Circulating Immune Features Synergizing Neutrophil-to-Lymphocyte Ratio in Prediction of Poor Survival of Early-Stage Hepatocellular Carcinoma After Thermal Ablation
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background and Aim Predictors of neutrophil-to-lymphocyte ratio (NLR) and traditional clinical variables for hepatocellular carcinoma (HCC) prognosis after locoregional therapies were useful while exhibited modest prognostic performances. We dig out the potential of circulating immune features for HCC prognosis prediction. Methods 244 patients with early-stage HCC who were treated with thermal ablation and performed the peripheral blood mononuclear cells (PBMCs) tests were included. Patients were randomly assigned in 3:1 ratio to discovery (n = 183) and validation (n = 62) sets. Three models, including clinical (Clin-model), NLR-Clin-model and Immune-NLR-Clin-model were constructed using Cox regression model. Concordance index (c-index), integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used for performance evaluation. Results The Immune-NLR-Clin-model exhibited the best performance of 0.706 (95% CI:0.644-0.768) and 0.702 (95% CI:0.566-0.837) in discovery and validation sets, respectively. At 36-month prediction, the IDI and continuous-NRI show trend of improvement, with the IDI was 0.050 (95%CI: −0.5%-12.5%) ( P < .0270) and the continuous-NRI was 0.147 (95%CI: −0.5%-36.6%) ( P = .060) in discovery cohort. Tre g, C D 8 + and N LR from the immune-related combined model were selected to build TREND score. The median overall survival in TREND-low risk and high risk were 98.08 and 62.00 months, respectively ( P < .0001). The discrimination ability approached significantly in validation set ( P = .3200). Conclusions Circulating immune features may be helpful components aiding NLR for HCC predictive models.
Authors: Fangying Fan, Guoping Dong, Chuanhui Han, Yanchun Luo, Xin Li, Xuanjuan Dong, Zhen Wang, Ping Liang, Jie Yu
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,650
OpenAlex ID: W4408406924
