The predictive value and model establishment of body composition in the long-term prognosis of patients after rectal cancer surgery

Shuo LIU; Yun LU; Jilin HU; Wenchang YANG; Rizhi ZHAO; Wenda XU; Hanyu YANG; Zechen LU; Zheng MA; Zhaolin DU; Yunzhi GAO; Yuan GAO

doi:10.19401/j.cnki.1007-3639.2025.07.006

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The predictive value and model establishment of body composition in the long-term prognosis of patients after rectal cancer surgery

Specialist's Article | 更新时间：2025-12-31

- The predictive value and model establishment of body composition in the long-term prognosis of patients after rectal cancer surgery
- China Oncology Vol. 35, Issue 7, Pages: 672-684(2025)
- 作者机构：
  
  1. 青岛大学附属医院胃肠外科，山东青岛 266000
  2. 日照市中医医院胃肠外科，山东日照 276800
- 作者简介：
- 基金信息：
  
  General Program of Shandong Provincial Natural Science Foundation(ZR2022MH252)
- DOI：10.19401/j.cnki.1007-3639.2025.07.006
  CLC： R735.3+7
- Received：21 May 2025，
  
  Revised：2025-07-16，
  
  Published：30 July 2025
- 稿件说明：
移动端阅览
Shuo LIU, Yun LU, Jilin HU, et al. The predictive value and model establishment of body composition in the long-term prognosis of patients after rectal cancer surgery[J]. China Oncology, 2025, 35(7): 672-684.
DOI：

Shuo LIU, Yun LU, Jilin HU, et al. The predictive value and model establishment of body composition in the long-term prognosis of patients after rectal cancer surgery[J]. China Oncology, 2025, 35(7): 672-684. DOI： 10.19401/j.cnki.1007-3639.2025.07.006.

摘要

背景与目的：

既往研究探讨了骨骼肌和脂肪组织的组成和分布对结直肠癌患者预后预测的意义，但多未对直肠癌与结肠癌患者群体加以区分。本研究旨在探究机体组分与直肠癌患者长期预后的关系，并建立术后预测模型。

方法：

回顾性收集2018年1月—2021年12月于青岛大学附属医院接受手术治疗的直肠癌患者的临床资料。纳入标准：① 年龄≥18岁；② 术前经肠镜检查和活组织病理学检查确诊为直肠癌；③ 行外科手术完整切除者；④ 术前1个月内行腹部计算机体层成像（computed tomography，CT）扫描。排除标准：① 临床资料缺失；② 肿瘤多发转移；③ 肿瘤T分期为0或为原位癌；④ 严重伪影导致CT成像质量差，难以区分脂肪和肌肉；⑤ 无法获得随访结果的患者。本研究已获得青岛大学附属医院医学伦理委员会批准（批件号：QYFYWZLL30313），伦理审批环节已豁免知情同意。通过CT扫描观察到的骨骼肌横截面积及皮下脂肪横截面积除以身高的平方得到骨骼肌指数（skeletal muscle index，SMI）和皮下脂肪指数（subcutaneous adipose tissue index，SATI）。采用单因素和多因素COX回归分析确定影响直肠癌患者无复发生存期（recurrence-free survival，RFS）及总生存期（overall survival，OS）的危险因素。根据多因素分析结果，构建列线图预测模型，并通过受试者工作特征（receiver operating characteristic，ROC）曲线、校准曲线和决策曲线（decision curve analysis，DCA）对列线图的预测能力和准确性进行评估，并进行内部验证。

结果：

共有696例患者被纳入本研究，96例（13.8%）患者出现术后复发，89例（12.8%）患者死亡。多因素COX回归分析显示，SMI、SATI、肿瘤T分期、N分期是影响患者术后RFS和OS的独立因素。基于以上独立预测因素分别构建直肠癌患者的RFS及OS的列线图预测模型，3、4和5年RFS的ROC曲线的曲线下面积（area under curve，AUC）分别为0.862、0.846和0.824；3、4和5年OS的AUC分别为0.886、0.898和0.875，通过校准曲线和DCA对模型进行评估，并进行内部验证，显示模型的预测准确性较好。

结论：

CT机体组分是直肠癌患者RFS和OS的独立预测因素，在此基础上开发的列线图模型对直肠癌患者预后有较好的预测价值。

Abstract

Background and Purpose:

Previous studies have investigated the prognostic significance of skeletal muscle and adipose tissue composition and distribution in colorectal cancer patients

yet most have not differentiated between rectal and colon cancer patient cohorts. This study aimed to explore the relationship between body composition and long-term prognos

and to develop a postoperative predictive model.

Methods:

Clinical data of rectal cancer patients who underwent surgical treatment at Qingdao University Affiliated Hospital from January 2018 to December 2021 were retrospectively collected. Inclusion criteria: ① Age

18 years; ② Preoperative colonoscopy and pathological diagnosis of colorectal cancer; ③ Complete surgical resection; ④ Abdominal computed tomography (CT) scan 1 month before surgery. Exclusion criteria: ① Clinical data is missing; ② Multiple metastases of tumors; ③ Tumor T stage 0 or carcinoma in situ; ④ Severe artifacts lead to poor quality CT imaging

making it difficult to distinguish between fat and muscle; ⑤ Inability to obtain follow-up results. This study has been approved by the Medical Ethics Committee of the Affiliated Hospital of Qingdao University (approval number: QYFYWZLL30313)

and informed consent has been waived in the ethical approval process. The skeletal muscle index (SMI) and subcutaneous adipose tissue index (SATI) were calculated by dividing the areas of skeletal muscle and subcutaneous fat observed on CT scans by the square of the patient's height. Univariate and multivariate COX regression analyses were conducted to identify risk factors influencing recurrence-free survival (RFS) and overall survival (OS) in rectal cancer patients. Based on the results of the multivariate analysis

a nomogram prediction model was developed

its predictive power and accuracy were assessed using the receiver operating characteristic (ROC) curve

calibration plots and decision curve analysis (DCA)

and internal validation was conducted.

Results:

A total of 696 patients were included in this study

with 96 (13.8%) patients experiencing postoperative recurrence and 89 (12.8%) patients dying. Multivariate COX regression analysis showed that SMI

SATI

tumor T stage and N stage were independent factors affecting the postoperative RFS and OS of patients. Nomogram prediction models for RFS and OS in rectal cancer patient

s were constructed based on the above independent predictors. The area under ROC curve (AUC) for 3-

4- and 5-year RFS was 0.862

0.846 and 0.824

respectively; the AUC for 3-

4- and 5-year OS was 0.886

0.898 and 0.875

respectively. The models were evaluated using calibration curves and decision curves

and internal validation was performed

which showed that the prediction accuracy of the models was good.

Conclusion:

CT body composition is an independent predictor of RFS and OS in rectal cancer patients

and the nomogram model developed based on these factors demonstrates good predictive value for patient prognosis.

关键词

Keywords

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