ArticleNature biomedical engineering2023
Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data.
Article in Nature biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Imaging the hallmarks of cancer.Nature reviews. Cancer · 2026Review
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- Fluorescence Lifetime Imaging Enables In Vivo Quantification of PD-L1 Expression and Intertumoral Heterogeneity.Cancer research · 2025Article
- Imaging hypoxia for head and neck cancer: current status, challenges, and prospects.Theranostics · 2025Review
- Imaging cancer metabolism using magnetic resonance.Npj imaging · 2024Review
- Article
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Authors and funding
14 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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