Multimodal Integration of Longitudinal Noninvasive Diagnostics for Survival Prediction in Immunotherapy Using Deep Learning

Purpose: Analyzing noninvasive longitudinal and multimodal data using artificial intelligence could potentially transform immunotherapy for cancer patients, paving the way towards precision medicine. Methods: In this study, we integrated pre- and on-treatment blood measurements, prescribed medications and CT-based volumes of organs from a large pan-cancer cohort of 694 patients treated with immunotherapy to predict short and long-term overall survival. By leveraging a combination of recent developments, different variants of our extended multimodal transformer-based simple temporal attention (MMTSimTA) network were trained end-to-end to predict mortality at three, six, nine and twelve months. These models were also compared to baseline methods incorporating intermediate and late fusion based integration methods. Results: The strongest prognostic performance was demonstrated using the extended transformer-based multimodal model with area under the curves (AUCs) of $0.84 \pm $0.04, $0.83 \pm $0.02, $0.82 \pm $0.02, $0.81 \pm $0.03 for 3-, 6-, 9-, and 12-month survival prediction, respectively. Conclusion: Our findings suggest that analyzing integrated early treatment data has potential for predicting survival of immunotherapy patients. Integrating complementary noninvasive modalities into a jointly trained model, using our extended transformer-based architecture, demonstrated an improved multimodal prognostic performance, especially in short term survival prediction.