Retrospective Analysis of Radiofrequency Ablation in Patients with Small Solitary Hepatocellular Carcinoma: Survival Outcomes and Development of a Machine Learning Prognostic Model

Background and ObjectiveThe effectiveness of radiofrequency ablation (RFA) in improving long-term survival outcomes for patients with a solitary hepatocellular carcinoma (HCC) measuring 5 cm or less remains uncertain. This study was designed to elucidate the impact of RFA therapy on the survival outcomes of these patients and to construct a prognostic model for patients following RFA.MethodsThis study was performed using the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2017, focusing on patients diagnosed with a solitary HCC lesion <= 5 cm in size. We compared the overall survival (OS) and cancer-specific survival (CSS) rates of these patients with those of patients who received hepatectomy, radiotherapy, or chemotherapy or who were part of a blank control group. To enhance the reliability of our findings, we employed stabilized inverse probability treatment weighting (sIPTW) and stratified analyses. Additionally, we conducted a Cox regression analysis to identify prognostic factors. XGBoost models were developed to predict 1-, 3-, and 5-year CSS. The XGBoost models were evaluated via receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) curves and so on.ResultsRegardless of whether the data were unadjusted or adjusted for the use of sIPTWs, the 5-year OS (46.7%) and CSS (58.9%) rates were greater in the RFA group than in the radiotherapy (27.1%/35.8%), chemotherapy (32.9%/43.7%), and blank control (18.6%/30.7%) groups, but these rates were lower than those in the hepatectomy group (69.4%/78.9%). Stratified analysis based on age and cirrhosis status revealed that RFA and hepatectomy yielded similar OS and CSS outcomes for patients with cirrhosis aged over 65 years. Age, race, marital status, grade, cirrhosis status, tumor size, and AFP level were selected to construct the XGBoost models based on the training cohort. The areas under the curve (AUCs) for 1, 3, and 5 years in the validation cohort were 0.88, 0.81, and 0.79, respectively. Calibration plots further demonstrated the consistency between the predicted and actual values in both the training and validation cohorts.ConclusionRFA can improve the survival of patients diagnosed with a solitary HCC lesion <= 5 cm. In certain clinical scenarios, RFA achieves survival outcomes comparable to those of hepatectomy. The XGBoost models developed in this study performed admirably in predicting the CSS of patients with solitary HCC tumors smaller than 5 cm following RFA.