Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET–MRI data

Menée à l'aide de modèles murins et d'échantillons tissulaires provenant de patients présentant des métastases hépatiques ayant pour origine un cancer colorectal, cette étude met en évidence l'intérêt d'algorithmes d'apprentissage automatique, utilisant des données d'IRM et de tomographies numériques par émission de positrons, pour quantifier l'hétérogénéité intratumorale

Résumé en anglais

In oncology, intratumoural heterogeneity is closely linked with the efficacy of therapy, and can be partially characterized via tumour biopsies. Here we show that intratumoural heterogeneity can be characterized spatially via phenotype-specific, multi-view learning classifiers trained with data from dynamic positron emission tomography (PET) and multiparametric magnetic resonance imaging (MRI). Classifiers trained with PET–MRI data from mice with subcutaneous colon cancer quantified phenotypic changes resulting from an apoptosis-inducing targeted therapeutic and provided biologically relevant probability maps of tumour-tissue subtypes. When applied to retrospective PET–MRI data of patients with liver metastases from colorectal cancer, the trained classifiers characterized intratumoural tissue subregions in agreement with tumour histology. The spatial characterization of intratumoural heterogeneity in mice and patients via multimodal, multiparametric imaging aided by machine-learning may facilitate applications in precision oncology.