Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretation

TL;DR

This paper introduces IMLSP, a multi-modal deep learning framework for predicting multiple head and neck cancer survival outcomes simultaneously, with visual explanations to aid interpretability and personalized treatment planning.

Contribution

It presents a novel multi-label survival prediction model with interpretability via Grad-TEAM, improving prognostic accuracy and understanding of AI decision processes in HNC treatment.

Findings

Outperforms single-modal and single-label models on all survival outcomes.

Generates patient-specific activation maps highlighting tumor regions.

Multi-label learning enhances prognostic performance and learning efficiency.

Abstract

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.