Commentary on: Nomograms based on inflammatory biomarkers for predicting tumor grade and microvascular invasion in stage I/II hepatocellular carcinoma

Prediction Model and Nomogram of Early Recurrence of Hepatocellular Carcinoma after Radiofrequency Ablation Based on Logistic Regression Analysis

The purpose of this study was to screen for high-risk factors leading to the early recurrence of hepatocellular carcinoma (HCC) after radiofrequency ablation (RFA) and to construct a prediction model and nomogram. This retrospective study included 108 patients with primary HCC who underwent RFA treatment at the Harbin Medical University Cancer Hospital between January 2018 and June 2019. Four risk factors were screened for using univariate and multivariate logistic regression analyses: number of tumors (hazard ratio [HR] = 14.684, 95% confidence interval [CI]: 1.099-196.215, p = 0.042), neutrophil-to-lymphocyte ratio (NLR) (HR = 2.178, 95% CI: 1.003-4.730, p = 0.049), contrast-enhanced ultrasound (CEUS) performance (HR = 6.482, 95% CI: 1.161-36.184, p = 0.033) and α-fetoprotein (AFP) level (HR = 1.001, 95% CI: 1.000-1.003, p = 0.040). We established a prediction model: Logit(p) = -3.096 + 2.827 × (number of tumors >1 = 1) + 1.851 × (CEUS revealing rapid enhancement of blood flow signal in the arterial phase and clearance in the portal phase = 1) + 1.941 × (NLR >1.55 = 1) + 0.257 × (AFP >32.545 = 1). Through clinical decision curve analysis, the model's threshold was 0.043-0.873, indicating a high clinical value. Patients with a high AFP level, typical CEUS enhancement pattern, multiple tumors and elevated NLR are more likely to relapse early.