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<p>Neoadjuvant therapy is standard for locally advanced rectal cancer (LARC), yet regimen selection remains population-based, risking over- or undertreatment. We developed and validated a deep learning framework that provides a generalizable paradigm for data-driven treatment regimen selection by estimating patient-specific probabilities of pathological complete response (pCR) across multiple therapeutic options. In a multicenter cohort, a hard-gated mixture-of-experts model integrating pretreatment multiparametric MRI and clinical variables generated regimen-specific pCR probabilities to support clinician-led treatment decision-making. The model achieved strong predictive performance, with AUCs of 0.827 and 0.790 in the validation and prospective test cohorts. In the combined validation and test cohorts, 53.16% of patients were recommended treatment escalation, with an observed pCR rate of 11.11% and a model-estimated pCR probability of 30.95% under the model-supported regimen. Meanwhile, 5.91% of patients were identified for de-intensification while maintaining a high estimated likelihood of response. This framework provides probabilistic support for multidisciplinary optimization of neoadjuvant treatment intensity in LARC. The prospective cohort was registered in the Chinese Clinical Trial Registry (ChiCTR2400085797; June 18, 2024).</p>

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Personalized neoadjuvant treatment regimen selection in locally advanced rectal cancer based on regimen-specific response modeling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月26日

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  • Xiangyu Liu,
  • Yuanling Tang,
  • Song Zhang,
  • Haiyang Bian,
  • Hanlin Shu,
  • Leen Liao,
  • Xiaolin Pang,
  • Qianting Lv,
  • Jia Chen,
  • Peirong Ding,
  • Ping Liu,
  • Yu Shen,
  • Ziqiang Wang,
  • Shouping Zhu,
  • Jie Tian,
  • Zhenyu Liu,
  • Xin Wang

摘要

Neoadjuvant therapy is standard for locally advanced rectal cancer (LARC), yet regimen selection remains population-based, risking over- or undertreatment. We developed and validated a deep learning framework that provides a generalizable paradigm for data-driven treatment regimen selection by estimating patient-specific probabilities of pathological complete response (pCR) across multiple therapeutic options. In a multicenter cohort, a hard-gated mixture-of-experts model integrating pretreatment multiparametric MRI and clinical variables generated regimen-specific pCR probabilities to support clinician-led treatment decision-making. The model achieved strong predictive performance, with AUCs of 0.827 and 0.790 in the validation and prospective test cohorts. In the combined validation and test cohorts, 53.16% of patients were recommended treatment escalation, with an observed pCR rate of 11.11% and a model-estimated pCR probability of 30.95% under the model-supported regimen. Meanwhile, 5.91% of patients were identified for de-intensification while maintaining a high estimated likelihood of response. This framework provides probabilistic support for multidisciplinary optimization of neoadjuvant treatment intensity in LARC. The prospective cohort was registered in the Chinese Clinical Trial Registry (ChiCTR2400085797; June 18, 2024).