Scholarlink

Background <p>In diabetic gastric cancer patients, body composition (skeletal muscle–to–fat ratio, MFR) may influence surgical outcomes. We evaluated whether Photon-counting CT (PCD-CT) derived MFR predicts major postoperative complications, reflecting its value in perioperative risk stratification.</p> Methods <p>A retrospective analysis of 134 gastric cancer patients with type 2 diabetes was conducted. Preoperative PCD-CT scans assessed body composition. Logistic regression models identified predictors of poor postoperative outcomes, defined by major postoperative complications. The predictive accuracy of models incorporating clinical variables and MFR was evaluated using receiver operating characteristic curves, integrated Discrimination Improvement (IDI), and net Reclassification Improvement (NRI).</p> Results <p>Patients who developed major complications (<i>n</i> = 35) had significantly lower skeletal muscle area (45.5 vs. 56.2&#xa0;cm²; <i>P</i> &lt; 0.01) and higher fat accumulation. Abnormal MFR (0.34–0.57)was a strong predictor of poor outcomes (OR = 1.94, 95% CI: 1.17–2.58, <i>p</i> &lt; 0.01) compared to patients without complications (<i>n</i> = 99). The model combining clinical variables with MFR had the best performance (AUC = 0.75, sensitivity = 0.74, specificity = 0.71) in predicting major complications, outperforming a model based solely on clinical factors. It also showed substantial improvements in predictive accuracy, with an NRI of 0.52 (<i>p</i> &lt; 0.01) and an IDI of 0.09 (<i>p</i> &lt; 0.01).</p> Conclusion <p>MFR, quantified by PCD-CT, is a reliable and accurate biomarker for identifying diabetic gastric cancer patients at higher risk of major postoperative complications. MFR demonstrates strong predictive value for adverse surgical outcomes, reinforcing its role in perioperative risk stratification.</p> Clinical trial number <p>Not applicable.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing risk stratification in diabetic gastric cancer: muscle-fat ratio from photon-counting CT as a predictor of postoperative complications

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月19日

查看全文
  • Shuangxiang Lin,
  • Yuchen Jin,
  • Mengxi Xu,
  • Shuyue Wang,
  • Weisheng Yao,
  • Jiaxing Wu,
  • Xinhong Wang,
  • Jianzhong Sun

摘要

Background

In diabetic gastric cancer patients, body composition (skeletal muscle–to–fat ratio, MFR) may influence surgical outcomes. We evaluated whether Photon-counting CT (PCD-CT) derived MFR predicts major postoperative complications, reflecting its value in perioperative risk stratification.

Methods

A retrospective analysis of 134 gastric cancer patients with type 2 diabetes was conducted. Preoperative PCD-CT scans assessed body composition. Logistic regression models identified predictors of poor postoperative outcomes, defined by major postoperative complications. The predictive accuracy of models incorporating clinical variables and MFR was evaluated using receiver operating characteristic curves, integrated Discrimination Improvement (IDI), and net Reclassification Improvement (NRI).

Results

Patients who developed major complications (n = 35) had significantly lower skeletal muscle area (45.5 vs. 56.2 cm²; P < 0.01) and higher fat accumulation. Abnormal MFR (0.34–0.57)was a strong predictor of poor outcomes (OR = 1.94, 95% CI: 1.17–2.58, p < 0.01) compared to patients without complications (n = 99). The model combining clinical variables with MFR had the best performance (AUC = 0.75, sensitivity = 0.74, specificity = 0.71) in predicting major complications, outperforming a model based solely on clinical factors. It also showed substantial improvements in predictive accuracy, with an NRI of 0.52 (p < 0.01) and an IDI of 0.09 (p < 0.01).

Conclusion

MFR, quantified by PCD-CT, is a reliable and accurate biomarker for identifying diabetic gastric cancer patients at higher risk of major postoperative complications. MFR demonstrates strong predictive value for adverse surgical outcomes, reinforcing its role in perioperative risk stratification.

Clinical trial number

Not applicable.