Construction of a Nomogram to Predict Overall Survival in Patients with Early-Onset Hepatocellular Carcinoma: A Retrospective Cohort Study

SIMPLE SUMMARY: Hepatocellular carcinoma (HCC) is a severe global health concern, and it is increasingly jeopardizing younger individuals. Despite this, there is a lack of available tools for the prognosis estimation of early-onset HCC. In our study, we conducted a retrospective analysis of early-onset HCC (EO-LIHC) using data of the period from 2004 to 2018. We identified independent risk factors using a Cox regression analysis, including age, sex, AFP level, the grading and staging of the tumor, the size of the tumor, and whether the patient was receiving therapy like surgery and chemotherapy. We developed a predictive nomogram to estimate 1-, 3-, and 5-year survival rates of EO-LIHC patients and a user-friendly web-based survival prediction model tailored for these patients. These findings provide valuable insights for personalized care and treatment decisions for individuals with EO-LIHC. ABSTRACT: Hepatocellular carcinoma (HCC) is a widespread and impactful cancer which has pertinent implications worldwide. Although most cases of HCC are typically diagnosed in individuals aged ≥60 years, there has been a notable rise in the occurrence of HCC among younger patients. However, there is a scarcity of precise prognostic models available for predicting outcomes in these younger patients. A retrospective analysis was conducted to investigate early-onset hepatocellular carcinoma (EO-LIHC) using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2018. The analysis included 1392 patients from the SEER database and our hospital. Among them, 1287 patients from the SEER database were assigned to the training cohort (n = 899) and validation cohort 1 (n = 388), while 105 patients from our hospital were assigned to validation cohort 2. A Cox regression analysis showed that age, sex, AFP, grade, stage, tumor size, surgery, and chemotherapy were independent risk factors. The nomogram developed in this study demonstrated its discriminatory ability to predict the 1-, 3-, and 5-year overall survival (OS) rates in EO-LIHC patients based on individual characteristics. Additionally, a web-based OS prediction model specifically tailored for EO-LIHC patients was created and validated. Overall, these advancements contribute to improved decision-making and personalized care for individuals with EO-LIHC.