LAUSR

Objective This study evaluated the association between body composition and clinical parameters and prognosis in patients with colorectal cancer (CRC) treated by radical resection. Methods Baseline data on patient age, body mass index (BMI), bowel obstruction and tumor-related factors were collected retrospectively. Body composition parameters such as visceral fat area (VFA), total abdominal muscle area (TAMA), muscle attenuation (MA), posterior renal fat thickness (PPNF) and intermuscular fat area (IMF) are measured using Computed tomography (CT) scans. We also propose a new predictor of linear skeletal muscle index (LSMI) that can be easily measured clinically at CT. Follow-up endpoints were disease-free survival and all-cause death. We follow up with patients in hospital or by telephone. Univariate and multifactorial Cox proportional hazards analyses were performed to identify risk factors associated with prognosis. Survival analysis was performed using the Kaplan-Meier method and a nomogram was established to predict survival. Results A total of 1761 patients (median age 62 years) with CRC were enrolled in our study, of whom 201 had intestinal obstruction and 673 had a BMI > 24.0. Among all patients, the 3- and 5-year disease-free survival rates were 84.55% and 68.60% respectively, and the overall survival rates were 88.87% and 76.38%. Overall survival was significantly correlated with MA, LSMI, SMI, Tumor size, N stage, metastasis and adjuvant therapy by Cox regression analysis (p < 0.05). The risk of tumor progression was significantly associated with MA, VFA, LSMI, SMI, Male, N stage, metastasis and adjuvant therapy (p < 0.05). In addition, based on the Chinese population, we found that female patients with MA < 30.0 HU, LSMI < 18.2, and SMI < 38.0 had a worse prognosis, male patients with MA < 37.6 HU, LSMI < 21.9, and SMI < 40.3 had a poorer prognosis. Conclusion Our findings suggest that linear skeletal muscle index and MA can be used as new independent predictors for colorectal cancer patients treated with radical surgery, and that baseline data such as body composition parameters, LSMI and tumor-related factors can collectively predict patient prognosis. These results could help us to optimize the management and treatment of patients after surgery.