Multimodal fusion model for prognostic prediction and radiotherapy response assessment in head and neck squamous cell carcinoma.
Authors
Affiliations (16)
Affiliations (16)
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.
- Department of Pathology, Affiliated Hospital of Qingdao University, Qingdao, China.
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
- Department of Pathology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.
- The Second School of Clinical Medicine of Binzhou Medical University, Yantai, China.
- Shandong University of Traditional Chinese Medicine, Jinan, China.
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China.
- Linyi People's Hospital Affiliated to Shandong Second Medical University, Linyi, China. [email protected].
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China. [email protected].
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China. [email protected].
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China. [email protected].
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China. [email protected].
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China. [email protected].
- Shandong Provincial Key Laboratory of Neuroimmune Interaction and Regulation, Yantai, China. [email protected].
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China. [email protected].
- Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai, China. [email protected].
Abstract
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.