Comparison of the long-term outcomes of patients with hepatocellular carcinoma within the Milan criteria treated by ablation, resection, or transplantation.

BACKGROUND: Liver transplantation (LT), resection (LR), and ablation (LA) are three curative-intent treatment options for patients with early hepatocellular carcinoma (HCC). We aimed to develop a prognostic calculator to compare the long-term outcomes following each of these therapies. METHODS: A total of 976 patients with HCC within the Milan criteria who underwent LT, LR, and LA between 2009 and 2019 from four institutions were evaluated. Multistate competing risks prediction models for recurrence-free survival (RFS), recurrence within the Milan criteria (RWM), and HCC-specific survival (HSS) were derived to develop a prognostic calculator. RESULTS: During a median follow-up of 51 months, 420 (43%) patients developed recurrence. In the multivariate analysis, larger tumor size, multinodularity, older age, male, higher alpha-fetoprotein (AFP), higher albumin-bilirubin (ALBI) grade, and the presence of portal hypertension were significantly associated with higher recurrence and decreased survival rates. The RFS and HSS were both significantly higher among patients treated by LT than by LR or LA and significantly higher between patients treated by LR than by LA (all p < 0.001). For multinodular HCC ≤3 cm, although LT had better RFS and HSS than LR or LA, LA was noninferior to LR. An online prognostic calculator was then developed based on the preoperative clinical factors that were independently associated with outcomes to evaluate RFS, RWM, and HSS at different time intervals for all three treatment options. CONCLUSIONS: Although LT resulted in the best recurrence and survival outcomes, LR and LA also offered durable long-term alternatives. This prognostic calculator is a useful tool for clinicians to guide an informed and personalized discussion with patients based on their tumor biology and liver function.

A Novel Nomogram Model to Predict the Recurrence-Free Survival and Overall Survival of Hepatocellular Carcinoma.

Background: Treatments for patients with early-stage hepatocellular carcinoma (HCC) include liver transplantation (LT), liver resection (LR), radiofrequency ablation (RFA), and microwave ablation (MWA), are critical for their long-term survival. However, a computational model predicting treatment-independent prognosis of patients with HCC, such as overall survival (OS) and recurrence-free survival (RFS), is yet to be developed, to our best knowledge. The goal of this study is to identify prognostic factors associated with OS and RFS in patients with HCC and develop nomograms to predict them, respectively. Methods: We retrospectively retrieved 730 patients with HCC from three hospitals in China and followed them up for 3 and 5 years after invasive treatment. All enrolled patients were randomly divided into the training cohort and the validation cohort with a 7:3 ratio, respectively. Independent prognostic factors associated with OS and RFS were determined by the multivariate Cox regression analysis. Two nomogram prognostic models were built and evaluated by concordance index (C-index), calibration curves, area under the receiver operating characteristics (ROC) curve, time-dependent area under the ROC curve (AUC), the Kaplan-Meier survival curve, and decision curve analyses (DCAs), respectively. Results: Prognostic factors for OS and RFS were identified, and nomograms were successfully built. Calibration discrimination was good for both the OS and RFS nomogram prediction models (C-index: 0.750 and 0.746, respectively). For both nomograms, the AUC demonstrated outstanding predictive performance; the DCA shows that the model has good decision ability; and the calibration curve demonstrated strong predictive power. The nomograms successfully discriminated high-risk and low-risk patients with HCC associated with OS and RFS. Conclusions: We developed nomogram survival prediction models to predict the prognosis of HCC after invasive treatment with acceptable accuracies in both training and independent testing cohorts. The models may have clinical values in guiding the selection of clinical treatment strategies.