Joint research paper on quantification of intratumoural heterogeneity via machine-learning models trained on PET-MRI data published in Nature Biomedical Engineering

June 13, 2023

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

Prateek Katiyar, Johannes Schwenck, Leonie Frauenfeld, Mathew R Divine, Vaibhav Agrawal, Ursula Kohlhofer, Sergios Gatidis, Roland Kontermann, Alfred Königsrainer, Leticia Quintanilla-Martinez, Christian la Fougère, Bernhard Schölkopf, Bernd J Pichler, Jonathan A Disselhorst

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.



doi: 10.1038/s41551-023-01047-9

To the top of the page