The CRP–albumin–lymphocyte index provides enhanced prognostic value in liver cancer compared to the TNM staging system - Zhao, Hong; Yin, Bing; Li, Xiang-Rui; Liu, Xiao-Yue; Bu, Zhao-Ting; Shi, Han-Ping (Scientific reports, 2025)
Reliable biomarkers are critical for improving overall survival (OS) and guiding therapeutic strategies in liver cancer. This study evaluated the CRP–albumin–lymphocyte (CALLY) index—a composite indicator of systemic inflammation, nutritional status, and immune function—as a prognostic tool for liver cancer, comparing its predictive utility to the established TNM staging system. A retrospective cohort of 388 patients with histologically confirmed liver cancer was analyzed using data from the Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) database. Kaplan–Meier survival analysis, restricted cubic spline (RCS) functions, and multivariate Cox regression models were utilized to determine the prognostic significance of the CALLY index. A nomogram was constructed incorporating the CALLY index, age, and TNM stage to estimate 1-, 2-, 3-year OS. The model’s performance was benchmarked against the TNM staging system using time-dependent receiver operating characteristic (ROC) curves and Decision curve analysis (DCA). Multivariate Cox regression analysis demonstrated that the CALLY index was independently associated with OS in patients with liver cancer [Hazard ratio (HR) = 0.57, 95% confidence interval (CI) 0.39–0.83, P =0.003]. The prognostic value of the CALLY index was superior to that of the TNM staging system (C-index = 0.621, 95% CI 0.572–0.669, P < 0.001). Compared with the traditional TNM staging system, the prognostic nomogram incorporating the CALLY index, age, and TNM stage demonstrated higher accuracy in predicting 1-, 2-, and 3-year OS in patients with liver cancer (1-year: 0.705 vs. 0.644; 2-year: 0.698 vs. 0.66; 3-year: 0.6499 vs. 0.6079). The CALLY index is a robust prognostic biomarker for liver cancer, offering enhanced predictive accuracy over traditional staging methods. Its incorporation into a predictive nomogram may facilitate personalized treatment strategies, ultimately improving patient outcomes.