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<p>Early assessment of treatment response is essential for optimizing cancer management, as it allows timely interventions during the course of therapy, potentially improving cancer control and clinical outcomes. In this study, we aimed to develop and validate a machine learning model integrating radiomics, dosiomics, and clinical characteristics to predict response to radiotherapy in patients with locally advanced, unresectable non-small cell lung cancer (NSCLC). A total of 222 patients across multiple centers who received radiotherapy for NSCLC were enrolled and divided into training (<i>n</i> = 110), internal validation (<i>n</i> = 28), and external validation (<i>n</i> = 84) cohorts. Objective response rate (ORR) and progression-free survival (PFS) were predicted using models based on radiomics, dosiomics, and clinical characteristics. Model performance was evaluated using receiver operating characteristic (ROC) curves, DeLong test, decision curve analysis (DCA), Kaplan-Meier survival analysis, and Integrated Brier Score (IBS). The clinical models, C<sub>ORR</sub> and C<sub>PFS</sub> (both based on planning target volume [PTV] and lymphocyte count) were compared with combined radiomics–dosiomics–clinical models (RDC<sub>ORR</sub> and RDC<sub>PFS</sub>). For ORR prediction, RDC<sub>ORR</sub> achieved AUCs of 0.901, 0.894, and 0.869 in the training, internal validation, and external validation cohorts, outperforming C<sub>ORR</sub> (AUCs of 0.723, 0.606, and 0.723), with <i>p</i> &lt; 0.05. DCA indicated that RDC<sub>ORR</sub> outperformed C<sub>ORR</sub>, providing a higher overall net benefit. For PFS prediction, RDC<sub>PFS</sub> yielded higher concordance indices (0.805, 0.730, and 0.743 in the training, internal validation, and external validation cohorts) than C<sub>PFS</sub> (0.679, 0.699, and 0.640, respectively). RDC<sub>PFS</sub> showed the lowest IBS across all cohorts (0.092, 0.107, and 0.093, respectively) compared with C<sub>PFS</sub> and the reference model, indicating better predictive accuracy. The combined model integrating radiomics, dosiomics, and clinical characteristics enhances the prediction of radiotherapy response in locally advanced, unresectable NSCLC, facilitating improved patient monitoring and more effective adjuvant clinical trial design.</p>

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Development and validation of a radiomics-dosiomics model for predicting radiotherapy response in locally advanced, unresectable non-small cell lung cancer: a multi-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月15日

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  • Xun Wang,
  • Xiufen Sun,
  • Yueqin Chen,
  • Guqing Zhang,
  • Shucheng Ye,
  • Aiping Zhang,
  • Huipeng Yang,
  • Zhanguo Sun,
  • Shuang Ge

摘要

Early assessment of treatment response is essential for optimizing cancer management, as it allows timely interventions during the course of therapy, potentially improving cancer control and clinical outcomes. In this study, we aimed to develop and validate a machine learning model integrating radiomics, dosiomics, and clinical characteristics to predict response to radiotherapy in patients with locally advanced, unresectable non-small cell lung cancer (NSCLC). A total of 222 patients across multiple centers who received radiotherapy for NSCLC were enrolled and divided into training (n = 110), internal validation (n = 28), and external validation (n = 84) cohorts. Objective response rate (ORR) and progression-free survival (PFS) were predicted using models based on radiomics, dosiomics, and clinical characteristics. Model performance was evaluated using receiver operating characteristic (ROC) curves, DeLong test, decision curve analysis (DCA), Kaplan-Meier survival analysis, and Integrated Brier Score (IBS). The clinical models, CORR and CPFS (both based on planning target volume [PTV] and lymphocyte count) were compared with combined radiomics–dosiomics–clinical models (RDCORR and RDCPFS). For ORR prediction, RDCORR achieved AUCs of 0.901, 0.894, and 0.869 in the training, internal validation, and external validation cohorts, outperforming CORR (AUCs of 0.723, 0.606, and 0.723), with p < 0.05. DCA indicated that RDCORR outperformed CORR, providing a higher overall net benefit. For PFS prediction, RDCPFS yielded higher concordance indices (0.805, 0.730, and 0.743 in the training, internal validation, and external validation cohorts) than CPFS (0.679, 0.699, and 0.640, respectively). RDCPFS showed the lowest IBS across all cohorts (0.092, 0.107, and 0.093, respectively) compared with CPFS and the reference model, indicating better predictive accuracy. The combined model integrating radiomics, dosiomics, and clinical characteristics enhances the prediction of radiotherapy response in locally advanced, unresectable NSCLC, facilitating improved patient monitoring and more effective adjuvant clinical trial design.