Machine Learning for Classifying Abnormal Brain Tissue Progression based on Multi-parametric Magnetic Resonance Data

In vivo Magnetic Resonance Imaging (MRI) represents one of the major breakthroughs in medicine and biomedical sciences. In recent years, conventional MRI techniques have been complemented by several advanced MR modalities that target not only the morphology of an imaged organ, but also its functional properties: for instance, MR spectroscopy/MR spectroscopic imaging reveal metabolic information, perfusion MRI reveals microvascularisation, diffusion MRI reveals tissue architecture, etc. In neuroradiology, conventional MRI has a rather poor specificity in the diagnosis,prognosis and therapy follow-up of brain tumours. Several advanced MRI techniques have shown their own potential in brain tumour applications.However, there is a strong need for integrated data processing methods that can take advantage of the strengths and complementarity of all the available advanced MR techniques, in order to make a real impact in routine clinical practice. This doctoral project will develop classification methods that can cope with multi-modal MR data (in particular, anatomic MRI, spectroscopy, diffusion and perfusion MRI), and apply them to the diagnosis and follow-up of brain tumour and multiple sclerosis. One of the main goals is to solve some of the standing problems in brain tumour applications, e.g., non-invasive tumour subtyping and grading, prediction of tumour infiltration, or tumour recurrence after treatment. To this end, several data processing steps need to be designed and optimized: computation of relevant features from the data, matching the image resolutions among the different modalities, multi-modal data fusion and classification. Multi-modal MR data from brain tumour patients will be available from UZ Leuven. The data comes from patients with untreated brain tumours (low and high grade glioma; metastases), as well as longitudinal follow-up of glioma patients focusing on a new treatment approachcalled dendritic cell therapy. Data from multiple sclerosis patients will be available through a collaboration with Universite Claude Bernard Lyon I.nrpages: 170status: publishe

Apprentissage par machine pour classifier la progression anormale des tissus cérébraux en fonction de données de résonance magnétique multiparamétriques

Machine learning is a subdiscipline in the field of artificial intelligence, which focuses on algorithms capable of adapting their parameters based on a set of observed data, by optimizing an objective or cost function. Machine learning has been the subject of large interest in the biomedical community because it can improve sensitivity and/or specificity of detection and diagnosis of any disease, while increasing the objectivity of the decision-making process. With the late increase in volume and complexity of medical data being collected, there is a clear need for applying machine learning algorithms in multi-parametric analysis for new detection and diagnostic modalities. Biomedical imaging is becoming indispensable for healthcare, as multiple modalities, such as Magnetic Resonance Imaging (MRI), Computed Tomography, and Positron Emission Tomography, are being increasingly used in both research and clinical settings. The non-invasive standard for brain imaging is MRI, as it can provide structural and functional brain maps with high resolution, all within acceptable scanning times. However, with the increase of MRI data volume and complexity, it is becoming more time consuming and difficult for clinicians to integrate all data and make accurate decisions. The aim of this thesis is to develop machine learning methods for automated preprocessing and diagnosis of abnormal brain tissues, in particular for the followup of glioblastoma multiforme (GBM) and multiple sclerosis (MS). Current conventional MRI (cMRI) techniques are very useful in detecting the main features of brain tumours and MS lesions, such as size and location, but are insufficient in specifying the grade or evolution of the disease. Therefore, the acquisition of advanced MRI, such as perfusion weighted imaging (PWI), diffusion kurtosis imaging (DKI), and magnetic resonance spectroscopic imaging (MRSI), is necessary to provide complementary information such as blood flow, tissue organisation, and metabolism, induced by pathological changes. In the GBM experiments our aim is to discriminate and predict the evolution of patients treated with standard radiochemotherapy and immunotherapy based on conventional and advanced MRI data. In the MS experiments our aim is to discriminate between healthy subjects and MS patients, as well as between different MS forms, based only on clinical and MRSI data. As a first experiment in GBM follow-up, only advanced MRI parameters were explored on a relatively small subset of patients. Average PWI parameters computed on manually delineated regions of interest (ROI) were found to be perfect biomarkers for predicting GBM evolution one month prior to the clinicians. In a second experiment in GBM follow-up of a larger subset of patients, MRSI was replaced by cMRI, while PWI and DKI parameter quantification was automated. Feature extraction was done on semi-manual tumour delineations, thereby reducing the time put by the clinician for manual delineating the contrast enhancing (CE) ROI. Learning a modified boosting algorithm on features extracted from semi-manual ROIs was shown to provide very high accuracy results for GBM diagnosis. In a third experiment in GBM follow-up of an extended subset of patients, a modified version of parametric response maps (PRM) was proposed to take into account the most likely infiltration area of the tumour, reducing even further the time a clinician would have to put for manual delineating the tumour, because all subsequent MRI scans were registered to the first one. Two types of computing PRM were compared, one based on cMRI and one based on PWI, as features extracted with these two modalities were the best in discriminating the GBM evolution, according to results from the previous two experiments. Results obtained within this last GBM analysis showed that using PRM based on cMRI is clearly superior to using PRM based on PWI [etc…]«Machine Learning» est un champ d'étude de l'intelligence artificielle qui se concentre sur des algorithmes capables d'adapter leur paramètres en se basant sur les données observées par l'optimisation d'une fonction objective ou d'une fonction de cout. Cette discipline a soulevé l'intérêt de la communauté de la recherche biomédicale puisqu'elle permet d'améliorer la sensibilité et la spécificité de la détection et du diagnostic de nombreuses pathologies tout en augmentant l'objectivité dans le processus de prise de décision thérapeutique. L'imagerie biomédicale est devenue indispensable en médecine, puisque plusieurs modalités comme l'imagerie par résonance magnétique (IRM), la tomodensitométrie et la tomographie par émission de positron sont de plus en plus utilisées en recherche et en clinique. L'IRM est la technique d'imagerie non-invasive de référence pour l'étude du cerveau humain puisqu'elle permet dans un temps d'acquisition raisonnable d'obtenir à la fois des cartographies structurelles et fonctionnelles avec une résolution spatiale élevée. Cependant, avec l'augmentation du volume et de la complexité des données IRM, il devient de plus en plus long et difficile pour le clinicien d'intégrer toutes les données afin de prendre des décisions précises. Le but de cette thèse est de développer des méthodes de « machine learning » automatisées pour la détection de tissu cérébral anormal, en particulier dans le cas de suivi de glioblastome multiforme (GBM) et de sclérose en plaques (SEP). Les techniques d'IRM conventionnelles (IRMc) actuelles sont très utiles pour détecter les principales caractéristiques des tumeurs cérébrales et les lésions de SEP, telles que leur localisation et leur taille, mais ne sont pas suffisantes pour spécifier le grade ou prédire l'évolution de la maladie. Ainsi, les techniques d'IRM avancées, telles que l'imagerie de perfusion (PWI), de diffusion (DKI) et la spectroscopie par résonance magnétique (SRM), sont nécessaires pour apporter des informations complémentaires sur les variations du flux sanguin, de l'organisation tissulaire et du métabolisme induits par la maladie. Dans une première étude de suivi de patients GBM, seuls les paramètres d'IRM avancés ont été explorés dans un relativement petit sous-groupe de patients. Les paramètres de PWI moyens, mesurés dans les régions d'intérêts (ROI) délimités manuellement, se sont avérés être d'excellents marqueurs, puisqu'ils permettent de prédire l'évolution du GBM en moyenne un mois plus tôt que le clinicien. Dans une seconde étude, réalisée sur un échantillon plus important que la précédente, la SRM a été remplacée par l'IRMc et la quantification de la PWI et du kurtosis de diffusion (DKI) a été réalisée de manière automatique. L'extraction des paramètres d'imagerie a été effectuée sur des segmentations semi-automatiques des tumeurs, réduisant ainsi le temps nécessaire au clinicien pour la délimitation du ROI de la partie de la lésion rehaussée au produit de contraste (CE-ROI). L'application d'un algorithme modifié de «boosting» sur les paramètres extraits des ROIs a montré une grande précision pour le diagnostic du GBM. Dans une troisième, une version modifiée des cartes paramétriques de réponse (PRM) est proposée pour prendre en compte la région d'infiltration de la tumeur, réduisant toujours plus le temps nécessaire pour la délimitation de la tumeur par le clinicien, puisque toutes les images IRM sont recalées sur la première. Deux façons de générer les RPM ont été comparées, l'une basée sur l'IRMc et l'autre basée sur la PWI, ces deux paramètres étant les meilleurs pour la discrimination de l'évolution du GBM, comme le montrent les deux études précédentes. Les résultats de cette étude montrent que l'emploi de PRM basés sur l'IRMc permet d'obtenir des résultats supérieurs à ceux obtenus avec les PRM basés sur la PWI [etc…

A Machine Learning approach to Predict Age-Related Motor Performance using MRI-MRS data

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Classifying glioblastoma multiforme follow-up progressive vs. responsive forms using multi-parametric MRI features

Purpose: The purpose of this paper is discriminating between tumor progression and response to treatment based on follow-up multi-parametric magnetic resonance imaging (MRI) data retrieved from glioblastoma multiforme (GBM) patients. Materials and Methods: Multi-parametric MRI data consisting of conventional MRI (cMRI) and advanced MRI [i.e., perfusion weighted MRI (PWI) and diffusion kurtosis MRI (DKI)] were acquired from 29 GBM patients treated with adjuvant therapy after surgery. We propose an automatic pipeline for processing advanced MRI data and extracting intensity-based histogram features and 3-D texture features using manually and semi-manually delineated regions of interest (ROIs). Classifiers are trained using a leave-one-patient-out cross validation scheme on complete MRI data. Balanced accuracy rate (BAR)-values are computed and compared between different ROIs, MR modalities, and classifiers, using non-parametric multiple comparison tests. Results: Maximum BAR-values using manual delineations are 0.956, 0.85, 0.879, and 0.932, for cMRI, PWI, DKI, and all three MRI modalities combined, respectively. Maximum BAR-values using semi-manual delineations are 0.932, 0.894, 0.885, and 0.947, for cMRI, PWI, DKI, and all three MR modalities combined, respectively. After statistical testing using Kruskal-Wallis and post-hoc Dunn-Šidák analysis we conclude that training a RUSBoost classifier on features extracted using semi-manual delineations on cMRI or on all MRI modalities combined performs best. Conclusions: We present two main conclusions: (1) using T1 post-contrast (T1pc) features extracted from manual total delineations, AdaBoost achieves the highest BAR-value, 0.956; (2) using T1pc-average, T1pc-90th percentile, and Cerebral Blood Volume (CBV) 90th percentile extracted from semi-manually delineated contrast enhancing ROIs, SVM-rbf, and RUSBoost achieve BAR-values of 0.947 and 0.932, respectively. Our findings show that AdaBoost, SVM-rbf, and RUSBoost trained on T1pc and CBV features can differentiate progressive from responsive GBM patients with very high accuracy.status: publishe

Comparison of manual and semi-manuel delineations for classifying glioblastoma multiforme patients based on histogram and texture MRI features

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A comparison of Machine Learning approaches for classifying Multiple Sclerosis courses using MRSI and brain segmentations.

International audienc

A comparison of Machine Learning approaches for classifying Multiple Sclerosis courses using MRSI and brain segmentations

© Springer International Publishing AG 2017. The objective of this paper is to classify Multiple Sclerosis courses using features extracted from Magnetic Resonance Spectroscopic Imaging (MRSI) combined with brain tissue segmentations of gray matter, white matter, and lesions. To this purpose we trained several classifiers, ranging from simple (i.e. Linear Discriminant Analysis) to state-of-the-art (i.e. Convolutional Neural Networks). We investigate four binary classification tasks and report maximum values of Area Under receiver operating characteristic Curve between 68% and 95%. Our best results were found after training Support Vector Machines with gaussian kernel on MRSI features combined with brain tissue segmentation features.status: publishe

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features

International audiencePurpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features. Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract N-acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests. Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71–72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features. Ion-M ˘ argineanu et al. Classifiers for Follow-up MS Patients Conclusions: Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms

Tumour relapse prediction using multi-parametric MR data recorded during follow-up of GBM patients

Ion-Margineanu A., Van Cauter S., Sima D.M., Maes F., Van Gool S.W., Sunaert S., Himmelreich U., Van Huffel S., ''Tumour relapse prediction using multi-parametric MR data recorded during follow-up of GBM patients'', 23rd annual meeting ISMRM, pp. 2253, May 30 - June 5, 2015, Toronto, Ontario, Canada.status: publishe

Tumour relapse prediction using multiparametric MR data recorded during follow-up of GBM patients

PURPOSE: We have focused on finding a classifier that best discriminates between tumour progression and regression based on multiparametric MR data retrieved from follow-up GBM patients. MATERIALS AND METHODS: Multiparametric MR data consisting of conventional and advanced MRI (perfusion, diffusion, and spectroscopy) were acquired from 29 GBM patients treated with adjuvant therapy after surgery over a period of several months. A 27-feature vector was built for each time point, although not all features could be obtained at all time points due to missing data or quality issues. We tested classifiers using LOPO method on complete and imputed data. We measure the performance by computing BER for each time point and wBER for all time points. RESULTS: If we train random forests, LogitBoost, or RobustBoost on data with complete features, we can differentiate between tumour progression and regression with 100% accuracy, one time point (i.e., about 1 month) earlier than the date when doctors had put a label (progressive or responsive) according to established radiological criteria. We obtain the same result when training the same classifiers solely on complete perfusion data. CONCLUSIONS: Our findings suggest that ensemble classifiers (i.e., random forests and boost classifiers) show promising results in predicting tumour progression earlier than established radiological criteria and should be further investigated.Ion-Margineanu A., Van Cauter S., Sima D., Maes F., Van Gool S., Sunaert S., Himmelreich U., Van Huffel S., ''Tumour relapse prediction using multiparametric MR data recorded during follow-up of GBM patients'', BioMed research international, pp. 1-13, 2015.status: publishe