Enhancing Survival Outcomes in Head and Neck Cancer through Joint HPV Classification and Tumor Segmentation

Dalal Chamseddine, Shamimeh Ahrari, Mira Rizkallah, Thomas Carlier, Diana Mateus

Keywords: Multitask learning, Survival prediction, PET/CT, Head and neck cancer

Abstract:

Head and Neck Cancer (HNC) is a broad term for cancers that develop in the head and neck region. Accurate survival prediction is critical for guiding patient management and treatment planning. Traditional survival models, such as Kaplan–Meier curves and Cox Proportional Hazards models, are limited by their dependence on linearity and proportional hazards assumptions. Recently, deep learning–based survival models have demonstrated promising results for risk prediction. However, current approaches still struggle to fully integrate multimodal data and to capture region-specific features effectively. In this work, we employed a Multitask Learning framework that simultaneously performs Human Papillomavirus (HPV) classification, tumor segmentation, and survival prediction based on Positron Emission Tomography, Computed Tomography imaging, and clinical features. Integrating these three tasks into a unified model enables the use of shared feature representations. The segmentation module facilitates the extraction of tumor-specific features, while the classification branch incorporates HPV status prediction as a critical prognostic factor in HNC.
This approach was developed and evaluated as part of our participation in the MICCAI 2025 HEad and neCK TumOR segmentation and
outcome prediction challenge as the SIMS-LIFE team.

Submission Number: 7