Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma: A step towards precision medicine

Abstract

The high rate of early recurrence in hepatocellular carcinoma (HCC) post curative surgical intervention poses a substantial clinical hurdle, impacting patient outcomes and complicating postoperative management. The advent of machine learning provides a unique opportunity to harness vast datasets, identifying subtle patterns and factors that elude conventional prognostic methods. Machine learning models, equipped with the ability to analyse intricate relationships within datasets, have shown promise in predicting outcomes in various medical disciplines. In the context of HCC, the application of machine learning to predict early recurrence holds potential for personalized postoperative care strategies. This editorial comments on the study carried out exploring the merits and efficacy of random survival forests (RSF) in identifying significant risk factors for recurrence, stratifying patients at low and high risk of HCC recurrence and comparing this to traditional COX proportional hazard models (CPH). In doing so, the study demonstrated that the RSF models are superior to traditional CPH models in predicting recurrence of HCC and represent a giant leap towards precision medicine.

Keywords: Machine learning; Artificial intelligence; Hepatocellular carcinoma; Hepatology; Early recurrence; Liver resection

Core Tip: This study addresses the crucial issue of early recurrence in hepatocellular carcinoma, emphasizing the significance of aggressive tumour characteristics. random survival forests, a machine learning model, surpasses conventional COX proportional hazard models, offering improved prediction, clinical usefulness, and overall performance. The model's ability to stratify risk facilitates targeted postoperative strategies, showcasing its potential as a guide for personalized patient care.