Multimodal Integration of Longitudinal Noninvasive Diagnostics for Survival Prediction in Immunotherapy Using Deep Learning
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2024
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Abstract
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.
| Reference Key |
gerven2024multimodal
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|---|---|
| Authors | Melda Yeghaian; Zuhir Bodalal; Daan van den Broek; John B A G Haanen; Regina G H Beets-Tan; Stefano Trebeschi; Marcel A J van Gerven |
| Journal | arXiv |
| Year | 2024 |
| DOI |
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