Multimodal fusion model for prognostic prediction and radiotherapy response assessment in head and neck squamous cell carcinoma

作者：Ruxian Tian, Feng Hou, Haicheng Zhang, Guohua Yu, Ping Yang, J. Li, Ting Yuan, Xi Chen, Y.-Z. Chen, Yan Hao, Yisong Yao, Hongfei Zhao, Pengyi Yu, Fang Han, Liling Song, Anning Li, Zhonglu Liu, Huaiqing Lv, Dexin Yu, Hongxia Cheng, Ning Mao, Xicheng Song · 发表于：npj Digital Medicine · 年份：2025 · DOI：10.1038/s41746-025-01712-0 · 被引用次数：18 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、Head and Neck Cancer Studies

Accurate prediction of prognosis and postoperative radiotherapy response is critical for personalized treatment in head and neck squamous cell carcinoma (HNSCC). We developed a multimodal deep learning model (MDLM) integrating computed tomography, whole-slide images, and clinical features from 1087 HNSCC patients across multiple centers. The MDLM exhibited good performance in predicting overall survival (OS) and disease-free survival in external test cohorts. Additionally, the MDLM outperformed unimodal models. Patients with a high-risk score who underwent postoperative radiotherapy exhibited prolonged OS compared to those who did not (P = 0.016), whereas no significant improvement in OS was observed among patients with a low-risk score (P = 0.898). Biological exploration indicated that the model may be related to changes in the cytochrome P450 metabolic pathway, tumor microenvironment, and myeloid-derived cell subpopulations. Overall, the MDLM effectively predicts prognosis and postoperative radiotherapy response, offering a promising tool for personalized HNSCC therapy.