Σκοπός αυτής της διπλωματικής εργασίας είναι να ερευνηθούν μέθοδοι μηχανικής μάθησης για την ταξινόμηση διαφόρων τύπων καρκινικών όγκων εγκεφάλου με χρήση δεδομένων μαγνητικής τομογραφίας. Η διάγνωση του τύπου του καρκίνου είναι σημαντική για τον κατάλληλο σχεδιασμό της θεραπείας. Γενικά η ταξινόμηση καρκινικών όγκων αποτελείται από επιμέρους βήματα, όπως καθορισμός των περιοχών ενδιαφέροντος (ROIs), εξαγωγή χαρακτηριστικών, επιλογή χαρακτηριστικών, ταξινόμηση. Η εργασία αυτή εστιάζει στα δύο τελευταία βήματα ώστε να εξαχθεί μια γενική επισκόπηση της επίδρασης των εκάστοτε μεθόδων όσον αφορά την ταξινόμηση των διαφόρων όγκων. Τα εξαγόμενα χαρακτηριστικά περιλαμβάνουν χαρακτηριστικά φωτεινότητας και περιγράμματος από συμβατικές τεχνικές απεικόνισης μαγνητικής τομογραφίας (Τ2, Τ1 με έγχυση σκιαγραφικού, Flair,Τ1) καθώς και μη συμβατικές τεχνικές (Μαγνητική τομογραφία αιματικής διήθησης ). Για την επιλογή των χαρακτηριστικών χρησιμοποιήθηκαν διάφορες μέθοδοι φιλτραρίσματος, όπως CFSsubset, wrapper, consistency σε συνδυασμό με μεθόδους αναζήτησης, όπως scatter, best first, greedy stepwise, με τη βοήθεια του πακέτου Waikato Environment for Knowledge Analysis (WEKA). Οι μέθοδοι εφαρμόστηκαν σε 101 ασθενείς με καρκινικούς όγκους εγκεφάλου οι οποίοι είχαν διαγνωστεί ως μετάσταση (24), μηνιγγίωμα (4), γλοίωμα βαθμού 2 (22), γλοίωμα βαθμού 3 (17) ή γλοίωμα βαθμού 4 (34) και επαληθεύτηκαν με τη στρατηγική του αχρησιμοποίητου παραδείγματος (Leave One Out-LOO) / The objective of this study is to investigate the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. A computer-assisted classification method combining conventional magnetic resonance imaging (MRI) and perfusion MRI is developed and used for differential diagnosis. The characterization and accurate determination of brain tumor grade and type is very important because it influences and specifies patient's treatment planning. The proposed scheme consists of several steps including ROI definition, feature extraction, feature selection and classification. The extracted features include tumor shape and intensity characteristics. Features subset selection is performed using two filtering methods, correlation-based feature selection method and consistency method, and a wrapper approach in combination with three different search algorithms (best first, greedy stepwise and scatter). These methods are implemented using the assistance of the WEKA software [20]. The highest binary classification accuracy assessed by leave-one-out (LOO) cross-validation on 102 brain tumors, is 94.1% for discrimination of metastases from gliomas, and 91.3% for discrimination of high grade from low grade neoplasms. Multi-class classification is also performed and 76.29% accuracy achieved.

Brain tumors represent a diverse spectrum of cancer types which can induce grave complications and lead to poor life expectancy. Amongst the various brain tumor types, gliomas are primary brain tumors that compose about 30% of adult brain tumors. They are graded according to the World Health Organization into Grades 1 to 4 (G1-G4), where G4 is the highest grade with the highest malignancy and poor prognosis. Early diagnosis and classification of brain tumor grade is very important since it can improve the treatment procedure and (potentially) prolong a patient's life, since life expectancy largely depends on the level of malignancy and the tumor's histological characteristics. While clinicians have diagnostic tools they use as a gold standard, such as biopsies these are either invasive or costly. A widely used example of a non-invasive technique is magnetic resonance imaging, due to its ability to produce images with different soft-tissue contrast and high spatial resolution thanks to multiple imaging sequences. However, the examination of such images can be overwhelming for radiologists due to the overall large amount of data. Deep learning approaches, on the other hand, have shown great potential in brain tumor diagnosis and can assist radiologists in the decision-making process. In this thesis, brain tumor grade classification in MR images is performed using deep learning. Two popular pre-trained CNN models (VGG-19, ResNet50) were employed using single MR modalities and combinations of them to classify gliomas into three grades. All models were trained using data augmentation on 2D images from the TCGA dataset, which consisted of 3D volumes from 142 anonymized patients. The models were evaluated based on accuracy, precision, recall, F1-score, AUC score, as well as the Wilcoxon Signed-Rank test to establish if one classifier was statistically significantly better than the other. Since deep learning models are typically 'black box' models and can be difficult to interpret by non-experts, Gradient-weighted Class Activation Mapping (Grad-CAM) was used in order to address model explainability. For single modalities, VGG-19 displayed the highest performance with a test accuracy of 77.86%, whilst for combinations of two and three modalities T1ce, FLAIR and T2, T1ce, FLAIR were the best performing ones for VGG-19 with a test accuracy of 74.48%, 75.78%, respectively. Statistical comparisons indicated that for single MR modalities and combinations of two MR modalities, there was not a statistically significant difference between the two classifiers, whilst for combination of three modalities, one model was better than the other. However, given the small size of the test population, these comparisons have low statistical power. The use of Grad-CAM for model explainability indicated that ResNet50 was able to localize the tumor region better than VGG-19.