A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features
Menée à partir de clichés d'IRMs pré-opératoires réalisés sur 922 patientes atteintes d'un cancer mammaire invasif, cette étude analyse la corrélation entre des caractéristiques moléculaires tumorales (statut des récepteurs hormonaux, statut du facteur de croissance EGF, marqueur de prolifération Ki-67) et les caractéristiques tumorales identifiées à l'aide d'algorithmes d'apprentissage automatique exploitant des images de la tumeur et du tissu environnant
Background : Recent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship.
Methods : We analysed a set of 922 patients with invasive breast cancer and pre-operative MRI. The MRIs were analysed by a computer algorithm to extract 529 features of the tumour and the surrounding tissue. Machine-learning-based models based on the imaging features were trained using a portion of the data (461 patients) to predict the following molecular, genomic, and proliferation characteristics: tumour surrogate molecular subtype, oestrogen receptor, progesterone receptor and human epidermal growth factor status, as well as a tumour proliferation marker (Ki-67). Trained models were evaluated on the set of the remaining 461 patients.
Results : Multivariate models were predictive of Luminal A subtype with AUC = 0.697 (95% CI: 0.647–0.746, p < .0001), triple negative breast cancer with AUC = 0.654 (95% CI: 0.589–0.727, p < .0001), ER status with AUC = 0.649 (95% CI: 0.591–0.705, p < .001), and PR status with AUC = 0.622 (95% CI: 0.569–0.674, p < .0001). Associations between individual features and subtypes we also found.
Conclusions : There is a moderate association between tumour molecular biomarkers and algorithmically assessed imaging features.
British Journal of Cancer , résumé, 2018