, A.; Draganski, B.

    2016-01-01

    Despite the constant improvement of algorithms for automated brain tissue classification, the accurate delineation of subcortical structures using magnetic resonance images (MRI) data remains challenging. The main difficulties arise from the low gray-white matter contrast of iron rich areas in T1-weighted (T1w) MRI data and from the lack of adequate priors for basal ganglia and thalamus. The most recent attempts to obtain such priors were based on cohorts with limited size that included subjects in a narrow age range, failing to account for age-related gray-white matter contrast changes. Aiming to improve the anatomical plausibility of automated brain tissue classification from T1w data, we have created new tissue probability maps for subcortical gray matter regions. Supported by atlas-derived spatial information, raters manually labeled subcortical structures in a cohort of healthy subjects using magnetization transfer saturation and R2* MRI maps, which feature optimal gray-white matter contrast in these areas. After assessment of inter-rater variability, the new tissue priors were tested on T1w data within the framework of voxel-based morphometry. The automated detection of gray matter in subcortical areas with our new probability maps was more anatomically plausible compared to the one derived with currently available priors. We provide evidence that the improved delineation compensates age-related bias in the segmentation of iron rich subcortical regions. The new tissue priors, allowing robust detection of basal ganglia and thalamus, have the potential to enhance the sensitivity of voxel-based morphometry in both healthy and diseased brains. PMID:26854557

  8. ADHD classification by a texture analysis of anatomical brain MRI data.

    PubMed

    Chang, Che-Wei; Ho, Chien-Chang; Chen, Jyh-Horng

    2012-01-01

    The ADHD-200 Global Competition provides an excellent opportunity for building diagnostic classifiers of Attention-Deficit/Hyperactivity Disorder (ADHD) based on resting-state functional MRI (rs-fMRI) and structural MRI data. Here, we introduce a simple method to classify ADHD based on morphological information without using functional data. Our test results show that the accuracy of this approach is competitive with methods based on rs-fMRI data. We used isotropic local binary patterns on three orthogonal planes (LBP-TOP) to extract features from MR brain images. Subsequently, support vector machines (SVM) were used to develop classification models based on the extracted features. In this study, a total of 436 male subjects (210 with ADHD and 226 controls) were analyzed to show the discriminative power of the method. To analyze the properties of this approach, we tested disparate LBP-TOP features from various parcellations and different image resolutions. Additionally, morphological information using a single brain tissue type (i.e., gray matter (GM), white matter (WM), and CSF) was tested. The highest accuracy we achieved was 0.6995. The LBP-TOP was found to provide better discriminative power using whole-brain data as the input. Datasets with higher resolution can train models with increased accuracy. The information from GM plays a more important role than that of other tissue types. These results and the properties of LBP-TOP suggest that most of the disparate feature distribution comes from different patterns of cortical folding. Using LBP-TOP, we provide an ADHD classification model based only on anatomical information, which is easier to obtain in the clinical environment and which is simpler to preprocess compared with rs-fMRI data. PMID:23024630

  9. Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas

    PubMed Central

    Chestek, Cynthia A.; Gilja, Vikash; Blabe, Christine H.; Foster, Brett L.; Shenoy, Krishna V.; Parvizi, Josef; Henderson, Jaimie M.

    2013-01-01

    Objective Brain machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system. Approach We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed 5 distinct isometric hand postures, as well as 4 distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with 2 participants. Main Results Classification rates were 68%, 84%, and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1×). A similar increase in errors (2.6×) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system. Significance These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training, and

  10. Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas

    NASA Astrophysics Data System (ADS)

    Chestek, Cynthia A.; Gilja, Vikash; Blabe, Christine H.; Foster, Brett L.; Shenoy, Krishna V.; Parvizi, Josef; Henderson, Jaimie M.

    2013-04-01

    Objective. Brain-machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system.Approach. We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed five distinct isometric hand postures, as well as four distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with two participants. Main results. Classification rates were 68%, 84% and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1×). A similar increase in errors (2.6×) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system. Significance. These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training

  11. Case-control study of the association between malignant brain tumours diagnosed between 2007 and 2009 and mobile and cordless phone use

    PubMed Central

    HARDELL, LENNART; CARLBERG, MICHAEL; SÖDERQVIST, FREDRIK; MILD, KJELL HANSSON

    2013-01-01

    Previous studies have shown a consistent association between long-term use of mobile and cordless phones and glioma and acoustic neuroma, but not for meningioma. When used these phones emit radiofrequency electromagnetic fields (RF-EMFs) and the brain is the main target organ for the hand-held phone. The International Agency for Research on Cancer (IARC) classified in May, 2011 RF-EMF as a group 2B, i.e. a ‘possible’ human carcinogen. The aim of this study was to further explore the relationship between especially long-term (>10 years) use of wireless phones and the development of malignant brain tumours. We conducted a new case-control study of brain tumour cases of both genders aged 18–75 years and diagnosed during 2007–2009. One population-based control matched on gender and age (within 5 years) was used to each case. Here, we report on malignant cases including all available controls. Exposures on e.g. use of mobile phones and cordless phones were assessed by a self-administered questionnaire. Unconditional logistic regression analysis was performed, adjusting for age, gender, year of diagnosis and socio-economic index using the whole control sample. Of the cases with a malignant brain tumour, 87% (n=593) participated, and 85% (n=1,368) of controls in the whole study answered the questionnaire. The odds ratio (OR) for mobile phone use of the analogue type was 1.8, 95% confidence interval (CI)=1.04–3.3, increasing with >25 years of latency (time since first exposure) to an OR=3.3, 95% CI=1.6–6.9. Digital 2G mobile phone use rendered an OR=1.6, 95% CI=0.996–2.7, increasing with latency >15–20 years to an OR=2.1, 95% CI=1.2–3.6. The results for cordless phone use were OR=1.7, 95% CI=1.1–2.9, and, for latency of 15–20 years, the OR=2.1, 95% CI=1.2–3.8. Few participants had used a cordless phone for >20–25 years. Digital type of wireless phones (2G and 3G mobile phones, cordless phones) gave increased risk with latency >1–5 years, then a

  12. EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces

    NASA Astrophysics Data System (ADS)

    Ashari, Rehab Bahaaddin

    Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences. This dissertation studies and examines different ideas using principal angles and subspaces concepts. It introduces a novel mathematical approach for comparing sets of EEG signals for use in new BCI technology. The success of the presented results show that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. In this application, the appearance of a subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Smoothing the signals before using them is the only preprocessing step that was implemented in this study. The smoothing process based on minimizing the second derivative in time is implemented to increase the classification accuracy instead of using the bandpass filter that relies on assumptions on the frequency content of EEG. This study examines four different ways of removing outliers that are based on the principal angles and shows that the outlier removal methods did not help in the presented situations. One of the concepts that this dissertation focused on is the effect of the number of trials on the classification accuracies. The achievement of the good classification results by using a small number of trials starting from two trials only

  13. Improved CSF classification and lesion detection in MR brain images with multiple sclerosis

    NASA Astrophysics Data System (ADS)

    Wolff, Yulian; Miron, Shmuel; Achiron, Anat; Greenspan, Hayit

    2007-03-01

    The study deals with the challenging task of automatic segmentation of MR brain images with multiple sclerosis lesions (MSL). Multi-Channel data is used, including "fast fluid attenuated inversion recovery" (fast FLAIR or FF), and statistical modeling tools are developed, in order to improve cerebrospinal fluid (CSF) classification and to detect MSL. Two new concepts are proposed for use within an EM framework. The first concept is the integration of prior knowledge as it relates to tissue behavior in different MRI modalities, with special attention given to the FF modality. The second concept deals with running the algorithm on a subset of the input that is most likely to be noise- and artifact-free data. This enables a more reliable learning of the Gaussian mixture model (GMM) parameters for brain tissue statistics. The proposed method focuses on the problematic CSF intensity distribution, which is a key to improved overall segmentation and lesion detection. A level-set based active contour stage is performed for lesion delineation, using gradient and shape properties combined with previously learned region intensity statistics. In the proposed scheme there is no need for preregistration of an atlas, a common characteristic in brain segmentation schemes. Experimental results on real data are presented.

  14. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme

    PubMed Central

    Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

    2016-01-01

    Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI. PMID:26880873

  15. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

    PubMed

    Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

    2016-01-01

    Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI. PMID:26880873

  16. Tumours of the upper alimentary tract.

    PubMed

    Head, K W

    1976-01-01

    Tumours of the oropharynx of domestic animals are common in most parts of the world, but squamous cell carcinoma of the upper alimentary tract shows differences in prevalence in different geographical areas and occurs at different sites in the various species. Oral tumours of the melanogenic system are more common in dogs than in man. The following main histological categories, which broadly correspond to those used in the classification of tumours of man, are described: papilloma; squamous cell carcinoma; salivary gland tumours; malignant melanoma; tumours of soft (mesenchymal) tissues; tumours of the facial bones; tumours of haematopoietic and related tissues; and odontogenic tumours and jaw cysts. Papilloma, squamous cell carcinoma, malignant melanoma, fibroma, and fibrosarcoma account for about 80% of the tumours that occur in the upper alimentary tract of domestic animals. PMID:1086147

  17. Supervised novelty detection in brain tissue classification with an application to white matter hyperintensities

    NASA Astrophysics Data System (ADS)

    Kuijf, Hugo J.; Moeskops, Pim; de Vos, Bob D.; Bouvy, Willem H.; de Bresser, Jeroen; Biessels, Geert Jan; Viergever, Max A.; Vincken, Koen L.

    2016-03-01

    Novelty detection is concerned with identifying test data that differs from the training data of a classifier. In the case of brain MR images, pathology or imaging artefacts are examples of untrained data. In this proof-of-principle study, we measure the behaviour of a classifier during the classification of trained labels (i.e. normal brain tissue). Next, we devise a measure that distinguishes normal classifier behaviour from abnormal behavior that occurs in the case of a novelty. This will be evaluated by training a kNN classifier on normal brain tissue, applying it to images with an untrained pathology (white matter hyperintensities (WMH)), and determine if our measure is able to identify abnormal classifier behaviour at WMH locations. For our kNN classifier, behaviour is modelled as the mean, median, or q1 distance to the k nearest points. Healthy tissue was trained on 15 images; classifier behaviour was trained/tested on 5 images with leave-one-out cross-validation. For each trained class, we measure the distribution of mean/median/q1 distances to the k nearest point. Next, for each test voxel, we compute its Z-score with respect to the measured distribution of its predicted label. We consider a Z-score >=4 abnormal behaviour of the classifier, having a probability due to chance of 0.000032. Our measure identified >90% of WMH volume and also highlighted other non-trained findings. The latter being predominantly vessels, cerebral falx, brain mask errors, choroid plexus. This measure is generalizable to other classifiers and might help in detecting unexpected findings or novelties by measuring classifier behaviour.

  18. Improving the classification of brain tumors in mice with perturbation enhanced (PE)-MRSI.

    PubMed

    Simões, Rui Vasco; Ortega-Martorell, Sandra; Delgado-Goñi, Teresa; Le Fur, Yann; Pumarola, Martí; Candiota, Ana Paula; Martín, Juana; Stoyanova, Radka; Cozzone, Patrick J; Julià-Sapé, Margarida; Arús, Carles

    2012-02-01

    Classifiers based on statistical pattern recognition analysis of MRSI data are becoming important tools for the non-invasive diagnosis of human brain tumors. Here we investigate the potential interest of perturbation-enhanced MRSI (PE-MRSI), in this case acute hyperglycemia, for improving the discrimination between mouse brain MRS patterns of glioblastoma multiforme (GBM), oligodendroglioma (ODG), and non-tumor brain parenchyma (NT). Six GBM-bearing mice and three ODG-bearing mice were scanned at 7 Tesla by PRESS-MRSI with 12 and 136 ms echo-time, during euglycemia (Eug) and also during induced acute hyperglycemia (Hyp), generating altogether four datasets per animal (echo time + glycemic condition): 12Eug, 136Eug, 12Hyp, and 136Hyp. For classifier development all spectral vectors (spv) selected from the MRSI matrix were unit length normalized (UL2) and used either as a training set (76 GBM spv, four mice; 70 ODG spv, two mice; 54 NT spv) or as an independent testing set (61 GBM spv, two mice; 31 ODG, one mouse; 23 NT spv). All Fisher's LDA classifiers obtained were evaluated as far as their descriptive performance-correctly classified cases of the training set (bootstrapping)-and predictive accuracy-balanced error rate of independent testing set classification. MRSI-based classifiers at 12Hyp were consistently more efficient in separating GBM, ODG, and NT regions, with overall accuracies always >80% and up to 95-96%; remaining classifiers were within the 48-85% range. This was also confirmed by user-independent selection of training and testing sets, using leave-one-out (LOO). This highlights the potential interest of perturbation-enhanced MRSI protocols for improving the non-invasive characterization of preclinical brain tumors. PMID:22193155

  19. Brain fingerprinting classification concealed information test detects US Navy military medical information with P300

    PubMed Central

    Farwell, Lawrence A.; Richardson, Drew C.; Richardson, Graham M.; Furedy, John J.

    2014-01-01

    A classification concealed information test (CIT) used the “brain fingerprinting” method of applying P300 event-related potential (ERP) in detecting information that is (1) acquired in real life and (2) unique to US Navy experts in military medicine. Military medicine experts and non-experts were asked to push buttons in response to three types of text stimuli. Targets contain known information relevant to military medicine, are identified to subjects as relevant, and require pushing one button. Subjects are told to push another button to all other stimuli. Probes contain concealed information relevant to military medicine, and are not identified to subjects. Irrelevants contain equally plausible, but incorrect/irrelevant information. Error rate was 0%. Median and mean statistical confidences for individual determinations were 99.9% with no indeterminates (results lacking sufficiently high statistical confidence to be classified). We compared error rate and statistical confidence for determinations of both information present and information absent produced by classification CIT (Is a probe ERP more similar to a target or to an irrelevant ERP?) vs. comparison CIT (Does a probe produce a larger ERP than an irrelevant?) using P300 plus the late negative component (LNP; together, P300-MERMER). Comparison CIT produced a significantly higher error rate (20%) and lower statistical confidences: mean 67%; information-absent mean was 28.9%, less than chance (50%). We compared analysis using P300 alone with the P300 + LNP. P300 alone produced the same 0% error rate but significantly lower statistical confidences. These findings add to the evidence that the brain fingerprinting methods as described here provide sufficient conditions to produce less than 1% error rate and greater than 95% median statistical confidence in a CIT on information obtained in the course of real life that is characteristic of individuals with specific training, expertise, or organizational

  20. Brain fingerprinting classification concealed information test detects US Navy military medical information with P300.

    PubMed

    Farwell, Lawrence A; Richardson, Drew C; Richardson, Graham M; Furedy, John J

    2014-01-01

    A classification concealed information test (CIT) used the "brain fingerprinting" method of applying P300 event-related potential (ERP) in detecting information that is (1) acquired in real life and (2) unique to US Navy experts in military medicine. Military medicine experts and non-experts were asked to push buttons in response to three types of text stimuli. Targets contain known information relevant to military medicine, are identified to subjects as relevant, and require pushing one button. Subjects are told to push another button to all other stimuli. Probes contain concealed information relevant to military medicine, and are not identified to subjects. Irrelevants contain equally plausible, but incorrect/irrelevant information. Error rate was 0%. Median and mean statistical confidences for individual determinations were 99.9% with no indeterminates (results lacking sufficiently high statistical confidence to be classified). We compared error rate and statistical confidence for determinations of both information present and information absent produced by classification CIT (Is a probe ERP more similar to a target or to an irrelevant ERP?) vs. comparison CIT (Does a probe produce a larger ERP than an irrelevant?) using P300 plus the late negative component (LNP; together, P300-MERMER). Comparison CIT produced a significantly higher error rate (20%) and lower statistical confidences: mean 67%; information-absent mean was 28.9%, less than chance (50%). We compared analysis using P300 alone with the P300 + LNP. P300 alone produced the same 0% error rate but significantly lower statistical confidences. These findings add to the evidence that the brain fingerprinting methods as described here provide sufficient conditions to produce less than 1% error rate and greater than 95% median statistical confidence in a CIT on information obtained in the course of real life that is characteristic of individuals with specific training, expertise, or organizational

  1. A framework to support automated classification and labeling of brain electromagnetic patterns.

    PubMed

    Frishkoff, Gwen A; Frank, Robert M; Rong, Jiawei; Dou, Dejing; Dien, Joseph; Halderman, Laura K

    2007-01-01

    This paper describes a framework for automated classification and labeling of patterns in electroencephalographic (EEG) and magnetoencephalographic (MEG) data. We describe recent progress on four goals: 1) specification of rules and concepts that capture expert knowledge of event-related potentials (ERP) patterns in visual word recognition; 2) implementation of rules in an automated data processing and labeling stream; 3) data mining techniques that lead to refinement of rules; and 4) iterative steps towards system evaluation and optimization. This process combines top-down, or knowledge-driven, methods with bottom-up, or data-driven, methods. As illustrated here, these methods are complementary and can lead to development of tools for pattern classification and labeling that are robust and conceptually transparent to researchers. The present application focuses on patterns in averaged EEG (ERP) data. We also describe efforts to extend our methods to represent patterns in MEG data, as well as EM patterns in source (anatomical) space. The broader aim of this work is to design an ontology-based system to support cross-laboratory, cross-paradigm, and cross-modal integration of brain functional data. Tools developed for this project are implemented in MATLAB and are freely available on request. PMID:18301711

  2. Mild traumatic brain injury literature review and proposed changes to classification.

    PubMed

    Krainin, Benjamin M; Forsten, Robert D; Kotwal, Russ S; Lutz, Robert H; Guskiewicz, Kevin M

    2011-01-01

    Mild traumatic brain injury (mTBI) reportedly occurs in 8-22% of U.S. servicemembers who conduct combat operations in Afghanistan and Iraq. The current definition for mTBI found in the medical literature, to include the Department of Defense (DoD) and Veterans Administration (VA) clinical practice guidelines is limited by the parameters of loss of consciousness, altered consciousness, or post-traumatic amnesia, and does not account for other constellations of potential symptoms. Although mTBI symptoms typically resolve within seven days, some servicemembers experience symptoms that continue for weeks, months, or years following an injury. Mild TBI is one of few disorders in medicine where a benign and misleading diagnostic classification is bestowed on patients at the time of injury, yet still can be associated with lifelong complications. This article comprehensively reviews the clinical literature over the past 20 years and proposes a new classification for TBI that addresses acute, sub-acute, and chronic phases, and includes neurocognitive, somatic, and psychological symptom presentation. PMID:22173595

  3. The Wechsler Adult Intelligence Scale-III and Malingering in Traumatic Brain Injury: Classification Accuracy in Known Groups

    ERIC Educational Resources Information Center

    Curtis, Kelly L.; Greve, Kevin W.; Bianchini, Kevin J.

    2009-01-01

    A known-groups design was used to determine the classification accuracy of Wechsler Adult Intelligence Scale-III (WAIS-III) variables in detecting malingered neurocognitive dysfunction (MND) in traumatic brain injury (TBI). TBI patients were classified into the following groups: (a) mild TBI not-MND (n = 26), (b) mild TBI MND (n = 31), and (c)…

  4. Animal models of tumour-associated epilepsy.

    PubMed

    Kirschstein, Timo; Köhling, Rüdiger

    2016-02-15

    Brain tumours cause a sizeable proportion of epilepsies in adulthood, and actually can be etiologically responsible also for childhood epilepsies. Conversely, seizures are often first clinical signs of a brain tumour. Nevertheless, several issues of brain-tumour associated seizures and epilepsies are far from understood, or clarified regarding clinical consensus. These include both the specific mechanisms of epileptogenesis related to different tumour types, the possible relationship between malignancy and seizure emergence, the interaction between tumour mass and surrounding neuronal networks, and - not least - the best treatment options depending on different tumour types. To investigate these issues, experimental models of tumour-induced epilepsies are necessary. This review concentrates on the description of currently used models, focusing on methodological aspects. It highlights advantages and shortcomings of these models, and identifies future experimental challenges. PMID:26092434

  5. Biophysical models of tumour growth

    NASA Astrophysics Data System (ADS)

    Tracqui, P.

    2009-05-01

    Tumour growth is a multifactorial process, which has stimulated in recent decades the development of numerous models trying to figure out the mechanisms controlling solid tumours morphogenesis. While the earliest models were focusing on cell proliferation kinetics, modulated by the availability of supplied nutrients, new modelling approaches emphasize the crucial role of several biophysical processes, including local matrix remodelling, active cell migration and traction, and reshaping of host tissue vasculature. After a brief presentation of this experimental background, this review will outline a number of representative models describing, at different scales, the growth of avascular and vascularized tumours. Special attention will be paid to the formulation of tumour-host tissue interactions that selectively drive changes in tumour size and morphology, and which are notably mediated by the mechanical status and elasticity of the tumour microenvironment. Emergence of invasive behaviour through growth instabilities at the tumour-host interface will be presented considering both reaction-diffusion and mechano-cellular models. In the latter part of the review, patient-oriented implications of tumour growth modelling are outlined in the context of brain tumours. Some conceptual views of the adaptive strategies and selective barriers that govern tumour evolution are presented in conclusion as potential guidelines for the development of future models.

  6. Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.

    PubMed

    Xu, Fangzhou; Zhou, Weidong; Zhen, Yilin; Yuan, Qi; Wu, Qi

    2016-09-01

    The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems. PMID:27255798

  7. Classification

    NASA Astrophysics Data System (ADS)

    Oza, Nikunj

    2012-03-01

    A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. A set of training examples— examples with known output values—is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate’s measurements. The generalization performance of a learned model (how closely the target outputs and the model’s predicted outputs agree for patterns that have not been presented to the learning algorithm) would provide an indication of how well the model has learned the desired mapping. More formally, a classification learning algorithm L takes a training set T as its input. The training set consists of |T| examples or instances. It is assumed that there is a probability distribution D from which all training examples are drawn independently—that is, all the training examples are independently and identically distributed (i.i.d.). The ith training example is of the form (x_i, y_i), where x_i is a vector of values of several features and y_i represents the class to be predicted.* In the sunspot classification example given above, each training example

  8. C1q-tumour necrosis factor-related protein 8 (CTRP8) is a novel interaction partner of relaxin receptor RXFP1 in human brain cancer cells.

    PubMed

    Glogowska, Aleksandra; Kunanuvat, Usakorn; Stetefeld, Jörg; Patel, Trushar R; Thanasupawat, Thatchawan; Krcek, Jerry; Weber, Ekkehard; Wong, G William; Del Bigio, Marc R; Hoang-Vu, Cuong; Hombach-Klonisch, Sabine; Klonisch, Thomas

    2013-12-01

    We report a novel ligand-receptor system composed of the leucine-rich G-protein-coupled relaxin receptor, RXFP1, and the C1q-tumour necrosis factor-related protein 8 (CTRP8) in human primary brain cancer, a tumour entity devoid of the classical RXFP1 ligands, RLN1-3. In structural homology studies and computational docking experiments we delineated the N-terminal region of the globular C1q region of CTRP8 and the leucine-rich repeat units 7 and 8 of RXFP1 to mediate this new ligand-receptor interaction. CTRP8 secreted from HEK293T cells, recombinant human (rh) CTRP8, and short synthetic peptides derived from the C1q globular domain of human CTRP8 caused the activation of RXFP1 as determined by elevated intracellular cAMP levels and the induction of a marked pro-migratory phenotype in established glioblastoma (GB) cell lines and primary cells from GB patients. Employing a small competitor peptide, we were able to disrupt the CTRP8-RXFP1-induced increased GB motility. The CTRP8-RXFP1-mediated migration in GB cells involves the activation of PI3K and specific protein kinase C pathways and the increased production/secretion of the potent lysosomal protease cathepsin B (cathB), a known prognostic marker of GB. Specific inhibition of CTRP8-induced cathB activity effectively blocked the ability of primary GB to invade laminin matrices. Finally, co-immunoprecipitation studies revealed the direct interaction of human CTRP8 with RXFP1. Our results support a therapeutic approach in GB aimed at targeting multiple steps of the CTRP8-RXFP1 signalling pathway by a combined inhibitor and peptide-based strategy to block GB dissemination within the brain. PMID:24014093

  9. Tumours of the thymus

    PubMed Central

    Sellors, T. Holmes; Thackray, A. C.; Thomson, A. D.

    1967-01-01

    Eighty-eight cases of thymoma are discussed with the object of trying to co-ordinate the histological and clinical features. The pathological specimens were in all cases obtained at operation. The pathology classification introduced by Thomson and Thackray in 1957 has been found to correspond adequately with the clinical pattern. The most common groups of tumours are basically epithelial and can be separated into five or six subdivisions, each of which has a separate pattern of behaviour. Lymphoid and teratomatous tumours also occur, but there were only two examples in this series. Clinically, separation of patients who suffered from myasthenia (38) and those who did not (50) affords the first main grouping. The majority of patients who had myasthenia gravis had tumours classified as epidermoid (19) and lymphoepithelial (14), the former with a more malignant appearance and behaviour than the latter. Removal of the tumour with or without radiation gave considerable and sometimes complete relief from myasthenic symptoms. Non-myasthenic thymoma (50) was usually discovered as a result of pressure signs or in the course of routine radiography. Spindle or oval celled tumours followed a benign pattern whereas undifferentiated thymoma was in every sense malignant, as also were teratomatous growths. Granulomatous or Hodgkin-like thymomas were of special interest and had an unpredictable course, some patients surviving many years after what was regarded as inadequate treatment. The place of radiotherapy as a pre- or post-operative agent complementary to surgery is discussed. Images PMID:6033387

  10. MRI Brain Images Healthy and Pathological Tissues Classification with the Aid of Improved Particle Swarm Optimization and Neural Network

    PubMed Central

    Sheejakumari, V.; Sankara Gomathi, B.

    2015-01-01

    The advantages of magnetic resonance imaging (MRI) over other diagnostic imaging modalities are its higher spatial resolution and its better discrimination of soft tissue. In the previous tissues classification method, the healthy and pathological tissues are classified from the MRI brain images using HGANN. But the method lacks sensitivity and accuracy measures. The classification method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new classification method is proposed in this paper. Here, new tissues classification method is proposed with improved particle swarm optimization (IPSO) technique to classify the healthy and pathological tissues from the given MRI images. Our proposed classification method includes the same four stages, namely, tissue segmentation, feature extraction, heuristic feature selection, and tissue classification. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of the proposed classification method in classifying the tissues and the achieved improvement in sensitivity and accuracy measures. Furthermore, the performance of the proposed technique is evaluated by comparing it with the other segmentation methods. PMID:25977706

  11. Impact of brain tumour location on emotion and personality: a voxel-based lesion-symptom mapping study on mentalization processes.

    PubMed

    Campanella, Fabio; Shallice, Tim; Ius, Tamara; Fabbro, Franco; Skrap, Miran

    2014-09-01

    Patients affected by brain tumours may show behavioural and emotional regulation deficits, sometimes showing flattened affect and sometimes experiencing a true 'change' in personality. However, little evidence is available to the surgeon as to what changes are likely to occur with damage at specific sites, as previous studies have either relied on single cases or provided only limited anatomical specificity, mostly reporting associations rather than dissociations of symptoms. We investigated these aspects in patients undergoing surgery for the removal of cerebral tumours. We argued that many of the problems described can be ascribed to the onset of difficulties in one or more of the different levels of the process of mentalizing (i.e. abstracting and reflecting upon) emotion and intentions, which impacts on everyday behaviour. These were investigated in terms of (i) emotion recognition; (ii) Theory of Mind; (iii) alexithymia; and (iv) self-maturity (personality disorder). We hypothesized that temporo/limbic areas would be critical for processing emotion and intentions at a more perceptual level, while frontal lobe structures would be more critical when higher levels of mentalization/abstraction are required. We administered four different tasks, Task 1: emotion recognition of Ekman faces; Task 2: the Eyes Test (Theory of Mind); Task 3: Toronto Alexithymia Scale; and Task 4: Temperament and Character Inventory (a personality inventory), both immediately before and few days after the operation for the removal of brain tumours in a series of 71 patients (age range: 18-75 years; 33 female) with lesions located in the left or right frontal, temporal and parietal lobes. Lobe-based and voxel-based analysis confirmed that tasks requiring interpretation of emotions and intentions at more basic (less mentalized) levels (Tasks 1 and 2) were more affected by temporo/insular lesions, with emotion recognition (Task 1) being maximally impaired by anterior temporal and amygdala

  12. Classification

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj C.

    2011-01-01

    A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. In supervised learning, a set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. This chapter discusses methods to perform machine learning, with examples involving astronomy.

  13. Diagnosis, classification and grading of canine mammary tumours as a model to study human breast cancer: an Clinico-Cytohistopathological study with environmental factors influencing public health and medicine

    PubMed Central

    2013-01-01

    Background The human “Elston and Ellis grading method” was utilized in dogs with mammary tumor to examine its relation to prognosis in this species, based on a 2-year follow-up period. Although cytopathology is widely used for early diagnosis of human neoplasms, it is not commonly performed in veterinary medicine. Our objectives in this study were to identify cytopathology criteria of malignancy for canine mammary tumors and the frequency of different types of mammary lesions and their relationship with histologic grade was investigated. Another aim of this study was to differentiate the simple and adenocarcinoma tumors from the complex or mixed tumor described by Elston and Ellis grading method. Methods The study was performed in 15 pure or mixed-breed female dogs submitted to surgical resections of mammary tumours. The mammary tumours were excised by simple mastectomy or regional mastectomy, with or without the superficial inguinal lymph nodes. Female dogs were mainly terriers (9 dogs) or mixed (3 dogs), the 3 other animals were a German shepherd, Dachshund and Pekingese. Before surgical excision of the tumour, FNAC was performed using a 0.6 mm diameter needle attached to a 10 ml syringe held in a standard metal syringe holder. The cytological sample was smeared onto a glass slide and either air-dried for May-Grünwald-stain, or ethanol-fixed for Papanicolaou stain and masses were surgically removed, the tumours were grossly examined and tissue samples were fixed in 10%-buffered-formalin and embedded in paraffin. Sections 4 μm thick were obtained from each sample and H&E stained. Results We obtained a correct cytohistological correlation in 14/15 cases (93.3%) when all cytopathological examinations were considered. Of the 15 cases examined, 2(13.3%) had well-differentiated (grade I), 6(40%) had moderately differentiated (grade II) and 7(46.7%) had poorly differentiated (grade III) tumours. Classification of all canine mammary gland lesions revealed 13

  14. Neural network classification of autoregressive features from electroencephalogram signals for brain computer interface design

    NASA Astrophysics Data System (ADS)

    Huan, Nai-Jen; Palaniappan, Ramaswamy

    2004-09-01

    In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.

  15. A systematic review of functional magnetic resonance imaging and diffusion tensor imaging modalities used in presurgical planning of brain tumour resection.

    PubMed

    Dimou, S; Battisti, R A; Hermens, D F; Lagopoulos, J

    2013-04-01

    Historically, brain tumour resection has relied upon standardised anatomical atlases and classical mapping techniques for successful resection. While these have provided adequate results in the past, the emergence of new technologies has heralded a wave of less invasive, patient-specific techniques for the mapping of brain function. Functional magnetic resonance imaging (fMRI) and, more recently, diffusion tensor imaging (DTI) are two such techniques. While fMRI is able to highlight localisation of function within the cortex, DTI represents the only technique able to elucidate white matter structures in vivo. Used in conjunction, both of these techniques provide important presurgical information for thorough preoperative planning, as well as intraoperatively via integration into frameless stereotactic neuronavigational systems. Together, these techniques show great promise for improved neurosurgical outcomes. While further research is required for more widespread clinical validity and acceptance, results from the literature provide a clear road map for future research and development to cement these techniques into the clinical setup of neurosurgical departments globally. PMID:23187966

  16. Segmentation and classification of normal-appearing brain: how much is enough?

    NASA Astrophysics Data System (ADS)

    Glass, John O.; Reddick, Wilburn E.; Ji, Qing; Glas, Lauren S.

    2002-05-01

    In this study, subsets of MR slices were examined to assess their ability to optimally predict the total cerebral volume of gray matter, white matter and CSF. Patients underwent a clinical imaging protocol consisting of T1-, T2-, PD-, and FLAIR-weighted images after obtaining informed consent. MR imaging sets were registered, RF-corrected, and then analyzed with a hybrid neural network segmentation and classification algorithm to identify normal brain parenchyma. After processing the data, the correlation between the image subsets and the total cerebral volumes of gray matter, white matter and CSF were examined. The 29 subjects (18F, 11M) assessed in this study were 1.7 ? 18.7 (median = 5.2) years of age. The five subsets accounted for 5%, 15%, 24%, 56%, and 79% of the total cerebral volume. The predictive correlation for gray matter, white matter, and CSF in each of these subsets were: 5% (R= 0.94, 0.92, 0.91), 15% (R= 0.93, 0.95, 0.94), 24% (R= 0.92, 0.95, 0.94), 56% (R= 0.75, 0.95, 0.89), and 79% (R= 0.89, 0.98, 0.99) respectively. All subsets of slices examined were significantly correlated (p<0.001) with the total cerebral volume of gray matter, white matter, and CSF.

  17. Brain functional connectivity patterns for emotional state classification in Parkinson's disease patients without dementia.

    PubMed

    Yuvaraj, R; Murugappan, M; Acharya, U Rajendra; Adeli, Hojjat; Ibrahim, Norlinah Mohamed; Mesquita, Edgar

    2016-02-01

    Successful emotional communication is crucial for social interactions and social relationships. Parkinson's Disease (PD) patients have shown deficits in emotional recognition abilities although the research findings are inconclusive. This paper presents an investigation of six emotions (happiness, sadness, fear, anger, surprise, and disgust) of twenty non-demented (Mini-Mental State Examination score >24) PD patients and twenty Healthy Controls (HCs) using Electroencephalogram (EEG)-based Brain Functional Connectivity (BFC) patterns. The functional connectivity index feature in EEG signals is computed using three different methods: Correlation (COR), Coherence (COH), and Phase Synchronization Index (PSI). Further, a new functional connectivity index feature is proposed using bispectral analysis. The experimental results indicate that the BFC change is significantly different among emotional states of PD patients compared with HC. Also, the emotional connectivity pattern classified using Support Vector Machine (SVM) classifier yielded the highest accuracy for the new bispectral functional connectivity index. The PD patients showed emotional impairments as demonstrated by a poor classification performance. This finding suggests that decrease in the functional connectivity indices during emotional stimulation in PD, indicating functional disconnections between cortical areas. PMID:26515932

  18. Automatic classification of sulcal regions of the human brain cortex using pattern recognition

    NASA Astrophysics Data System (ADS)

    Behnke, Kirsten J.; Rettmann, Maryam E.; Pham, Dzung L.; Shen, Dinggang; Resnick, Susan M.; Davatzikos, Christos; Prince, Jerry L.

    2003-05-01

    Parcellation of the cortex has received a great deal of attention in magnetic resonance (MR) image analysis, but its usefulness has been limited by time-consuming algorithms that require manual labeling. An automatic labeling scheme is necessary to accurately and consistently parcellate a large number of brains. The large variation of cortical folding patterns makes automatic labeling a challenging problem, which cannot be solved by deformable atlas registration alone. In this work, an automated classification scheme that consists of a mix of both atlas driven and data driven methods is proposed to label the sulcal regions, which are defined as the gray matter regions of the cortical surface surrounding each sulcus. The premise for this algorithm is that sulcal regions can be classified according to the pattern of anatomical features (e.g. supramarginal gyrus, cuneus, etc.) associated with each region. Using a nearest-neighbor approach, a sulcal region is classified as being in the same class as the sulcus from a set of training data which has the nearest pattern of anatomical features. Using just one subject as training data, the algorithm correctly labeled 83% of the regions that make up the main sulci of the cortex.

  19. Automated Classification to Predict the Progression of Alzheimer's Disease Using Whole-Brain Volumetry and DTI

    PubMed Central

    Jung, Won Beom; Lee, Young Min; Kim, Young Hoon

    2015-01-01

    Objective This study proposes an automated diagnostic method to classify patients with Alzheimer's disease (AD) of degenerative etiology using magnetic resonance imaging (MRI) markers. Methods Twenty-seven patients with subjective memory impairment (SMI), 18 patients with mild cognitive impairment (MCI), and 27 patients with AD participated. MRI protocols included three dimensional brain structural imaging and diffusion tensor imaging to assess the cortical thickness, subcortical volume and white matter integrity. Recursive feature elimination based on support vector machine (SVM) was conducted to determine the most relevant features for classifying abnormal regions and imaging parameters, and then a factor analysis for the top-ranked factors was performed. Subjects were classified using nonlinear SVM. Results Medial temporal regions in AD patients were dominantly detected with cortical thinning and volume atrophy compared with SMI and MCI patients. Damage to white matter integrity was also accredited with decreased fractional anisotropy and increased mean diffusivity (MD) across the three groups. The microscopic damage in the subcortical gray matter was reflected in increased MD. Classification accuracy between pairs of groups (SMI vs. MCI, MCI vs. AD, SMI vs. AD) and among all three groups were 84.4% (±13.8), 86.9% (±10.5), 96.3% (±4.6), and 70.5% (±11.5), respectively. Conclusion This proposed method may be a potential tool to diagnose AD pathology with the current clinical criteria. PMID:25670951

  20. Tumours of the liver and biliary system

    PubMed Central

    Ponomarkov, V.; Mackey, L. J.

    1976-01-01

    In this histological classification of liver and gall bladder tumours the tumour types largely correspond to those found in man. The most common tumours in this group are liver cell adenoma, hepatocellular carcinoma, and cholangiocarcinoma. ImagesFig. 5Fig. 6Fig. 7Fig. 8Fig. 13Fig. 14Fig. 1Fig. 2Fig. 3Fig. 4Fig. 9Fig. 10Fig. 11Fig. 12 PMID:1086149

  1. Aggregation of sparse linear discriminant analyses for event-related potential classification in brain-computer interface.

    PubMed

    Zhang, Yu; Zhou, Guoxu; Jin, Jing; Zhao, Qibin; Wang, Xingyu; Cichocki, Andrzej

    2014-02-01

    Two main issues for event-related potential (ERP) classification in brain-computer interface (BCI) application are curse-of-dimensionality and bias-variance tradeoff, which may deteriorate classification performance, especially with insufficient training samples resulted from limited calibration time. This study introduces an aggregation of sparse linear discriminant analyses (ASLDA) to overcome these problems. In the ASLDA, multiple sparse discriminant vectors are learned from differently l1-regularized least-squares regressions by exploiting the equivalence between LDA and least-squares regression, and are subsequently aggregated to form an ensemble classifier, which could not only implement automatic feature selection for dimensionality reduction to alleviate curse-of-dimensionality, but also decrease the variance to improve generalization capacity for new test samples. Extensive investigation and comparison are carried out among the ASLDA, the ordinary LDA and other competing ERP classification algorithms, based on different three ERP datasets. Experimental results indicate that the ASLDA yields better overall performance for single-trial ERP classification when insufficient training samples are available. This suggests the proposed ASLDA is promising for ERP classification in small sample size scenario to improve the practicability of BCI. PMID:24344691

  2. Subdural Pressure and Brain Condition During Propofol Vs Isoflurane - Nitrous Oxide Anaesthesia in Patients Undergoing Elective Supratentorial Tumour Surgery

    PubMed Central

    Santra, Sankari; Das, Bibhukalyani

    2009-01-01

    Summary Total intravenous anaesthesia has received much importance than inhalational anaesthesia in neuroanaesthetic practice. In an effort to determine whether any important clinical differences occur, studies concerning intracranial pressure (ICP), degree of dural tension and degree of brain swelling during intravenous and inhalational based anaesthesia are warranted like the present one. A total of 68 patients were assigned randomly to one of two groups. In Group-I(n=34), anaesthesia was induced with propofol (1-3mg.kg−1) and maintained with propofol (6-10mg.kg−1.hr−1) and fentanyl (2-3mcg.kg−1.hr−1). In Group-II (n=34), anaesthesia was induced with propofol (1-3mg.kg−1) but maintained with isoflurane, nitrous oxide and fentanyl (2-3mcg.kg−1.hr−1). Moderate hypocapnia was applied to maintain arterial carbon dioxide around 30mmHg. Mean arterial blood pressure was stabilized with phenylephrine whenever necessary. Subdural intracranial pressure, mean arterial pressure, cerebral perfusion pressure were monitored before and after 10min period of hyperventilation. Furthermore, the tension of dura before and after of hyperventilation and the degree of brain swelling after opening of dura were also estimated by the neurosurgeon. No differences were found between the groups with regards to demographics, neuroradiologic diagnosis, position of head and time of ICP measurement. Before hyperventilation, both ICP and dural tension were significantly lower in Group I compared with Group-II (P<0.05). But after hyperventilation there was no significant difference of ICP and dural tension in between groups. The degree of brain swelling after opening of dura was similar in both groups. There was a positive correlation between measured ICP and brain swelling score. PMID:20640077

  3. Matched signal detection on graphs: Theory and application to brain imaging data classification.

    PubMed

    Hu, Chenhui; Sepulcre, Jorge; Johnson, Keith A; Fakhri, Georges E; Lu, Yue M; Li, Quanzheng

    2016-01-15

    Motivated by recent progress in signal processing on graphs, we have developed a matched signal detection (MSD) theory for signals with intrinsic structures described by weighted graphs. First, we regard graph Laplacian eigenvalues as frequencies of graph-signals and assume that the signal is in a subspace spanned by the first few graph Laplacian eigenvectors associated with lower eigenvalues. The conventional matched subspace detector can be applied to this case. Furthermore, we study signals that may not merely live in a subspace. Concretely, we consider signals with bounded variation on graphs and more general signals that are randomly drawn from a prior distribution. For bounded variation signals, the test is a weighted energy detector. For the random signals, the test statistic is the difference of signal variations on associated graphs, if a degenerate Gaussian distribution specified by the graph Laplacian is adopted. We evaluate the effectiveness of the MSD on graphs both with simulated and real data sets. Specifically, we apply MSD to the brain imaging data classification problem of Alzheimer's disease (AD) based on two independent data sets: 1) positron emission tomography data with Pittsburgh compound-B tracer of 30 AD and 40 normal control (NC) subjects, and 2) resting-state functional magnetic resonance imaging (R-fMRI) data of 30 early mild cognitive impairment and 20 NC subjects. Our results demonstrate that the MSD approach is able to outperform the traditional methods and help detect AD at an early stage, probably due to the success of exploiting the manifold structure of the data. PMID:26481679

  4. Improving brain-computer interface classification using adaptive common spatial patterns.

    PubMed

    Song, Xiaomu; Yoon, Suk-Chung

    2015-06-01

    Common Spatial Patterns (CSP) is a widely used spatial filtering technique for electroencephalography (EEG)-based brain-computer interface (BCI). It is a two-class supervised technique that needs subject-specific training data. Due to EEG nonstationarity, EEG signal may exhibit significant intra- and inter-subject variation. As a result, spatial filters learned from a subject may not perform well for data acquired from the same subject at a different time or from other subjects performing the same task. Studies have been performed to improve CSP's performance by adding regularization terms into the training. Most of them require target subjects' training data with known class labels. In this work, an adaptive CSP (ACSP) method is proposed to analyze single trial EEG data from single and multiple subjects. The method does not estimate target data's class labels during the adaptive learning and updates spatial filters for both classes simultaneously. The proposed method was evaluated based on a comparison study with the classic CSP and several CSP-based adaptive methods using motor imagery EEG data from BCI competitions. Experimental results indicate that the proposed method can improve the classification performance as compared to the other methods. For circumstances where true class labels of target data are not instantly available, it was examined if adding classified target data to training data would improve the ACSP learning. Experimental results show that it would be better to exclude them from the training data. The proposed ACSP method can be performed in real-time and is potentially applicable to various EEG-based BCI applications. PMID:25909828

  5. Irradiation characteristics of BNCT using near-threshold 7Li(p, n)7Be direct neutrons: application to intra-operative BNCT for malignant brain tumours.

    PubMed

    Tanaka, Kenichi; Kobayashi, Tooru; Sakurai, Yoshinori; Nakagawa, Yoshinobu; Ishikawa, Masayori; Hoshi, Masaharu

    2002-08-21

    A calculation method for the dosage of neutrons by near-threshold 7Li(p, n)7Be and gamma rays by 7Li(p, p'gamma)7Li was validated through experiments with variable distance between the Li target and the phantom, focusing on large angular dependence. The production of neutrons and gamma rays in the Li target was calculated by Lee's method and their transport in the phantom was calculated using the MCNP-4B code. The dosage in intra-operative boron neutron capture therapy (BNCT) using near-threshold 7Li(p, n)7Be direct neutrons was evaluated using the validated calculation method. The effectiveness of the usage of the direct neutrons was confirmed from the existence of the region satisfying the requirements of the protocol utilized in intra-operative BNCT for brain tumours in Japan. The boron-dose enhancer (BDE) introduced in this paper to increase the contribution of the 10B(n, alpha)7Li dose in the living body was effective. The void utilized to increase the dose in deep regions was also effective with BDE. For the investigation of 1.900 MeV proton beams, for example, it was found that intraoperative BNCT using near-threshold 7Li(p, n)7Be direct neutrons is feasible. PMID:12222863

  6. Myoepithelial cells in canine mammary tumours.

    PubMed

    Sánchez-Céspedes, Raquel; Millán, Yolanda; Guil-Luna, Silvia; Reymundo, Carlos; Espinosa de Los Monteros, Antonio; Martín de Las Mulas, Juana

    2016-01-01

    Mammary tumours are the most common neoplasms of female dogs. Compared to mammary tumours of humans and cats, myoepithelial (ME) cell involvement is common in canine mammary tumours (CMT) of any subtype. Since ME cell involvement in CMT influences both histogenetic tumour classification and prognosis, correct identification of ME cells is important. This review describes immunohistochemical methods for identification of canine mammary ME cells used in vivo. In addition, phenotypic and genotypic methods to isolate ME cells for in vitro studies to analyse tumour-suppressor protein production and gene expression are discussed. The contribution of ME cells to both histogenetic classifications and the prognosis of CMT is compared with other species and the potential use of ME cells as a method to identify carcinoma in situ is discussed. PMID:26639832

  7. Supervised classification of brain tissues through local multi-scale texture analysis by coupling DIR and FLAIR MR sequences

    NASA Astrophysics Data System (ADS)

    Poletti, Enea; Veronese, Elisa; Calabrese, Massimiliano; Bertoldo, Alessandra; Grisan, Enrico

    2012-02-01

    The automatic segmentation of brain tissues in magnetic resonance (MR) is usually performed on T1-weighted images, due to their high spatial resolution. T1w sequence, however, has some major downsides when brain lesions are present: the altered appearance of diseased tissues causes errors in tissues classification. In order to overcome these drawbacks, we employed two different MR sequences: fluid attenuated inversion recovery (FLAIR) and double inversion recovery (DIR). The former highlights both gray matter (GM) and white matter (WM), the latter highlights GM alone. We propose here a supervised classification scheme that does not require any anatomical a priori information to identify the 3 classes, "GM", "WM", and "background". Features are extracted by means of a local multi-scale texture analysis, computed for each pixel of the DIR and FLAIR sequences. The 9 textures considered are average, standard deviation, kurtosis, entropy, contrast, correlation, energy, homogeneity, and skewness, evaluated on a neighborhood of 3x3, 5x5, and 7x7 pixels. Hence, the total number of features associated to a pixel is 56 (9 textures x3 scales x2 sequences +2 original pixel values). The classifier employed is a Support Vector Machine with Radial Basis Function as kernel. From each of the 4 brain volumes evaluated, a DIR and a FLAIR slice have been selected and manually segmented by 2 expert neurologists, providing 1st and 2nd human reference observations which agree with an average accuracy of 99.03%. SVM performances have been assessed with a 4-fold cross-validation, yielding an average classification accuracy of 98.79%.

  8. O6-Methylguanine-DNA methyltransferase protein expression by immunohistochemistry in brain and non-brain systemic tumours: systematic review and meta-analysis of correlation with methylation-specific polymerase chain reaction

    PubMed Central

    2011-01-01

    Background The DNA repair protein O6-Methylguanine-DNA methyltransferase (MGMT) confers resistance to alkylating agents. Several methods have been applied to its analysis, with methylation-specific polymerase chain reaction (MSP) the most commonly used for promoter methylation study, while immunohistochemistry (IHC) has become the most frequently used for the detection of MGMT protein expression. Agreement on the best and most reliable technique for evaluating MGMT status remains unsettled. The aim of this study was to perform a systematic review and meta-analysis of the correlation between IHC and MSP. Methods A computer-aided search of MEDLINE (1950-October 2009), EBSCO (1966-October 2009) and EMBASE (1974-October 2009) was performed for relevant publications. Studies meeting inclusion criteria were those comparing MGMT protein expression by IHC with MGMT promoter methylation by MSP in the same cohort of patients. Methodological quality was assessed by using the QUADAS and STARD instruments. Previously published guidelines were followed for meta-analysis performance. Results Of 254 studies identified as eligible for full-text review, 52 (20.5%) met the inclusion criteria. The review showed that results of MGMT protein expression by IHC are not in close agreement with those obtained with MSP. Moreover, type of tumour (primary brain tumour vs others) was an independent covariate of accuracy estimates in the meta-regression analysis beyond the cut-off value. Conclusions Protein expression assessed by IHC alone fails to reflect the promoter methylation status of MGMT. Thus, in attempts at clinical diagnosis the two methods seem to select different groups of patients and should not be used interchangeably. PMID:21269507

  9. Individual 3D region-of-interest atlas of the human brain: automatic training point extraction for neural-network-based classification of brain tissue types

    NASA Astrophysics Data System (ADS)

    Wagenknecht, Gudrun; Kaiser, Hans-Juergen; Obladen, Thorsten; Sabri, Osama; Buell, Udalrich

    2000-04-01

    Individual region-of-interest atlas extraction consists of two main parts: T1-weighted MRI grayscale images are classified into brain tissues types (gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), scalp/bone (SB), background (BG)), followed by class image analysis to define automatically meaningful ROIs (e.g., cerebellum, cerebral lobes, etc.). The purpose of this algorithm is the automatic detection of training points for neural network-based classification of brain tissue types. One transaxial slice of the patient data set is analyzed. Background separation is done by simple region growing. A random generator extracts spatially uniformly distributed training points of class BG from that region. For WM training point extraction (TPE), the homogeneity operator is the most important. The most homogeneous voxels define the region for WM TPE. They are extracted by analyzing the cumulative histogram of the homogeneity operator response. Assuming a Gaussian gray value distribution in WM, a random number is used as a probabilistic threshold for TPE. Similarly, non-white matter and non-background regions are analyzed for GM and CSF training points. For SB TPE, the distance from the BG region is an additional feature. Simulated and real 3D MRI images are analyzed and error rates for TPE and classification calculated.

  10. Toward FRP-Based Brain-Machine Interfaces-Single-Trial Classification of Fixation-Related Potentials.

    PubMed

    Finke, Andrea; Essig, Kai; Marchioro, Giuseppe; Ritter, Helge

    2016-01-01

    The co-registration of eye tracking and electroencephalography provides a holistic measure of ongoing cognitive processes. Recently, fixation-related potentials have been introduced to quantify the neural activity in such bi-modal recordings. Fixation-related potentials are time-locked to fixation onsets, just like event-related potentials are locked to stimulus onsets. Compared to existing electroencephalography-based brain-machine interfaces that depend on visual stimuli, fixation-related potentials have the advantages that they can be used in free, unconstrained viewing conditions and can also be classified on a single-trial level. Thus, fixation-related potentials have the potential to allow for conceptually different brain-machine interfaces that directly interpret cortical activity related to the visual processing of specific objects. However, existing research has investigated fixation-related potentials only with very restricted and highly unnatural stimuli in simple search tasks while participant's body movements were restricted. We present a study where we relieved many of these restrictions while retaining some control by using a gaze-contingent visual search task. In our study, participants had to find a target object out of 12 complex and everyday objects presented on a screen while the electrical activity of the brain and eye movements were recorded simultaneously. Our results show that our proposed method for the classification of fixation-related potentials can clearly discriminate between fixations on relevant, non-relevant and background areas. Furthermore, we show that our classification approach generalizes not only to different test sets from the same participant, but also across participants. These results promise to open novel avenues for exploiting fixation-related potentials in electroencephalography-based brain-machine interfaces and thus providing a novel means for intuitive human-machine interaction. PMID:26812487

  11. Toward FRP-Based Brain-Machine Interfaces—Single-Trial Classification of Fixation-Related Potentials

    PubMed Central

    Finke, Andrea; Essig, Kai; Marchioro, Giuseppe; Ritter, Helge

    2016-01-01

    The co-registration of eye tracking and electroencephalography provides a holistic measure of ongoing cognitive processes. Recently, fixation-related potentials have been introduced to quantify the neural activity in such bi-modal recordings. Fixation-related potentials are time-locked to fixation onsets, just like event-related potentials are locked to stimulus onsets. Compared to existing electroencephalography-based brain-machine interfaces that depend on visual stimuli, fixation-related potentials have the advantages that they can be used in free, unconstrained viewing conditions and can also be classified on a single-trial level. Thus, fixation-related potentials have the potential to allow for conceptually different brain-machine interfaces that directly interpret cortical activity related to the visual processing of specific objects. However, existing research has investigated fixation-related potentials only with very restricted and highly unnatural stimuli in simple search tasks while participant’s body movements were restricted. We present a study where we relieved many of these restrictions while retaining some control by using a gaze-contingent visual search task. In our study, participants had to find a target object out of 12 complex and everyday objects presented on a screen while the electrical activity of the brain and eye movements were recorded simultaneously. Our results show that our proposed method for the classification of fixation-related potentials can clearly discriminate between fixations on relevant, non-relevant and background areas. Furthermore, we show that our classification approach generalizes not only to different test sets from the same participant, but also across participants. These results promise to open novel avenues for exploiting fixation-related potentials in electroencephalography-based brain-machine interfaces and thus providing a novel means for intuitive human-machine interaction. PMID:26812487

  12. Description and classification of normal and pathological aging processes based on brain magnetic resonance imaging morphology measures.

    PubMed

    Perez-Gonzalez, Jorge Luis; Yanez-Suarez, Oscar; Bribiesca, Ernesto; Cosío, Fernando Arámbula; Jiménez, Juan Ramón; Medina-Bañuelos, Veronica

    2014-10-01

    We present a discrete compactness (DC) index, together with a classification scheme, based both on the size and shape features extracted from brain volumes, to determine different aging stages: healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). A set of 30 brain magnetic resonance imaging (MRI) volumes for each group was segmented and two indices were measured for several structures: three-dimensional DC and normalized volumes (NVs). The discrimination power of these indices was determined by means of the area under the curve (AUC) of the receiver operating characteristic, where the proposed compactness index showed an average AUC of 0.7 for HC versus MCI comparison, 0.9 for HC versus AD separation, and 0.75 for MCI versus AD groups. In all cases, this index outperformed the discrimination capability of the NV. Using selected features from the set of DC and NV measures, three support vector machines were optimized and validated for the pairwise separation of the three classes. Our analysis shows classification rates of up to 98.3% between HC and AD, 85% between HC and MCI, and 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indices to classify different aging stages. PMID:26158061

  13. Description and classification of normal and pathological aging processes based on brain magnetic resonance imaging morphology measures

    PubMed Central

    Perez-Gonzalez, Jorge Luis; Yanez-Suarez, Oscar; Bribiesca, Ernesto; Cosío, Fernando Arámbula; Jiménez, Juan Ramón; Medina-Bañuelos, Veronica

    2014-01-01

    Abstract. We present a discrete compactness (DC) index, together with a classification scheme, based both on the size and shape features extracted from brain volumes, to determine different aging stages: healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD). A set of 30 brain magnetic resonance imaging (MRI) volumes for each group was segmented and two indices were measured for several structures: three-dimensional DC and normalized volumes (NVs). The discrimination power of these indices was determined by means of the area under the curve (AUC) of the receiver operating characteristic, where the proposed compactness index showed an average AUC of 0.7 for HC versus MCI comparison, 0.9 for HC versus AD separation, and 0.75 for MCI versus AD groups. In all cases, this index outperformed the discrimination capability of the NV. Using selected features from the set of DC and NV measures, three support vector machines were optimized and validated for the pairwise separation of the three classes. Our analysis shows classification rates of up to 98.3% between HC and AD, 85% between HC and MCI, and 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indices to classify different aging stages. PMID:26158061

  14. A composite malignant tumour of the elderly female breast

    PubMed Central

    Wayte, D. M.; Stewart, J. B.; McKenzie, C. G.

    1970-01-01

    A composite malignant tumour arising in the breast of an elderly woman is described. The cystic tumour containing areas of squamous metaplasia, bone formation, adenocarcinoma, and osteosarcoma was surrounded by the typical changes of mammary dysplasia (fibroadenosis). The classification and acceptance of such tumours is highly debatable. There is no one acceptable classification of breast sarcomas and hence the prognosis of such neoplasms, particularly those containing heterologous tissues, is poorly defined. Evidence is presented in support of such composite tumours as being definite entities which arise from the closely associated epithelial and mesenchymal components of the breast simultaneously. Images PMID:4320045

  15. Tumours of the upper alimentary tract

    PubMed Central

    Head, K. W.

    1976-01-01

    Tumours of the oropharynx of domestic animals are common in most parts of the world, but squamous cell carcinoma of the upper alimentary tract shows differences in prevalence in different geographical areas and occurs at different sites in the various species. Oral tumours of the melanogenic system are more common in dogs than in man. The following main histological categories, which broadly correspond to those used in the classification of tumours of man, are described: papilloma; squamous cell carcinoma; salivary gland tumours; malignant melanoma; tumours of soft (mesenchymal) tissues; tumours of the facial bones; tumours of haematopoietic and related tissues; and odontogenic tumours and jaw cysts. Papilloma, squamous cell carcinoma, malignant melanoma, fibroma, and fibrosarcoma account for about 80% of the tumours that occur in the upper alimentary tract of domestic animals. ImagesFig. 6Fig. 7Fig. 8Fig. 9Fig. 34Fig. 35Fig. 36Fig. 37Fig. 2Fig. 3Fig. 4Fig. 5Fig. 22Fig. 23Fig. 24Fig. 25Fig. 26Fig. 27Fig. 28Fig. 29Fig. 14Fig. 15Fig. 16Fig. 17Fig. 30Fig. 31Fig. 32Fig. 33Fig. 18Fig. 19Fig. 20Fig. 21Fig. 10Fig. 11Fig. 12Fig. 13Fig. 1 PMID:1086147

  16. [Perioperative management of intracranial tumours: the neurosurgeon's role].

    PubMed

    Polo-Torres, C; Moscote-Salazar, L R; Alvis-Miranda, H R; Villa-Delgado, R

    2013-01-01

    The perioperative management of patients with brain tumours is a challenge for the neurosurgeon and the entire surgical team. The treating physician should consider factors such as the type of tumour, extent of disease, treatment received, the presence of comorbidities and prognosis of the disease itself. The successful execution of all aspects involved in perioperative management in patients with brain tumours will help prolong the life and improve the quality of life of patients. PMID:24008533

  17. Using kinetic parameter analysis of dynamic FDOPA-PET for brain tissue classification

    NASA Astrophysics Data System (ADS)

    Lin, Hong-Dun; Lin, Kang-Ping; Chung, Being-Tau; Yu, Chin-Lung; Wang, Rong-Fa; Wu, Liang-Chi; Liu, Ren-Shyan

    2002-04-01

    In clinically, structural image based brain tissue segmentation as a preprocess plays an important and essential role on a number of image preprocessing, such as image visualization, object recognition, image registration, and so forth. However, when we need to classify the tissues according to their physiological functions, those strategies are not satisfactory. In this study, we incorporated both tissue time-activity curves (TACs) and derived kinetic parametric curves (KPCs) information to segment brain tissues, such as striatum, gray and white matters, in dynamic FDOPA-PET studies. Four common clustering techniques, K-mean (KM), Fuzzy C-mean (FCM), Isodata (ISO), Markov Random Fields (MRF), and our method were compared to evaluate its precision. The results show 41% and 48% less mean errors in mean difference for KPCs and TACs, respectively, than other methods. Combined KPCs and TACs based clustering method provide the ability to define brain structure effectively.

  18. Malignant sweat gland tumours: an update.

    PubMed

    Cardoso, José C; Calonje, Eduardo

    2015-11-01

    Cutaneous adnexal tumours can be a diagnostic challenge for the pathologist. This is particularly true in the case of tumours with sweat gland differentiation, due to a large number of rare entities, a multiplicity of names to designate the same neoplasms and consequent lack of consensus regarding their classification and nomenclature. In the traditional view, sweat gland tumours were divided into eccrine and apocrine. However, this has been challenged in recent years, and in fact many of these tumours may have both eccrine and apocrine variants. Some display more complex features and defy classification, due to the presence of other lines of differentiation, namely follicular and/or sebaceous (in the case of apocrine tumours, due to the close embryological relationship between apocrine glands, hair follicles and sebaceous glands). The present paper reviews and updates the basic concepts regarding the following malignant sweat gland tumours: apocrine carcinoma, porocarcinoma, hidradenocarcinoma, spiradenocarcinoma, cylindrocarcinoma, microcystic adnexal carcinoma and related entities, squamoid eccrine ductal carcinoma, digital papillary adenocarcinoma, primary cutaneous mucinous carcinoma, endocrine mucin-producing sweat gland carcinoma and primary cutaneous signet ring cell carcinoma. Particular emphasis is put in recent findings that may have implications in the diagnosis and management of these tumours. PMID:26114606

  19. Classification effects of real and imaginary movement selective attention tasks on a P300-based brain-computer interface

    NASA Astrophysics Data System (ADS)

    Salvaris, Mathew; Sepulveda, Francisco

    2010-10-01

    Brain-computer interfaces (BCIs) rely on various electroencephalography methodologies that allow the user to convey their desired control to the machine. Common approaches include the use of event-related potentials (ERPs) such as the P300 and modulation of the beta and mu rhythms. All of these methods have their benefits and drawbacks. In this paper, three different selective attention tasks were tested in conjunction with a P300-based protocol (i.e. the standard counting of target stimuli as well as the conduction of real and imaginary movements in sync with the target stimuli). The three tasks were performed by a total of 10 participants, with the majority (7 out of 10) of the participants having never before participated in imaginary movement BCI experiments. Channels and methods used were optimized for the P300 ERP and no sensory-motor rhythms were explicitly used. The classifier used was a simple Fisher's linear discriminant. Results were encouraging, showing that on average the imaginary movement achieved a P300 versus No-P300 classification accuracy of 84.53%. In comparison, mental counting, the standard selective attention task used in previous studies, achieved 78.9% and real movement 90.3%. Furthermore, multiple trial classification results were recorded and compared, with real movement reaching 99.5% accuracy after four trials (12.8 s), imaginary movement reaching 99.5% accuracy after five trials (16 s) and counting reaching 98.2% accuracy after ten trials (32 s).

  20. Classification of resting, anticipation and movement states in self-initiated arm movements for EEG brain computer interfaces.

    PubMed

    Rodrigo, Miguel; Montesano, Luis; Minguez, Javier

    2011-01-01

    In the last years, there has been an increasing interest in using Brain Computer Interfaces (BCI) within motor rehabilitation therapies that use robotic devices or functional electro stimulation to help or guide the efforts of the patient to move her body. A crucial step of these therapies is to provide help to the user just when she is actually trying to accomplish a certain motion or task One of the most promising applications of BCI systems in this context is its ability to measure the user intentions and actions to trigger the rehabilitation devices accordingly. This paper studies the single-trial classification based on EEG measurements of three basic states during the execution of self-initiated motion: rest, motion preparation (or anticipation) and motion. We conducted an experiment where the participants had to reach at their will eight different locations from a fixed starting position. Results for seven healthy subjects show that it is possible to achieve good classification rates given that features are carefully selected for each subject and for each pair of states. PMID:22255775

  1. WAIS Digit Span-Based Indicators of Malingered Neurocognitive Dysfunction: Classification Accuracy in Traumatic Brain Injury

    ERIC Educational Resources Information Center

    Heinly, Matthew T.; Greve, Kevin W.; Bianchini, Kevin J.; Love, Jeffrey M.; Brennan, Adrianne

    2005-01-01

    The present study determined specificity and sensitivity to malingered neurocognitive dysfunction (MND) in traumatic brain injury (TBI) for several Wechsler Adult Intelligence Scale (WAIS) Digit Span scores. TBI patients (n = 344) were categorized into one of five groups: no incentive, incentive only, suspect, probable MND, and definite MND.…

  2. Outcome Classification of Preschool Children with Autism Spectrum Disorders Using Mri Brain Measures.

    ERIC Educational Resources Information Center

    Akshoomoff, Natacha; Lord, Catherine; Lincoln, Alan J.; Courchesne, Rachel Y.; Carper, Ruth A.; Townsend, Jeanne; Courchesne, Eric

    2004-01-01

    Objective: To test the hypothesis that a combination of magnetic resonance imaging (MRI) brain measures obtained during early childhood distinguish children with autism spectrum disorders (ASD) from typically developing children and is associated with functional outcome. Method: Quantitative MRI technology was used to measure gray and white matter…

  3. Brain

    MedlinePlus

    ... will return after updating. Resources Archived Modules Updates Brain Cerebrum The cerebrum is the part of the ... the outside of the brain and spinal cord. Brain Stem The brain stem is the part of ...

  4. The Application of the International Classification of Functioning, Disability and Health to Functional Auditory Consequences of Mild Traumatic Brain Injury.

    PubMed

    Werff, Kathy R Vander

    2016-08-01

    This article reviews the auditory consequences of mild traumatic brain injury (mTBI) within the context of the International Classification of Functioning, Disability and Health (ICF). Because of growing awareness of mTBI as a public health concern and the diverse and heterogeneous nature of the individual consequences, it is important to provide audiologists and other health care providers with a better understanding of potential implications in the assessment of levels of function and disability for individual interdisciplinary remediation planning. In consideration of body structures and function, the mechanisms of injury that may result in peripheral or central auditory dysfunction in mTBI are reviewed, along with a broader scope of effects of injury to the brain. The activity limitations and participation restrictions that may affect assessment and management in the context of an individual's personal factors and their environment are considered. Finally, a review of management strategies for mTBI from an audiological perspective as part of a multidisciplinary team is included. PMID:27489400

  5. Peculiarities of hyperlipidaemia in tumour patients.

    PubMed Central

    Dilman, V. M.; Berstein, L. M.; Ostroumova, M. N.; Tsyrlina, Y. V.; Golubev, A. G.

    1981-01-01

    The study group included 684 cases: 258 patients with breast carcinoma, 113 males with lung cancer, 42 patients with rectal tumours, 42 patients with stomach tumours, 59 patients with fibroadenomatosis, and 170 healthy subjects of varying age (male and female). A relatively high blood triglyceride level was found in patients with breast, lung, rectal (females), and stomach (female) tumours. The blood concentration of high-density lipoprotein-cholesterol in patients with breast, lung, and stomach (female) tumours was relatively low. The elimination of tumour (breast carcinoma) did not lead to significant changes in lipid metabolism. There was no correlation between degree of lipidaemia and stage of tumour progression except in the cases of rectal cancer. Preliminary results are presented on the tentative classification of hyperlipoproteinaemia in tumour patients, using the lipid concentration threshold values advocated by Carlson et al. (1977); an increased frequency of Type IV hyperlipoproteinaemia proved to be the most characteristic feature of tumour patients. The results are discussed in terms of the concept of the importance of lipid metabolic disturbances, primarily those due to ageing, in the genesis of the syndrome of "cancerophilia" (predisposition to cancer). PMID:7248149

  6. Characterization of a Raman spectroscopy probe system for intraoperative brain tissue classification

    PubMed Central

    Desroches, Joannie; Jermyn, Michael; Mok, Kelvin; Lemieux-Leduc, Cédric; Mercier, Jeanne; St-Arnaud, Karl; Urmey, Kirk; Guiot, Marie-Christine; Marple, Eric; Petrecca, Kevin; Leblond, Frédéric

    2015-01-01

    A detailed characterization study is presented of a Raman spectroscopy system designed to maximize the volume of resected cancer tissue in glioma surgery based on in vivo molecular tissue characterization. It consists of a hand-held probe system measuring spectrally resolved inelastically scattered light interacting with tissue, designed and optimized for in vivo measurements. Factors such as linearity of the signal with integration time and laser power, and their impact on signal to noise ratio, are studied leading to optimal data acquisition parameters. The impact of ambient light sources in the operating room is assessed and recommendations made for optimal operating conditions. In vivo Raman spectra of normal brain, cancer and necrotic tissue were measured in 10 patients, demonstrating that real-time inelastic scattering measurements can distinguish necrosis from vital tissue (including tumor and normal brain tissue) with an accuracy of 87%, a sensitivity of 84% and a specificity of 89%. PMID:26203368

  7. Toward more intuitive brain-computer interfacing: classification of binary covert intentions using functional near-infrared spectroscopy.

    PubMed

    Hwang, Han-Jeong; Choi, Han; Kim, Jeong-Youn; Chang, Won-Du; Kim, Do-Won; Kim, Kiwoong; Jo, Sungho; Im, Chang-Hwan

    2016-09-01

    In traditional brain-computer interface (BCI) studies, binary communication systems have generally been implemented using two mental tasks arbitrarily assigned to “yes” or “no” intentions (e.g., mental arithmetic calculation for “yes”). A recent pilot study performed with one paralyzed patient showed the possibility of a more intuitive paradigm for binary BCI communications, in which the patient’s internal yes/no intentions were directly decoded from functional near-infrared spectroscopy (fNIRS). We investigated whether such an “fNIRS-based direct intention decoding” paradigm can be reliably used for practical BCI communications. Eight healthy subjects participated in this study, and each participant was administered 70 disjunctive questions. Brain hemodynamic responses were recorded using a multichannel fNIRS device, while the participants were internally expressing “yes” or “no” intentions to each question. Different feature types, feature numbers, and time window sizes were tested to investigate optimal conditions for classifying the internal binary intentions. About 75% of the answers were correctly classified when the individual best feature set was employed (75.89% ± 1.39 and 74.08% ± 2.87 for oxygenated and deoxygenated hemoglobin responses, respectively), which was significantly higher than a random chance level (68.57% for p < 0.001). The kurtosis feature showed the highest mean classification accuracy among all feature types. The grand-averaged hemodynamic responses showed that wide brain regions are associated with the processing of binary implicit intentions. Our experimental results demonstrated that direct decoding of internal binary intention has the potential to be used for implementing more intuitive and user-friendly communication systems for patients with motor disabilities. PMID:27050535

  8. Magnetic resonance imaging findings in 40 dogs with histologically confirmed intracranial tumours.

    PubMed

    Ródenas, Sergio; Pumarola, Marti; Gaitero, Lluís; Zamora, Angels; Añor, Sònia

    2011-01-01

    Magnetic resonance (MR) images of 40 dogs with histologically confirmed primary and secondary intracranial tumours were reviewed. Forty-one tumours were diagnosed by means of MR imaging (MRI). MRI findings allowed diagnosis of a neoplastic lesion in 37/41 cases. Based on MRI features, differentiation between neoplastic and non-neoplastic lesions was possible in 24/27 (89%) primary brain tumours and in 13/14 (92%) secondary brain tumours. Diagnosis of tumour type based on MRI features was correct in 19/27 (70%) primary tumours and in 13/14 secondary tumours. The results of this study show that MRI is a good diagnostic imaging modality to detect neoplastic lesions and to diagnose tumour type in dogs. However, as some neoplasms show equivocal MRI features the technique has limitations in the detection of some intracranial tumours and in predicting tumour type. PMID:19914851

  9. Unsupervised multiparametric classification of dynamic susceptibility contrast imaging: study of the healthy brain.

    PubMed

    Artzi, M; Aizenstein, O; Hendler, T; Ben Bashat, D

    2011-06-01

    Characterization and quantification of magnetic resonance perfusion images is important for clinical interpretation, though this calls for a reproducible and accurate method of analysis and a robust healthy reference. The few studies which have examined the perfusion of the healthy brain using dynamic susceptibility contrast (DSC) imaging were largely limited to manual definition of the regions of interest (ROI) and results were dependent on the location of the ROI. The current study aimed to develop a methodology for DSC data analysis and to obtain reference values of healthy subjects. Twenty three healthy volunteers underwent DSC. An unsupervised multiparametric clustering method was applied to four perfusion parameters. Three clusters were defined and identified as: dura-blood-vessels, gray matter and white matter and their vascular characteristics were obtained. Additionally, regional perfusion differences were studied and revealed a prolonged mean transient time and a trend for higher vascularity in the posterior compared with the anterior and middle cerebral vascular territories. While additional studies are required to confirm our findings, this result may have important clinical implications. The proposed unsupervised multiparametric method enabled accurate tissue differentiation, is easy replicable and has a wide range of applications in both pathological and healthy brains. PMID:21419230

  10. A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction

    NASA Astrophysics Data System (ADS)

    Wels, Michael; Zheng, Yefeng; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

    2011-06-01

    We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average