The occurrence of metachronous liver metastasis (MLM) after primary resection of colorectal cancer (CRC) significantly worsens patient prognosis. Identification of patients at high risk of MLM would enable closer monitoring and more timely intervention. We therefore aimed to develop and validate a novel nomogram to predict MLM in patients with CRC, based on preoperative inflammatory indicators and postoperative clinicopathological features. Data from 505 patients with CRC who underwent radical resection of the primary tumor at the First Affiliated Hospital of Zhengzhou University between August 2018 and August 2022 were retrospectively analyzed. The patients were divided into training ( n = 289) and validation ( n = 216) cohorts. Independent risk factors for MLM were identified using least absolute shrinkage and selection operator and multivariate logistic regression analyses of the training cohort and used to develop a novel nomogram. To quantify the incremental predictive value of inflammatory markers, three models were constructed using logistic regression: a pathological model (T stage, N stage, vascular invasion, mismatch repair status, and carcinoembryonic antigen), an inflammatory model (neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio), and a combined nomogram including all eight predictors. The discriminative performance of the three models was compared using area under the receiver operating characteristic curve (AUC), the DeLong test, calibration curves, decision curve analysis, and reclassification metrics (net reclassification improvement and integrated discrimination improvement). Sensitivity analyses modeling inflammatory markers as continuous variables were performed. A preoperative carcinoembryonic antigen (CEA) level > 5 ng/mL, neutrophil-to-lymphocyte ratio (NLR) > 2.35, platelet-to-lymphocyte ratio (PLR) > 127.29, and lymphocyte-to-monocyte ratio (LMR) > 3.80, and the postoperative clinicopathological features T stage, N stage, vascular invasion, and mismatch repair (MMR) proficient were identified as independent risk factors for MLM of CRC and used to develop the nomogram. In the three-model comparison, the combined nomogram achieved the highest discriminative performance (training AUC 0.937, validation AUC 0.777), significantly outperforming the pathological model (training AUC 0.911, DeLong P = 0.012; validation AUC 0.735, DeLong P = 0.037) and the inflammatory model (training AUC 0.838, DeLong P < 0.001; validation AUC 0.706, DeLong P = 0.021). Calibration curves and decision curve analysis demonstrated that the combined model had the best calibration and the highest net clinical benefit. The preoperative inflammatory indicators NLR, PLR, and LMR were identified as independent risk factors for MLM. The three-model comparison demonstrated that the combined nomogram significantly outperformed both the pathological-only and inflammatory-only models in discrimination, calibration, and clinical net benefit, supporting the complementary predictive value of inflammatory markers. The novel nomogram developed and validated in this study demonstrated good discriminative ability and accuracy in predicting MLM in patients with CRC, although external validation in larger multi-center cohorts is required.
Gu et al. (Fri,) studied this question.