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

Shu-Wen Zhang; Ning-Ning Zhang; Wen-Wen Zhu; Tian Liu; Jia-Yu Lv; Wen-Tao Jiang; Ya-Min Zhang; Tian-Qiang Song; Li Zhang; Yan Xie; Yong-He Zhou; Wei Lu

doi:10.3389/fonc.2022.946531

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

Front Oncol. 2022 Jul 22:12:946531. doi: 10.3389/fonc.2022.946531. eCollection 2022.

Authors

Shu-Wen Zhang¹, Ning-Ning Zhang¹, Wen-Wen Zhu¹, Tian Liu¹, Jia-Yu Lv², Wen-Tao Jiang³, Ya-Min Zhang⁴, Tian-Qiang Song⁵, Li Zhang³, Yan Xie³, Yong-He Zhou⁶, Wei Lu¹

Affiliations

¹ Department of Hepatobiliary Oncology, Liver Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
² Department of Hepatology, Tianjin Third Central Hospital, Tianjin, China.
³ Department of Liver Transplantation, Tianjin First Center Hospital, NHC Key Laboratory for Critical Care Medicine, Key Laboratory of Transplantation, Chinese Academy of Medical Sciences, Tianjin, China.
⁴ Department of Hepatobiliary Surgery, Tianjin First Central Hospital, Tianjin Key Laboratory for Organ Transplantation, Tianjin, China.
⁵ Liver Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
⁶ Tianjin Second People's Hospital, Tianjin Medical Research Institute of Liver Disease, Tianjin, China.

Abstract

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.

Keywords: OS; Prognosis; RFS; hepatocellular carcinoma; nomogram.