Predictive value of preoperative clinical parameters for early recurrence after radical resection of colorectal cancer liver metastases

Objective To investigate the risk factors influencing early recurrence after radical resection in patients with colorectal cancer liver metastases (CRLM) and to develop a predictive model. Methods A retrospective analysis was conducted on the clinical data of CRLM patients who underwent radical resection from January 2012 to June 2023. Logistic regression and Lasso regression analysis were utilized to explore the risk factors for early recurrence, and a nomogram was constructed based on the risk factors. Assessing model discrimination using area under the curve (AUC) of the receiver operating characteristic curve, plotting calibration curves, and clinical decision curves to assess model calibration and clinical utility. Results The study included 242 patients, who were randomly divided into a training cohort (169 patients) and a validation cohort (73 patients). The Logistic and Lasso regression analyses identified preoperative systemic immune-inflammation index (SII) ≥470.1[OR=2.96, 95%CI (1.14, 7.67)], prognostic nutritional index (PNI) <43.5[OR=5.91, 95%CI (1.41, 24.84)], albumin-globulin ratio (AGR) <1.3[OR=7.62, 95%CI (2.78, 20.90)], carcinoembryonic antigen (CEA) ≥5.8 ng/ mL [OR=2.93, 95%CI (1.09, 7.86)], and bilobar distribution of liver metastases[OR=3.66, 95%CI (1.40, 9.57)] as independent risk factors for early recurrence. The nomogram developed from these findings demonstrated good discriminative ability with AUCs of 0.884 in the training cohort and 0.869 in the validation cohort. The calibration curve indicated that the nomogram model exhibited excellent calibration, and the clinical decision curve analysis suggested strong clinical utility. Conclusion Preoperative SII≥470.1, PNI<43.5, AGR<1.3, CEA≥5.8 ng/mL, and bilobar distribution of liver metastases are significant independent risk factors for early recurrence in CRLM patients. The nomogram constructed based on these factors can effectively predict early recurrence in postoperative patients.