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Background: We aimed to evaluate the value of using preoperative magnetic resonance imaging (MRI) features and clinical indicators to predict the early response of hepatocellular carcinoma (HCC) to transcatheter arterial chemoembolization (TACE). We also aimed to establish a preoperative prediction model., Methods: We retrospectively reviewed data of 111 patients with HCC who underwent magnetic resonance imaging (MRI) before the first TACE and underwent MRI or computed tomography between 30 and 60 days after TACE. We used the modified response evaluation criteria in solid tumors for evaluating the TACE response. We used univariate and multivariate logistic regression analyses to identify independent predictors based on MRI features and clinical indicators. Moreover, receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of the prediction model and each independent predictor., Results: Among the 111 included patients, 85 were men (76.6%). Patient age was 31-86 years (average age, 61.08 ± 11.50 years). After the first treatment session, 56/111 (50.5%) patients showed an objective response (complete response + partial response), whereas the remaining showed non-response (stable disease + local progressive disease). In the univariate analysis, we identified irregular margins, number of nodules, and satellite nodules as predictors of early objective response. However, in the multivariate logistic regression analysis, irregular margins, number of nodules and pretreatment platelet were identified as the independent predictors of early objective response. A combined prediction model was then established, which factored in irregular margins, the number of nodules, and the pretreatment platelet count. This model showed good diagnostic performance (area under the ROC curve = 0.755), with the sensitivity, specificity, positive predictive value, and negative predictive value being 78.6%, 69.1%, 72.1%, and 76.0%, respectively., Conclusions: Irregular margins, the number of nodules and the pretreatment platelet count are independent predictors of the early response of HCC to TACE. Our clinical combined model can provide a superior predictive power to a single indicator., (© 2022. The Author(s).)

Background: The Liver Imaging Reporting and Data System (LI-RADS) was established for noninvasive diagnosis for hepatocellular carcinoma (HCC). However, whether training can improve readers' agreement with the expert consensus and inter-reader agreement for final categories is still unclear., Purpose: To explore training effectiveness on readers' agreement with the expert consensus and inter-reader agreement., Study Type: Prospective., Subjects: Seventy lesions in 61 patients at risk of HCC undergoing liver MRI; 20 visiting scholars., Field Strength/sequence: 1.5 T or 3 T, Dual-echo T 1 WI, Fast spin-echo T 2 WI, SE-EPI DWI, and Dynamic multiphase fast gradient-echo T 1 WI., Assessment: Seventy lesions assigned LI-RADS categories of LR1-LR5, LR-M, and LR-TIV by three radiologists in consensus were randomly selected, with 10 cases for each category. The consensus opinion was the standard reference. The third radiologist delivered the training. Twenty readers reviewed images independently and assigned each an LI-RADS category both before and after the training., Statistical Tests: Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, receiver operating characteristic (ROC) analysis, simple and weighted kappa statistics, and Fleiss kappa statistics., Results: Before and after training: readers' AUC (areas under ROC) for LR-1-LR-5, LR-M, and LR-TIV were 0.898 vs. 0.913, 0.711 vs. 0.876, 0.747 vs. 0.860, 0.724 vs. 0.815, 0.844 vs. 0.895, 0.688 vs. 0.873, and 0.720 vs. 0.948, respectively, and all improved significantly (P < 0.05), except LR-1(P = 0.25). Inter-reader agreement between readers for LR-1-LR-5, LR-M, LR-TIV were 0.725 vs. 0.751, 0.325 vs. 0.607, 0.330 vs. 0.559, 0.284 vs. 0.488, 0.447 vs. 0.648, 0.229 vs. 0.589, and 0.362 vs. 0.852, respectively, and all increased significantly (P < 0.05). For training effectiveness on both AUC and inter-reader agreement, LR-TIV, LR-M, and LR-2 improved most, and LR-1 made the least., Data Conclusion: This study shows LI-RADS training could improve reader agreement with the expert consensus and inter-reader agreement for final categories., Level of Evidence: 2 TECHNICAL EFFICACY STAGE: 2., (© 2021 International Society for Magnetic Resonance in Medicine.)

Aim: To investigate the diagnostic performance of Liver Imaging Reporting and Data System (LI-RADS) version 2017 for diagnosing hepatocellular carcinoma (HCC), by using major features only and combined major and ancillary features on computed tomography (CT)., Materials and Methods: A total of 147 HCC, 35 non-HCC malignancy, and 37 benign lesions in 205 patients at high risk of HCC were evaluated retrospectively, and the diagnostic performance of LI-RADS for diagnosing HCC were compared between using major features only and adopting major and ancillary features in combination., Results: When using LR-5 as a predictor for diagnosing HCC, the diagnostic specificity (90.3% versus 91.7%), positive predictive value (92.3% versus 93.3%), and accuracy (68% versus 68.8%) were increased based on major and ancillary features in combination than just using major features on CT. When using LR-4/5 as a predictor for diagnosing HCC, the diagnostic sensitivity (78.9% versus 85.7%), negative predictive value (64.4% versus 72%), and accuracy (78.5% versus 82.2%) were increased while preserving a high specificity (77.8% versus 75%), according to major and ancillary features in combination rather than just using major features on CT. The LI-RADS categories of 8.7% (19/219) lesions were adjusted by adding the ancillary features on CT., Conclusion: Adding the ancillary features visible on CT can improve the diagnostic performance of the LI-RADS v2017 algorithm for diagnosing HCC, especially for LR-3 lesions., (Copyright © 2020. Published by Elsevier Ltd.)

Background: The Liver Imaging Reporting and Data System (LI-RADS) is widely adopted for noninvasive diagnosis of hepatocellular carcinoma (HCC). It's updated to version 2018 recently, with some major changes compared with v2017. However, the diagnostic performance of LI-RADS v2018 and its difference with v2017 are yet to be validated., Purpose: To compare the diagnostic performances of LI-RADS on MR for diagnosing HCC between v2017 and v2018., Study Type: Retrospective., Subjects: In all, 181 patients with 217 hepatic observations (146 HCCs, 16 non-HCC malignancies and 55 benign lesions) with liver MRI and pathological or follow-up imaging diagnoses., Field Strength/sequence: 1.5 T or 3 T MRI. Dual-echo T 1 WI, T 2 WI, diffusion-weighted imaging, and a liver acquisition with volume acceleration. Assessment Images were independently interpreted by three radiologists, and then in consensus for observations with different LR categories, according to LI-RADS v2017 and v2018, separately., Statistical Tests: Sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (+LR), and Youden index., Results: When adopting LR-5 as a predictor of HCC, the sensitivity (80.8% vs. 71.2%), NPV (69.6% vs. 60.7%), and accuracy (83.9% vs. 77.9%) were all increased for LI-RADS v2018 compared with v2017, with a greater Youden index (0.709 vs. 0.627). However, the diagnostic performances of MRI for diagnosing HCC were not changed while adopting LR-4/5 as a predictor. The threshold growths of 76% (19/25) observations in v2017 were revised to subthreshold growth in v2018, and 16 LR-4 observations in v2017 were changed to LR-5 based on v2018., Data Conclusion: The diagnostic performance of LI-RADS v2018 for diagnosing HCC is superior to v2017, with a greater sensitivity, NPV, and accuracy. The revisions in v2018 mainly affect the categorization when adopting LR-5 as a predictor of HCC., Level of Evidence: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:746-755., (© 2019 International Society for Magnetic Resonance in Medicine.)