Kavli Affiliate: Jing Wang
| First 5 Authors: Meixu Chen, Kai Wang, Jing Wang, ,
| Summary:
A comprehensive and reliable survival prediction model is of great importance
to assist in the personalized management of Head and Neck Cancer (HNC) patients
treated with curative Radiation Therapy (RT). In this work, we propose IMLSP,
an Interpretable Multi-Label multi-modal deep Survival Prediction framework for
predicting multiple HNC survival outcomes simultaneously and provide time-event
specific visual explanation of the deep prediction process. We adopt Multi-Task
Logistic Regression (MTLR) layers to convert survival prediction from a
regression problem to a multi-time point classification task, and to enable
predicting of multiple relevant survival outcomes at the same time. We also
present Grad-TEAM, a Gradient-weighted Time-Event Activation Mapping approach
specifically developed for deep survival model visual explanation, to generate
patient-specific time-to-event activation maps. We evaluate our method with the
publicly available RADCURE HNC dataset, where it outperforms the corresponding
single-modal models and single-label models on all survival outcomes. The
generated activation maps show that the model focuses primarily on the tumor
and nodal volumes when making the decision and the volume of interest varies
for high- and low-risk patients. We demonstrate that the multi-label learning
strategy can improve the learning efficiency and prognostic performance, while
the interpretable survival prediction model is promising to help understand the
decision-making process of AI and facilitate personalized treatment.
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