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Background <p>Early postoperative recurrence in locally advanced gastric cancer (LAGC) severely compromises patient outcomes, yet current predictive models are inadequate due to limited generalizability and insufficient validation. This study aimed to establish and robustly validate a multimodal Radiomics-Clinical Integrated Risk Stratification Assessment (RSA) model to accurately predict early recurrence.</p> Methods <p>We retrospectively analyzed 2,516 LAGC patients from six hospitals across northern and southern China, divided into training, internal validation, and external validation cohorts. Radiomics features were extracted from portal venous-phase abdominal CT images using nnU-Net-based segmentation, followed by a hierarchical feature-selection framework integrating the mRMR algorithm and LASSO regression. A predictive RSA model combining clinical factors and radiomics features was developed. Model robustness was validated internally, externally, prospectively (clinical trial cohort, NCT01516944, n = 569), and further independently tested using a publicly available dataset from The Cancer Imaging Archive (TCIA, n = 41).</p> Results <p>The RSA model exhibited superior performance across all validation cohorts, achieving AUC values of 0.873 (training), 0.871–0.872 (internal validation), 0.870–0.873 (external validation), 0.857 (prospective validation), and 0.850 (TCIA validation). Decision curve analyses confirmed the RSA model provided significant clinical benefits beyond traditional models. Transcriptomic analyses revealed that low-risk patients exhibited enhanced immune infiltration and significant activation of immune-related pathways, including IL6/JAK/STAT3 and interferon signaling. Multivariate Cox regression demonstrated the RSA model independently predicted five-year overall survival across validation cohorts (HR range: 1.830–2.166, all P &lt; 0.001), surpassing standard clinical staging.</p> Conclusions <p>Our RSA model, integrating robust radiomics methodologies and critical clinical parameters, consistently demonstrated high accuracy and clinical applicability in diverse populations, significantly improving prediction of early postoperative recurrence in LAGC. This approach provides novel insights into tumor-immune microenvironment interactions, paving the way toward personalized postoperative management strategies.</p>

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Radiomics-based ensemble model predicts postoperative recurrence of gastric cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月25日

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  • Ping’an Ding,
  • Sheng Chen,
  • Honghai Guo,
  • Jiaxuan Yang,
  • Qiang Fu,
  • Baoquan Wang,
  • Renjun Gu,
  • Lilong Zhang,
  • Jiaxiang Wu,
  • Haotian Wu,
  • Ning Meng,
  • Xiaolong Li,
  • Zhenjiang Guo,
  • Yuan Tian,
  • Lingjiao Meng,
  • Qun Zhao

摘要

Background

Early postoperative recurrence in locally advanced gastric cancer (LAGC) severely compromises patient outcomes, yet current predictive models are inadequate due to limited generalizability and insufficient validation. This study aimed to establish and robustly validate a multimodal Radiomics-Clinical Integrated Risk Stratification Assessment (RSA) model to accurately predict early recurrence.

Methods

We retrospectively analyzed 2,516 LAGC patients from six hospitals across northern and southern China, divided into training, internal validation, and external validation cohorts. Radiomics features were extracted from portal venous-phase abdominal CT images using nnU-Net-based segmentation, followed by a hierarchical feature-selection framework integrating the mRMR algorithm and LASSO regression. A predictive RSA model combining clinical factors and radiomics features was developed. Model robustness was validated internally, externally, prospectively (clinical trial cohort, NCT01516944, n = 569), and further independently tested using a publicly available dataset from The Cancer Imaging Archive (TCIA, n = 41).

Results

The RSA model exhibited superior performance across all validation cohorts, achieving AUC values of 0.873 (training), 0.871–0.872 (internal validation), 0.870–0.873 (external validation), 0.857 (prospective validation), and 0.850 (TCIA validation). Decision curve analyses confirmed the RSA model provided significant clinical benefits beyond traditional models. Transcriptomic analyses revealed that low-risk patients exhibited enhanced immune infiltration and significant activation of immune-related pathways, including IL6/JAK/STAT3 and interferon signaling. Multivariate Cox regression demonstrated the RSA model independently predicted five-year overall survival across validation cohorts (HR range: 1.830–2.166, all P < 0.001), surpassing standard clinical staging.

Conclusions

Our RSA model, integrating robust radiomics methodologies and critical clinical parameters, consistently demonstrated high accuracy and clinical applicability in diverse populations, significantly improving prediction of early postoperative recurrence in LAGC. This approach provides novel insights into tumor-immune microenvironment interactions, paving the way toward personalized postoperative management strategies.