Novel model combining contrast-enhanced ultrasound with serology predicts hepatocellular carcinoma recurrence after hepatectomy.

backgroundSurgery is the primary curative option in patients with hepatocellular carcinoma (HCC). However, recurrence within 2 years is observed in 30%-50% of patients, being a major cause of mortality.

aimTo construct and verify a non-invasive prediction model combining contrast-enhanced ultrasound (CEUS) with serology biomarkers to predict the early recurrence of HCC.

methodsRecords of 744 consecutive patients undergoing first-line curative surgery for HCC in one institution from 2016-2018 were reviewed, and 292 local patients were selected for analysis. General characteristics including gender and age, CEUS liver imaging reporting and data system (LIRADS) parameters including wash-in time, wash-in type, wash-out time, and wash-out type, and serology biomarkers including alanine aminotransferase, aspartate aminotransferase, platelets, and alpha-fetoprotein (AFP) were collected. Univariate analysis and multivariate Cox proportional hazards regression model were used to evaluate the independent prognostic factors for tumor recurrence. Then a nomogram called CEUS model was constructed. The CEUS model was then used to predict recurrence at 6 mo, 12 mo, and 24 mo, the cut-off value was calculate by X-tile, and each C-index was calculated. Then Kaplan-Meier curve was compared by log-rank test. The calibration curves of each time were depicted.

resultsA nomogram predicting early recurrence (ER), named CEUS model, was formulated based on the results of the multivariate Cox regression analysis. This nomogram incorporated tumor diameter, preoperative AFP level, and LIRADS, and the hazard ratio was 1.123 (95% confidence interval [CI]: 1.041-1.211), 1.547 (95%CI: 1.245-1.922), and 1.428 (95%CI: 1.059-1.925), respectively. The cut-off value at 6 mo, 12 mo, and 24 mo was 100, 80, and 50, and the C-index was 0.748 (95%CI: 0.683-0.813), 0.762 (95%CI: 0.704-0.820), and 0.762 (95%CI: 0.706-0.819), respectively. The model showed satisfactory results, and the calibration at 6 mo was desirable; however, the calibration at 12 and 24 mo should be improved.

conclusionThe CEUS model enables the well-calibrated individualized prediction of ER before surgery and may represent a novel tool for biomarker research and individual counseling.