Scholarlink

Objective <p>This study aimed to construct a nomogram based on body composition parameters and the prognostic nutritional index (PNI) using quantitative computed tomography (QCT) to predict early postoperative complications in patients with colorectal cancer (CRC).</p> Materials and methods <p>We retrospectively analyzed the data of 157 patients who underwent radical resection for CRC between January 2019 and April 2024. All patients underwent QCT 1 month prior to surgery. Body composition was assessed at the level of the third lumbar vertebra, including measurements of the visceral fat area, subcutaneous fat area, and intramuscular fat infiltration (MFI) of the posterior vertebral muscles. The visceral-to-subcutaneous fat ratio (VSR) was calculated.</p> Results <p>Among the 157 patients, 31 (19.7%) experienced early postoperative complications. Univariate analysis revealed that the PNI, albumin level, VSR, and MFI were significantly associated with these complications. Multivariate logistic regression analysis identified the PNI (odds ratio [OR] = 0.801; 95% confidence interval (CI): 0.653–0.983), VSR (OR = 3.084; 95% CI: 1.365–6.968), and MFI (OR = 1.074; 95% CI: 1.009–1.145) as independent risk factors for early postoperative complications in CRC. The areas under the receiver operating characteristic curves for the PNI, VSR, MFI, and nomogram model for predicting postoperative complications were 0.796, 0.798, 0.648, and 0.879, respectively. Based on these three independent risk factors, the nomogram demonstrated good discrimination, calibration, goodness of fit, and clinical utility.</p> Conclusions <p>The nomogram model utilizing QCT-based body composition metrics and the PNI exhibited strong predictive capability for early postoperative complications in patients with CRC.</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

A nomogram based on body composition and the prognostic nutritional index to predict early postoperative complications of colorectal cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月16日

查看全文
  • Ning Zhu,
  • Yan Liu,
  • Hongqing Yu,
  • Jingya Xu,
  • Yi Wei,
  • Jian Zhai

摘要

Objective

This study aimed to construct a nomogram based on body composition parameters and the prognostic nutritional index (PNI) using quantitative computed tomography (QCT) to predict early postoperative complications in patients with colorectal cancer (CRC).

Materials and methods

We retrospectively analyzed the data of 157 patients who underwent radical resection for CRC between January 2019 and April 2024. All patients underwent QCT 1 month prior to surgery. Body composition was assessed at the level of the third lumbar vertebra, including measurements of the visceral fat area, subcutaneous fat area, and intramuscular fat infiltration (MFI) of the posterior vertebral muscles. The visceral-to-subcutaneous fat ratio (VSR) was calculated.

Results

Among the 157 patients, 31 (19.7%) experienced early postoperative complications. Univariate analysis revealed that the PNI, albumin level, VSR, and MFI were significantly associated with these complications. Multivariate logistic regression analysis identified the PNI (odds ratio [OR] = 0.801; 95% confidence interval (CI): 0.653–0.983), VSR (OR = 3.084; 95% CI: 1.365–6.968), and MFI (OR = 1.074; 95% CI: 1.009–1.145) as independent risk factors for early postoperative complications in CRC. The areas under the receiver operating characteristic curves for the PNI, VSR, MFI, and nomogram model for predicting postoperative complications were 0.796, 0.798, 0.648, and 0.879, respectively. Based on these three independent risk factors, the nomogram demonstrated good discrimination, calibration, goodness of fit, and clinical utility.

Conclusions

The nomogram model utilizing QCT-based body composition metrics and the PNI exhibited strong predictive capability for early postoperative complications in patients with CRC.