Construction of a prognostic model for colorectal cancer liver metastasis: A retrospective study based on population...

Colorectal cancer (CRC) is a prevalent gastrointestinal malignancy with a typically unfavorable prognosis following the onset of liver metastases.

To develop and validate a new clinical prediction model to accurately forecast overall survival (OS) in CRC patients following surgical treatment for liver metastasis.

This study included 1059 patients diagnosed with CRC liver metastases (CRLM) at the Xijing Hospital between 2010 and 2022. The patients were randomly divided into training and validation cohorts at a 7:3 ratio. Key clinical predictors were identified using least absolute shrinkage and selection operator (LASSO) regression combined with a Cox proportional hazards model, leading to the establishment of a prediction model and preparation of a nomogram to enhance its clinical utility. Decision curve analysis (DCA) and Kaplan-Meier survival analysis were employed to evaluate the precision and predictive performance of the model.

The LASSO-Cox regression analysis revealed multiple pivotal clinical biomarkers significantly linked to CRLM, including gamma-glutamyl transferase levels, blood chloride concentration, activated partial thromboplastin time, N stage, and vascular invasion. The model's receiver operating characteristic curve area under the curve exceeded 0.7 for both the training and validation groups with moderate-to-good predictive accuracy. Furthermore, DCA validated the nomogram's effectiveness for OS prediction. Kaplan-Meier risk stratification demonstrated markedly improved OS among patients classified as low-risk compared to those categorized as high-risk (P < 0.001), highlighting its clinical utility for risk assessment and treatment guidance.

The nomogram prediction model constructed in this study has good predictive value and can effectively assess the survival rate of patients with CRLM.