Predictive models for colorectal cancer recurrence using multi-modal healthcare data

Colorectal cancer recurrence is a major clinical problem - around 30-40% of patients who are treated with curative intent surgery will experience cancer relapse. Proactive prognostication is critical for early detection and treatment of recurrence. However, the common clinical approach to monitoring recurrence through testing for carcinoembryonic antigen (CEA) does not possess a strong prognostic performance. In our paper, we study a series of machine and deep learning architectures that exploit heterogeneous healthcare data to predict colorectal cancer recurrence. In particular, we demonstrate three different approaches to extract and integrate features from multiple modalities including longitudinal as well as tabular clinical data. Our best model employs a hybrid architecture that takes in multi-modal inputs and comprises: 1) a Transformer model carefully modified to extract high-quality features from time-series data, and 2) a Multi-Layered Perceptron (MLP) that learns tabular data features, followed by feature integration and classification for prediction of recurrence. It achieves an AUROC score of 0.95, as well as precision, sensitivity and specificity scores of 0.83, 0.80 and 0.96 respectively, surpassing the performance of all-known published results based on CEA, as well as most commercially available diagnostic assays. Our results could lead to better post-operative management and follow-up of colorectal cancer patients.