Abstract
Surgical resection remains primary curative treatment for patients with hepatocellular carcinoma (HCC) while over 50% of patients experience recurrence, which calls for individualized recurrence prediction and early surveillance. This study aimed to develop a machine learning prognostic model to identify high-risk patients after surgical resection and to review importance of variables in different time intervals. The patients in this study were from two centers including Eastern Hepatobiliary Surgery Hospital (EHSH) and Mengchao Hepatobiliary Hospital (MHH). The best-performed model was determined, validated, and applied to each time interval (0–1 year, 1–2 years, 2–3 years, and 3–5 years). Importance scores were used to illustrate feature importance in different time intervals. In addition, a risk heat map was constructed which visually depicted the risk of recurrence in different years. A total of 7,919 patients from two centers were included, of which 3,359 and 230 patients experienced recurrence, metastasis or died during the follow-up time in the EHSH and MHH datasets, respectively. The XGBoost model achieved the best discrimination with a c-index of 0.713 in internal validation cohort. Kaplan-Meier curves succeed to stratify external validation cohort into different risk groups (p < 0.05 in all comparisons). Tumor characteristics contribute more to HCC relapse in 0 to 1 year while HBV infection and smoking affect patients’ outcome largely in 3 to 5 years. Based on machine learning prediction model, the peak of recurrence can be predicted for individual HCC patients. Therefore, clinicians can apply it to personalize the management of postoperative survival.
Highlights
Hepatocellular carcinoma (HCC) is the most common primary liver cancer and ranks as the fourth leading cause of cancerrelated mortality (8.2%) worldwide [1]
A total of 7,919 patients who underwent surgical resection from two centers were included in the study. 80% of EHSH cohort was assigned as the derivation set (n = 5,928) and the rest was designated as internal validation set (n = 1,483)
Though curative resection offers the best prognosis for patients, disease recurrence remains a major obstacle to the long-term survival of patients [21]
Summary
Hepatocellular carcinoma (HCC) is the most common primary liver cancer and ranks as the fourth leading cause of cancerrelated mortality (8.2%) worldwide [1]. Surgical resection remains the primary curative treatment for patients with adequate liver function [2]. 50% to 70% of patients who undergo complete tumor resection still suffer from frequent recurrence and disease progression, leading to unfavorable prognoses [3]. Its prognostic value in predicting tumor recurrence is widely debated [4]. Recent models, including the Singapore Liver Cancer Recurrence (SLICER) score, Surgery-Specific Cancer of the Liver Italian Program (SS-CLIP), and the Korean model, were designed to detect tumor recurrence in specific groups of patients. The Early Recurrence After Surgery for Liver tumor (ERASL) model, which is based on Cox regression analysis, has been established to predict early tumor recurrence after liver resection. Despite its better discriminatory performances than other tools, the limited clinical parameters and the prediction for 2-year recurrence restrict its application in the full HCC survivorship management [8]
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