Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication




Conference paper

Publication Date



14224 LNCS


758 - 767