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Objectives <p>Proliferation marker Ki-67 and immune checkpoint Programmed Cell Death Ligand 1 (PD-L1) are both proteins associated with tumor treatment response. To develop and validate two dynamic nomograms from dual-energy CT (DECT) for noninvasive evaluation of the expression status of these two proteins in advanced gastric cancer (AGC), and explore their associations with treatment response.</p> Materials and methods <p>In this multicenter prospective and retrospective study, eligible AGC participants who underwent DECT scans were recruited into two study groups (Ki-67 and PD-L1 groups), with their clinical and DECT characteristics collected and measured. The prediction models (Ki-67 dynamic nomogram (KIDN) and PD-L1 dynamic nomogram (PDDN)) were constructed by logistic regression classifier, respectively, and two online calculators were established based on models. The performance of nomograms was comprehensively evaluated.</p> Results <p>In total, 459 and 417 eligible patients were analyzed in the Ki-67 and PD-L1 groups, respectively. The nomograms discriminating Ki-67 and PD-L1 achieved AUCs of 0.755 and 0.726, respectively, in the external validation cohort. Additionally, the two nomograms significantly or slightly outperformed any single predictor in both the training and validation cohorts. In both cohorts, KIDN and PDDN showed favorable calibration (Hosmer–Lemeshow test, all <i>p</i> &lt; 0.05) and clinical utility confirmed by decision curve analysis. Moreover, they demonstrated prognostic performance consistent with actual biomarkers and were associated with clinical response to immuno-chemotherapy (both <i>p</i> &lt; 0.05).</p> Conclusion <p>The dynamic nomograms, which integrate clinical features and DECT quantitative parameters, enable noninvasive evaluation of treatment-related protein expression in AGC.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis><i> Noninvasive tools for accurately evaluating Ki-67 and PD-L1 expression status in advanced gastric cancer (AGC) are currently inadequate</i>.</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis><i> Dual-energy CT-based nomograms demonstrated favorable performance in evaluating Ki-67 and PD-L1 expression status and were associated with response to immuno-chemotherapy in AGC</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis><i> We developed online calculators based on prediction models that may serve as a supplement to current invasive methods (e.g., immunohistochemistry), potentially aiding therapeutic decision-making</i>.</p> Graphical Abstract <p></p>

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Noninvasive evaluation of treatment-related protein expression status in advanced gastric cancer using dynamic nomograms with dual-energy CT: a multicenter study

  • 期刊论文

  • 发表日期：

    2025年8月13日

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  • Yiyang Liu,
  • Yaru You,
  • Lihong Chen,
  • Jing Li,
  • Mengchen Yuan,
  • Bo Duan,
  • Huiting Ge,
  • Xuejun Chen,
  • Li Yang,
  • Xingzhi Chen,
  • Linfeng Li,
  • Luhao Liu,
  • Yue Zheng,
  • Yiming Li,
  • Bei Dong,
  • Qian Jia,
  • Chengwei Ruan,
  • Diansen Chen,
  • Zongbin Hou,
  • Zihao Zhao,
  • Qingyu Ji,
  • Jianbo Gao

摘要

Objectives

Proliferation marker Ki-67 and immune checkpoint Programmed Cell Death Ligand 1 (PD-L1) are both proteins associated with tumor treatment response. To develop and validate two dynamic nomograms from dual-energy CT (DECT) for noninvasive evaluation of the expression status of these two proteins in advanced gastric cancer (AGC), and explore their associations with treatment response.

Materials and methods

In this multicenter prospective and retrospective study, eligible AGC participants who underwent DECT scans were recruited into two study groups (Ki-67 and PD-L1 groups), with their clinical and DECT characteristics collected and measured. The prediction models (Ki-67 dynamic nomogram (KIDN) and PD-L1 dynamic nomogram (PDDN)) were constructed by logistic regression classifier, respectively, and two online calculators were established based on models. The performance of nomograms was comprehensively evaluated.

Results

In total, 459 and 417 eligible patients were analyzed in the Ki-67 and PD-L1 groups, respectively. The nomograms discriminating Ki-67 and PD-L1 achieved AUCs of 0.755 and 0.726, respectively, in the external validation cohort. Additionally, the two nomograms significantly or slightly outperformed any single predictor in both the training and validation cohorts. In both cohorts, KIDN and PDDN showed favorable calibration (Hosmer–Lemeshow test, all p < 0.05) and clinical utility confirmed by decision curve analysis. Moreover, they demonstrated prognostic performance consistent with actual biomarkers and were associated with clinical response to immuno-chemotherapy (both p < 0.05).

Conclusion

The dynamic nomograms, which integrate clinical features and DECT quantitative parameters, enable noninvasive evaluation of treatment-related protein expression in AGC.

Key Points

Question Noninvasive tools for accurately evaluating Ki-67 and PD-L1 expression status in advanced gastric cancer (AGC) are currently inadequate.

Findings Dual-energy CT-based nomograms demonstrated favorable performance in evaluating Ki-67 and PD-L1 expression status and were associated with response to immuno-chemotherapy in AGC.

Clinical relevance We developed online calculators based on prediction models that may serve as a supplement to current invasive methods (e.g., immunohistochemistry), potentially aiding therapeutic decision-making.

Graphical Abstract