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<p>This study evaluated predictive models integrating Computed Tomography(CT) and ultrasound(US) to assess neoadjuvant therapy response in locally advanced gastric cancer (LAGC). A prospective multicenter trial (<i>n</i> = 75) developed five RECIST 1.1-derived submodels (RECIST_Expansion, UC_RECIST, CT_RECIST, V-RECIST, U-RECIST) and an ordinal regression-based nomogram, using pathological tumor regression grade (TRG) as the reference standard. The geometric approximation model V-RECIST exhibited superior diagnostic performance for TRG 0–2 vs. 3 discrimination (area under the curve [AUC] = 0.951, sensitivity 96.8%/specificity 80.1%) and TRG 0–1 vs. 2–3 classification (AUC = 0.868, sensitivity 85.7%/specificity 80.0%), validated by calibration curves and decision curve analysis. The US-only U-RECIST demonstrated good diagnostic accuracy (AUC = 0.906/0.811), suitable for preliminary assessment due to its safety and cost-effectiveness. The remaining models—RECIST_Expansion (lymph node evaluation only), UC_RECIST (CT-US combined protocol with gastric lesions as measurable targets), and CT_RECIST (CT-only for no-US settings)—also showed satisfactory accuracy. The ordinal regression model achieved slightly higher AUC values (0.952/0.870) and cross-validation accuracy (0.73) compared to modified RECIST models. These findings highlight the clinical utility of revised RECIST 1.1 models across diverse scenarios, with V-RECIST demonstrating the highest performance, U-RECIST offering radiation-free assessment and cost-effectiveness, and the ordinal model enhancing visualization while preserving diagnostic superiority.</p>

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Modified RECIST submodels and ordinal regression model predict neoadjuvant chemoimmunotherapy response in locally advanced gastric cancer​​

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月7日

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  • Shu Chen,
  • Shenghong Wei,
  • Zaisheng Ye,
  • Cheng Wei,
  • Sheng Liu,
  • Yi Wang,
  • Yi Zeng,
  • Jinhu Chen,
  • Xiaopeng Wang,
  • Jianping Jiang,
  • Xiaoling Chen,
  • Luchuan Chen

摘要

This study evaluated predictive models integrating Computed Tomography(CT) and ultrasound(US) to assess neoadjuvant therapy response in locally advanced gastric cancer (LAGC). A prospective multicenter trial (n = 75) developed five RECIST 1.1-derived submodels (RECIST_Expansion, UC_RECIST, CT_RECIST, V-RECIST, U-RECIST) and an ordinal regression-based nomogram, using pathological tumor regression grade (TRG) as the reference standard. The geometric approximation model V-RECIST exhibited superior diagnostic performance for TRG 0–2 vs. 3 discrimination (area under the curve [AUC] = 0.951, sensitivity 96.8%/specificity 80.1%) and TRG 0–1 vs. 2–3 classification (AUC = 0.868, sensitivity 85.7%/specificity 80.0%), validated by calibration curves and decision curve analysis. The US-only U-RECIST demonstrated good diagnostic accuracy (AUC = 0.906/0.811), suitable for preliminary assessment due to its safety and cost-effectiveness. The remaining models—RECIST_Expansion (lymph node evaluation only), UC_RECIST (CT-US combined protocol with gastric lesions as measurable targets), and CT_RECIST (CT-only for no-US settings)—also showed satisfactory accuracy. The ordinal regression model achieved slightly higher AUC values (0.952/0.870) and cross-validation accuracy (0.73) compared to modified RECIST models. These findings highlight the clinical utility of revised RECIST 1.1 models across diverse scenarios, with V-RECIST demonstrating the highest performance, U-RECIST offering radiation-free assessment and cost-effectiveness, and the ordinal model enhancing visualization while preserving diagnostic superiority.