Abstract
Existing methods for interpretability of model predictions are largely based on technical insights and are not linked to clinical context. We use the question of predicting response to radiotherapy in colorectal cancer patients as an exemplar for developing prediction models that do provide such contextual information and therefore can effectively support clinical decision making. There is a growing body of evidence that about 30% of colorectal cancer patients do not respond to radiotherapy and will need alternative treatment. The consensus molecular subtypes for colorectal cancer (CMS) provide one such approach to categorising patients based on their disease biology. Here we select the CMS4 subtype as a proxy for stromal infiltration. By jointly predicting a patient’s response to radiotherapy, the presence of CMS4, and the epithelial tissue map from morphological features extracted from standard H &E slides we provide a comprehensive clinically relevant assessment of a biopsy. A graph neural network is trained to achieve this joint prediction task, which subsequently provides novel interpretability maps to aid clinicians in their cancer treatment decision making process. Our model is trained and validated on two private rectal cancer datasets.
Supported by Cancer Research UK.
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Data Use Declaration and Acknowledgements
The Grampian, Aristotle and FOCUS datasets used in this study were available through the S:CORT consortium. Grampian and Aristotle will be made publicly available shortly. Additional data is available on request to the S:CORT consortium. The S:CORT consortium was reviewed and approved by the South Cambs Research Ethics committee (REC ref 15/EE/0241; IRAS reference 169363). The Stratification in Colorectal Cancer Consortium (S:CORT) was funded by the Medical Research Council and Cancer Research UK (MR/M016587/1). The Aristotle trial was funded by Cancer Research UK (CRUK/08/032). The funders played no role in the analyses performed or the results presented. Financial support: RW - EPSRC Center for Doctoral Training in Health Data Science (EP/S02428X/1), Oxford CRUK Cancer Centre; VHK - Promedica Foundation (F-87701-41-01) and Swiss National Science Foundation (P2SKP3\(\_\)168322/1, P2SKP3\(\_\)168322/2); TSM - S:CORT (see above); JR, KS - Oxford NIHR National Oxford Biomedical Research Centre and the PathLAKE consortium (InnovateUK). The computational aspects of this research were funded from the NIHR Oxford BRC with additional support from the Wellcome Trust Core Award Grant Number 203141/Z/16/Z. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.
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Wood, R. et al. (2023). Joint Prediction of Response to Therapy, Molecular Traits, and Spatial Organisation in Colorectal Cancer Biopsies. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14224. Springer, Cham. https://doi.org/10.1007/978-3-031-43904-9_73
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