The effect of adjuvant transarterial chemoembolization for hepatocellular carcinoma after liver resection based on risk stratification

Background:There is currently no standard adjuvant treatment proven to prevent hepatocellular carcinoma(HCC)recurrence.Recent studies suggest that postoperative adjuvant transarterial chemoembolization(PA-TACE)is beneficial for patients at high risk of tumor recurrence.However,it is difficult to select the patients.The present study aimed to develop an easy-to-use score to identify these patients.Methods:A total of 4530 patients undergoing liver resection were recruited.Independent risk factors were identified by Cox regression model in the training cohort and the Primary liver cancer big data transarterial chemoembolization(PDTE)scoring system was established.Results:The scoring system was composed of ten risk factors including alpha-fetoprotein(AFP),albuminbilirubin(ALBI)grade,operative bleeding loss,resection margin,tumor capsular,satellite nodules,tumor size and number,and microvascular and macrovascular invasion.Using 5 points as risk stratification,the patients with PA-TACE had higher recurrence-free survival(RFS)compared with non-TACE in>5 points group(P<0.001),whereas PA-TACE patients had lower RFS compared with non-TACE in≤5 points group(P=0.013).In the training and validation cohorts,the C-indexes of PDTE scoring system were 0.714[standard errors(SE)=0.010]and 0.716(SE=0.018),respectively.Conclusions:The model is a simple tool to identify PA-TACE for HCC patients after liver resection with a favorable performance.Patients with>5 points may benefit from PA-TACE.

Development of a machine learning model to predict early recurrence for hepatocellular carcinoma after curative resection

Background:Early recurrence is common for hepatocellular carcinoma(HCC)after surgical resection,being the leading cause of death.Traditionally,the COX proportional hazard(CPH)models based on linearity assumption have been used to predict early recurrence,but predictive performance is limited.Machine learning models offer a novel methodology and have several advantages over CPH models.Hence,the purpose of this study was to compare random survival forests(RSF)model with CPH models in prediction of early recurrence for HCC patients after curative resection.Methods:A total of 4,758 patients undergoing curative resection from two medical centers were included.Fifteen features including age,gender,etiology,platelet count,albumin,total bilirubin,AFP,tumor size,tumor number,microvascular invasion,macrovascular invasion,Edmondson-Steiner grade,tumor capsular,satellite nodules and liver cirrhosis were used to construct the RSF model in training cohort.Discrimination,calibration,clinical usefulness and overall performance were assessed and compared with other models.Results:Five hundred survival trees were used to generate the RFS model.The five highest Variable Importance(VIMP)were tumor size,macrovascular invasion,microvascular invasion,tumor number and AFP.In training,internal and external validation cohort,the C-index of RSF model were 0.725[standard errors(SE)=0.005],0.762(SE=0.011)and 0.747(SE=0.016),respectively;the Gönen&Heller’s K of RSF model were 0.684(SE=0.005),0.711(SE=0.008)and 0.697(SE=0.014),respectively;the time-dependent AUC(2 years)of RSF model were 0.818(SE=0.008),0.823(SE=0.014)and 0.785(SE=0.025),respectively.The RSF model outperformed early recurrence after surgery for liver tumor(ERASL)model,Korean model,American Joint Committee on Cancer tumor-node-metastasis(AJCC TNM)stage,Barcelona Clinic Liver Cancer(BCLC)stage and Chinese stage.The RSF model is capable of stratifying patients into three different risk groups(low-risk,intermediate-risk,high-risk groups)in the training and two validation cohorts(all P<0.0001).A web-based prediction tool was built to facilitate clinical application(https://recurrenceprediction.shinyapps.io/surgery_predict/).Conclusions:The RSF model is a reliable tool to predict early recurrence for patients with HCC after curative resection because it exhibited superior performance compared with other models.This novel model will be helpful to guide postoperative follow-up and adjuvant therapy.