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

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

doi:10.1038/s41551-023-01047-9

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

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Katiyar, Prateek
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;
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Agrawal, Vaibhav
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;
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Schölkopf, Bernhard
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;
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https://doi.org/10.1038/s41551-023-01047-9
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Katiyar, P., Schwenck, J., Frauenfeld, L., Divine, M. R., Agrawal, V., Kohlhofer, U., et al. (2023). Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET–MRI data. nature biomedical engineering, 7(8), 1014-1027. doi:10.1038/s41551-023-01047-9.

Cite as: https://hdl.handle.net/21.11116/0000-000D-C50D-7

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