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<p>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 (<i>P</i> = 0.016), whereas no significant improvement in OS was observed among patients with a low-risk score (<i>P</i> = 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月23日

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  • Ruxian Tian,
  • Feng Hou,
  • Haicheng Zhang,
  • Guohua Yu,
  • Ping Yang,
  • Jiaxuan Li,
  • Ting Yuan,
  • Xi Chen,
  • Ying Chen,
  • Yan Hao,
  • Yisong Yao,
  • Hongfei Zhao,
  • Pengyi Yu,
  • Han Fang,
  • Liling Song,
  • Anning Li,
  • Zhonglu Liu,
  • Huaiqing Lv,
  • Dexin Yu,
  • Hongxia Cheng,
  • Ning Mao,
  • Xicheng Song

摘要

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.