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| Home > Publications database > Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data. |
| Home > Publications database > Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data. |
| Journal Article | DKFZ-2023-01108 |
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2023
Nature Research
Tokyo
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Please use a persistent id in citations: doi:10.1038/s41551-023-01047-9
Abstract: 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.
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