关键词: 直肠癌, 计算机体层成像机体组分, 骨骼肌指数, 皮下脂肪指数, 列线图模型

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 prognosis, 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 patients 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.

Key words: Rectal cancer, Computed tomography body composition, Skeletal muscle index, Subcutaneous adipose tissue index, Nomogram model