Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma

Objective and Impact Statement.This study developed and validated a deep semantic segmentation feature-based radiomics(DSFR)model based on preoperative contrast-enhanced computed tomography(CECT)combined with clinical information to predict early recurrence(ER)of single hepatocellular carcinoma(HCC)after curative resection.ER prediction is of great significance to the therapeutic decision-making and surveillance strategy of HCC.Introduction.ER prediction is important for HCC.However,it cannot currently be adequately determined.Methods.Totally,208 patients with single HCC after curative resection were retrospectively recruited into a model-development cohort(n=180)and an independent validation cohort(n=28).DSFR models based on different CT phases were developed.The optimal DSFR model was incorporated with clinical information to establish a DSFR-C model.An integrated nomogram based on the Cox regression was established.The DSFR signature was used to stratify high-and low-risk ER groups.Results.A portal phase-based DSFR model was selected as the optimal model(area under receiver operating characteristic curve(AUC):development cohort,0.740;validation cohort,0.717).The DSFR-C model achieved AUCs of 0.782 and 0.744 in the development and validation cohorts,respectively.In the development and validation cohorts,the integrated nomogram achieved C-index of 0.748 and 0.741 and time-dependent AUCs of 0.823 and 0.822,respectively,for recurrence-free survival(RFS)prediction.The RFS difference between the risk groups was statistically significant(P<0.0001 and P=0.045 in the development and validation cohorts,respectively).Conclusion.CECT-based DSFR can predict ER in single HCC after curative resection,and its combination with clinical information further improved the performance for ER prediction.