Prognosticating Colorectal Cancer Recurrence using an Interpretable Deep Multi-view Network

Ho Danliang, Iain BH Tan, Mehul Motani

Prognosticating Colorectal Cancer Recurrence using an Interpretable Deep Multi-view Network

Ho Danliang, Iain BH Tan, Mehul Motani

Abstract: Colorectal cancer (CRC) is among the top three most common cancers worldwide, and around 30-50% of patients who have undergone curative-intent surgery will eventually develop recurrence. Early and accurate detection of cancer recurrence is essential to improve the health outcomes of patients. In our study, we propose an explainable multi-view deep neural network capable of extracting and integrating features from heterogeneous healthcare records. Our model takes in inputs from multiple views and comprises: 1) two subnetworks adapted to extract high quality features from time-series and tabular data views, and 2) a network that combines the two outputs and predicts CRC recurrence. Our model achieves an AUROC score of 0.95, and precision, sensitivity and specificity scores of 0.84, 0.82 and 0.96 respectively, outperforming all-known published results based on the commonly-used CEA prognostic marker, as well as that of most commercially available diagnostic assays. We explain our model's decision by highlighting important features within both data views that contribute to the outcome, using SHAP with a novel workaround that alleviates assumptions on feature independence. Through our work, we hope to contribute to the adoption of AI in healthcare by creating accurate and interpretable models, leading to better post-operative management of CRC patients.

Poster Paper

Abstract: Colorectal cancer (CRC) is among the top three most common cancers worldwide, and around 30-50% of patients who have undergone curative-intent surgery will eventually develop recurrence. Early and accurate detection of cancer recurrence is essential to improve the health outcomes of patients. In our study, we propose an explainable multi-view deep neural network capable of extracting and integrating features from heterogeneous healthcare records. Our model takes in inputs from multiple views and comprises: 1) two subnetworks adapted to extract high quality features from time-series and tabular data views, and 2) a network that combines the two outputs and predicts CRC recurrence. Our model achieves an AUROC score of 0.95, and precision, sensitivity and specificity scores of 0.84, 0.82 and 0.96 respectively, outperforming all-known published results based on the commonly-used CEA prognostic marker, as well as that of most commercially available diagnostic assays. We explain our model's decision by highlighting important features within both data views that contribute to the outcome, using SHAP with a novel workaround that alleviates assumptions on feature independence. Through our work, we hope to contribute to the adoption of AI in healthcare by creating accurate and interpretable models, leading to better post-operative management of CRC patients.