Nomograms for Predicting Hepatocellular Carcinoma Recurrence and Overall Postoperative Patient Survival

Lidi Ma; Kan Deng; Cheng Zhang; Haixia Li; Yingwei Luo; Yingsi Yang; Congrui Li; Xinming Li; Zhijun Geng; Chuanmiao Xie

doi:10.3389/fonc.2022.843589

Nomograms for Predicting Hepatocellular Carcinoma Recurrence and Overall Postoperative Patient Survival

Front Oncol. 2022 Feb 28:12:843589. doi: 10.3389/fonc.2022.843589. eCollection 2022.

Authors

Lidi Ma¹, Kan Deng², Cheng Zhang¹, Haixia Li², Yingwei Luo¹, Yingsi Yang¹, Congrui Li³, Xinming Li⁴, Zhijun Geng¹, Chuanmiao Xie¹

Affiliations

¹ Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
² Clinical Science, Philips Healthcare, Guangzhou, China.
³ Department of Diagnostic Radiology, Hunan Cancer Hospital, Central South University, Changsha, China.
⁴ Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.

Abstract

Background: Few studies have focused on the prognosis of patients with hepatocellular carcinoma (HCC) of Barcelona Clinic Liver Cancer (BCLC) stage 0‒C in terms of early recurrence and 5-years overall survival (OS). We sought to develop nomograms for predicting 5-year OS and early recurrence after curative resection of HCC, based on a clinicopathological‒radiological model. We also investigated whether different treatment methods influenced the OS of patients with early recurrence.

Methods: Retrospective data, including clinical pathology, radiology, and follow-up data, were collected for 494 patients with HCC who underwent hepatectomy. Nomograms estimating OS and early recurrence were constructed using multivariate Cox regression analysis, based on the random survival forest (RSF) model. We evaluated the discrimination and calibration abilities of the nomograms using concordance indices (C-index), calibration curves, and Kaplan‒Meier curves. OS curves of different treatments for patients who had recurrence within 2 years after curative surgery were depicted and compared using the Kaplan-Meier method and the log-rank test.

Results: Multivariate Cox regression revealed that BCLC stage, non-smooth margin, maximum tumor diameter, age, aspartate aminotransferase levels, microvascular invasion, and differentiation were prognostic factors for OS and were incorporated into the nomogram with good predictive performance in the training (C-index: 0.787) and testing cohorts (C-index: 0.711). A nomogram for recurrence-free survival was also developed based on four prognostic factors (BCLC stage, non-smooth margin, maximum tumor diameter, and microvascular invasion) with good predictive performance in the training (C-index: 0.717) and testing cohorts (C-index: 0.701). In comparison to the BCLC staging system, the C-index (training cohort: 0.787 vs. 0.678, 0.717 vs. 0.675; external cohort 2: 0.748 vs. 0.624, 0.729 vs. 0.587 respectively, for OS and RFS; external cohort1:0.716 vs. 0.627 for RFS, all p value<0.05), and model calibration curves all showed improved performance. Patients who underwent surgery after tumor recurrence had a higher reOS than those who underwent comprehensive treatments and supportive care.

Conclusions: The nomogram, based on clinical, pathological, and radiological factors, demonstrated good accuracy in estimating OS and recurrence, which can guide follow-up and treatment of individual patients. Reoperation may be the best option for patients with recurrence in good condition.

Keywords: hepatocellular carcinoma; nomogram; overall survival; prognosis; recurrence.