Abstract
BackgroundThe accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperative adjuvant treatment and monitoring. This study aimed to develop and validate an artificial neural network (ANN) model to predict PHER in HCC patients without macroscopic vascular invasion.MethodsNine hundred and three patients who underwent curative liver resection for HCC participated in this study. They were randomly divided into derivation (n = 679) and validation (n = 224) cohorts. The ANN model was developed in the derivation cohort and subsequently verified in the validation cohort.ResultsPHER morbidity in the derivation and validation cohorts was 34.8 and 39.2%, respectively. A multivariable analysis revealed that hepatitis B virus deoxyribonucleic acid load, γ-glutamyl transpeptidase level, α-fetoprotein level, tumor size, tumor differentiation, microvascular invasion, satellite nodules, and blood loss were significantly associated with PHER. These factors were incorporated into an ANN model, which displayed greater discriminatory abilities than a Cox’s proportional hazards model, preexisting recurrence models, and commonly used staging systems for predicting PHER. The recurrence-free survival curves were significantly different between patients that had been stratified into two risk groups.ConclusionWhen compared to other models and staging systems, the ANN model has a significant advantage in predicting PHER for HCC patients without macroscopic vascular invasion.
Highlights
The accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperative adjuvant treatment and monitoring
Numerous studies have reported that posthepatectomy early recurrence (PHER) is associated with intrahepatic metastases from primary tumors that cannot be clinically detected, while late recurrence results from tumor formation following liver cirrhosis [10]
The establishment of an accurate, reliable, and specific PHER prediction model may provide a reliable means for choosing postoperative adjuvant treatments in high-risk patients, such as radiofrequency ablation (RFA), transcatheter arterial chemoembolization (TACE), or sorafenib
Summary
The accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperative adjuvant treatment and monitoring. The most common curative treatment for early HCC patients is hepatectomy [2,3,4]. Its effectiveness is limited by the high incidence of tumor recurrence in the postoperative period (up to 60%), leading to poor long-term survival in HCC patients [2,3,4]. Accurate prognostic prediction of postoperative tumor recurrence is consequential in the screening and choice of adjuvant therapies for high-risk patients. The establishment of an accurate, reliable, and specific PHER prediction model may provide a reliable means for choosing postoperative adjuvant treatments in high-risk patients, such as radiofrequency ablation (RFA), transcatheter arterial chemoembolization (TACE), or sorafenib
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