Dynamic PET-MRI integration for a new multiparametric approach of tumour heterogeneity in non-small-cell lung cancer (NSCLC)/Integrating PET-MR data for a multiparametric approach of tumour heterogeneity in non-small-cell lung cancer (NSCLC)

Tumor heterogeneity is an important factor of progression and resistance to treatment. Multiparametric PET-MRI imaging offers unique opportunities to characterize biological cellular processes, but has never been evaluated at the regional level in Non-Small Cell Lung Cancer (NSCLC), the leading cause of oncological death. A simultaneous dynamic multiparametric ¹⁸F-FDG PET-MRI approach has been developed to this end. This approach required the 'in-house' implementation of the reference absolute PET quantitative method of glucose metabolism (Sokoloff's tri-compartmental model); the development of a method for correcting geometric distortions in diffusion weighted imaging, validated on phantom and clinically tested; the phantom validation of quantitative MRI methods (T1/T2 relaxometry), also clinically tested; and the 'in-house' implementation of the Tofts compartmental model (extended version) for the evaluation of tumor vascularisation by dynamic perfusion MRI. The results of our work, performed at the regional intra-tumor level, illustrate the heterogeneity of the regional interlinks between glucose metabolism and vascularisation in NSCLC, two fundamental biological hallmarks of tumor progression, and show that an unsupervised tumor partitioning by Gaussian mixture model, integrating all the PET-MRI biomarkers of this project, individualizes 3 types of supervoxels, whose biological signature can be predicted with 97% accuracy by 4 dominant PET-MRI biomarkers, revealed by metaheuristic machine learning methods. (author)