Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks
This work provides an incremental improvement in cancer survival prediction for head and neck cancer patients by integrating multi-modal data and addressing competing risks.
This paper addresses cancer survival prediction using multi-modal clinical and imaging data, specifically for head and neck cancer patients. The proposed Deep-CR MTLR model, which combines neural networks and an extended multi-task logistic regression, achieved a 2-year AUROC of 0.774 and a C-index of 0.788 for cancer-specific survival in a cohort of 2552 patients, outperforming single-modality predictors.
Accurate survival prediction is crucial for development of precision cancer medicine, creating the need for new sources of prognostic information. Recently, there has been significant interest in exploiting routinely collected clinical and medical imaging data to discover new prognostic markers in multiple cancer types. However, most of the previous studies focus on individual data modalities alone and do not make use of recent advances in machine learning for survival prediction. We present Deep-CR MTLR -- a novel machine learning approach for accurate cancer survival prediction from multi-modal clinical and imaging data in the presence of competing risks based on neural networks and an extension of the multi-task logistic regression framework. We demonstrate improved prognostic performance of the multi-modal approach over single modality predictors in a cohort of 2552 head and neck cancer patients, particularly for cancer specific survival, where our approach achieves 2-year AUROC of 0.774 and $C$-index of 0.788.