Radiomique multimodale en neuro-oncologie

Glioblastoma multiforme (GBM) is a WHO grade IV tumor that represents 49% of ail brain tumours. Despite aggressive treatment modalities (radiotherapy, chemotherapy and surgical resections) the prognosis is poor, as médian overall survival (OS) is 12-14 months. GBM’s neuroimaging (non-invasive) features can provide opportunities for subclassification, prognostication, and the development of targeted therapies that could advance the clinical practice. This thesis focuses on developing a prognostic model based on multimodal MRI-derived (Tl pre- and post-contrast, T2 and FLAIR) radiomics in GBM. The proposed methodological framework consists in i) registering the available 3D multimodal MR images andsegmenting the tumor volume, ii) extracting radiomics iii) building and validating a prognostic model using machine learning algorithms applied to multicentric clinical cohorts of patients. The core component of the framework rely on extracting radiomics (including intensity, shape and textural metrics) and building prognostic models using two different machine learning algorithms (Support Vector Machine (SVM) and Random Forest (RF)) that were compared by selecting, ranking and combining optimal features. The potential benefits and respective impact of several MRI pre-processing steps (spatial resampling of the voxels, intensities quantization and normalization, segmentation) for reliable extraction of radiomics was thoroughly assessed. Moreover, the standardization of the radiomics features among methodological teams was done by contributing to “Multicentre Initiative for Standardisation of Radiomics”. The accuracy obtained on the independent test dataset using SVM and RF reached upto 83%- 77% when combining ail available features and upto 87%-77% when using only reliable features previously identified as robust, depending on number of features and modality. In this thesis, I developed a framework for developing a compréhensive prognostic model for patients with GBM from multimodal MRI-derived “radiomics and machine learning”. The future work will consists in building a unified prognostic model exploiting other contextual data such as genomics. In case of new algorithm development we look forward to develop the Ensemble models and deep learning-based techniques.Le glioblastome multiforme (GBM) est une tumeur de grade IV représentant 49% de toutes les tumeurs cérébrales. Malgré des modalités de traitement agressives (radiothérapie, chimiothérapie et résection chirurgicale), le pronostic est mauvais avec une survie globale médiane de 12 à 14 mois. Les aractéristiques issues de la neuro imagerie des GBM peuvent fournir de nouvelles opportunités pour la classification, le pronostic et le développement de nouvelles thérapies ciblées pour faire progresser la pratique clinique. Cette thèse se concentre sur le développement de modèles pronostiques exploitant des caractéristiques de radiomique extraites des images multimodales IRM (T1 pré- et post-contraste, T2 et FLAIR). Le contexte méthodologique proposé consiste à i) recaler tous les volumes multimodaux IRM disponibles et en segmenter un volume tumoral unique, ii) extraire des caractéristiques radiomiques et iii) construire et valider les modèles pronostiques par l’utilisation d’algorithmes d’apprentissage automatique exploitant des cohortes cliniques multicentriques de patients. Le coeur des méthodes développées est fondé sur l’extraction de radiomiques (incluant des paramètres d’intensité, de forme et de textures) pour construire des modèles pronostiques à l’aide de deux algorithmes d’apprentissage, les machines à vecteurs de support (support vector machines, SVM) et les forêts aléatoires (random forest, RF), comparées dans leur capacité à sélectionner et combiner les caractéristiques optimales. Les bénéfices et l’impact de plusieurs étapes de pré-traitement des images IRM (re-échantillonnage spatial des voxels, normalisation, segmentation et discrétisation des intensités) pour une extraction de métriques fiables ont été évalués. De plus les caractéristiques radiomiques ont été standardisées en participant à l’initiative internationale de standardisation multicentrique des radiomiques. La précision obtenue sur le jeu de test indépendant avec les deux algorithmes d’apprentissage SVM et RF, en fonction des modalités utilisées et du nombre de caractéristiques combinées atteignait 77 à 83% en exploitant toutes les radiomiques disponibles sans prendre en compte leur fiabilité intrinsèque, et 77 à 87% en n’utilisant que les métriques identifiées comme fiables.Dans cette thèse, un contexte méthodologique a été proposé, développé et validé, qui permet la construction de modèles pronostiques dans le cadre des GBM et de l’imagerie multimodale IRM exploitée par des algorithmes d’apprentissage automatique. Les travaux futurs pourront s’intéresser à l’ajout à ces modèles des informations contextuelles et génétiques. D’un point de vue algorithmique, l’exploitation de nouvelles techniques d’apprentissage profond est aussi prometteuse

A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in Glioblastoma Multiforme

International audienceIn Glioblastoma Multiforme (GBM) image-derived features (“radiomics”) could help in individualizing patient management. Simple geometric features of tumors (necrosis, edema, active tumor) and first-order statistics in Magnetic Resonance Imaging (MRI) are used in clinical practice. However, these features provide limited characterization power because they do not incorporate spatial information and thus cannot differentiate patterns. The aim of this work is to develop and evaluate a methodological framework dedicated to building a prognostic model based on heterogeneity textural features of multimodal MRI sequences (T1, T1-contrast, T2 and FLAIR) in GBM. The proposed workflow consists in i) registering the available 3D multimodal MR images and segmenting the tumor volume, ii) extracting image features such as heterogeneity metrics and iii) building a prognostic model by selecting, ranking and combining optimal features through machine learning (Support Vector Machine). This framework was applied to 40 histologically proven GBM patients with the endpoint being overall survival (OS) classified as above or below the median survival (15 months). The models combining features from a maximum of two modalities were evaluated using leave-one-out cross-validation (LOOCV). A classification accuracy of 90% (sensitivity 85%, specificity 95%) was obtained by combining features from T1 pre-contrast and T1 post-contrast sequences. Our results suggest that several textural features in each MR sequence have prognostic value in GBM

Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures

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Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures.

BACKGROUND: Tumor hypoxia increases resistance to radiotherapy and systemic therapy. Our aim was to develop and validate a disease-agnostic and disease-specific CT (+FDG-PET) based radiomics hypoxia classification signature. MATERIAL AND METHODS: A total of 808 patients with imaging data were included: N = 100 training/N = 183 external validation cases for a disease-agnostic CT hypoxia classification signature, N = 76 training/N = 39 validation cases for the H&N CT signature and N = 62 training/N = 36 validation cases for the Lung CT signature. The primary gross tumor volumes (GTV) were manually defined by experts on CT. In order to dichotomize between hypoxic/well-oxygenated tumors a threshold of 20% was used for the [18F]-HX4-derived hypoxic fractions (HF). A random forest (RF)-based machine-learning classifier/regressor was trained to classify patients as hypoxia-positive/ negative based on radiomic features. RESULTS: A 11 feature "disease-agnostic CT model" reached AUC's of respectively 0.78 (95% confidence interval [CI], 0.62-0.94), 0.82 (95% CI, 0.67-0.96) and 0.78 (95% CI, 0.67-0.89) in three external validation datasets. A "disease-agnostic FDG-PET model" reached an AUC of 0.73 (0.95% CI, 0.49-0.97) in validation by combining 5 features. The highest "lung-specific CT model" reached an AUC of 0.80 (0.95% CI, 0.65-0.95) in validation with 4 CT features, while the "H&N-specific CT model" reached an AUC of 0.84 (0.95% CI, 0.64-1.00) in validation with 15 CT features. A tumor volume-alone model was unable to significantly classify patients as hypoxia-positive/ negative. A significant survival split (P = 0.037) was found between CT-classified hypoxia strata in an external H&N cohort (n = 517), while 117 significant hypoxia gene-CT signature feature associations were found in an external lung cohort (n = 80). CONCLUSION: The disease-specific radiomics signatures perform better than the disease agnostic ones. By identifying hypoxic patients our signatures have the potential to enrich interventional hypoxia-targeting trials

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

International audienceBackground Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue