A Novel Nomogram Model to Predict the Recurrence-Free Survival and Overall Survival of Hepatocellular Carcinoma - Zhang, Shu-Wen; Zhang, Ning-Ning; Zhu, Wen-Wen; Liu, Tian; Lv, Jia-Yu; Jiang, Wen-Tao; Zhang, Ya-Min; Song, Tian-Qiang; Zhang, Li; Xie, Yan; Zhou, Yong-He; Lu, Wei (Frontiers in Oncology, 2022)
Background: Treatments for early‐stage hepatocellular carcinoma (HCC) patients 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 HCC patients, such as overall survival (OS) and recurrence-free survival (RFS), is yet to be developed, to our best knowledge. The goal of this study was to identify prognostic factors associated with OS and RFS in HCC patients and develop nomograms to predict them, respectively. Methods: We retrospectively retrieved 730 HCC patients 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 (K–M) survival curve, and decision curve analyses (DCA) 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 HCC patients 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.