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Preoperative Nomogram Incorporating Clinical Factors, Serological Markers and LI-RADS MRI Features to Predict Early Recurrence of Hepatocellular Carcinoma Treated with Transarterial Chemoembolization

Published:November 21, 2022DOI:https://doi.org/10.1016/j.acra.2022.10.020

      Purpose

      To develop and validate a preoperative nomogram model that incorporates clinical factors, serological markers and liver imaging reporting and data system (LI-RADS v2018) MRI features for predicting early recurrence (ER) of hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE).

      Methods

      One hundred and fourteen patients with HCC who underwent MRI scanning before TACE were enrolled retrospectively and divided into a training cohort (n=80) and a test cohort (n=34). The clinical factors, serological markers and LI-RADS v2018 MRI features associated with ER were determined by univariable and multivariable analyses. A nomogram model predicting ER after TACE was developed, and its discriminatory ability, goodness-of-fit and clinical application were evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA), respectively.

      Results

      In total, 74 (64.9%) patients were diagnosed with ER according to the follow-up results. Increased alpha fetoprotein (AFP) levels, larger tumor size, nonsmooth margin, mosaic architecture satellite nodules and corona enhancement were independent predictors associated with ER (p < 0.05). For the established nomogram model that incorporated these six significant predictors, the AUC values were 0.94 (95% CI: 0.89-0.99) and 0.95 (95% CI: 0.88-1.00) for predicting ER after TACE in the training and test cohorts, respectively. The calibration curve and DCA results demonstrate the good goodness-of-fit and clinical benefits of this nomogram.

      Conclusion

      A preoperative nomogram model based on serological markers and LI-RADS v2018 MRI features could adequately predict ER in HCC patients after TACE, which may provide personalized guidance for predicting prognosis.

      Key Words

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