TY  - JOUR
AU  - Katiyar, Prateek
AU  - Schwenck, Johannes
AU  - Frauenfeld, Leonie
AU  - Divine, Mathew R
AU  - Agrawal, Vaibhav
AU  - Kohlhofer, Ursula
AU  - Gatidis, Sergios
AU  - Kontermann, Roland
AU  - Königsrainer, Alfred
AU  - Quintanilla-Martinez, Leticia
AU  - la Fougère, Christian
AU  - Schölkopf, Bernhard
AU  - Pichler, Bernd
AU  - Disselhorst, Jonathan A
TI  - Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data.
JO  - Nature biomedical engineering
VL  - 7
IS  - 8
SN  - 2157-846X
CY  - Tokyo
PB  - Nature Research
M1  - DKFZ-2023-01108
SP  - 1014-1027
PY  - 2023
N1  - 2023 Aug;7(8):1014-1027
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:37277483
DO  - DOI:10.1038/s41551-023-01047-9
UR  - https://inrepo02.dkfz.de/record/276425
ER  -