Milena Cerny

Background: The Liver Imaging Reporting and Data System (LI‐RADS) diagnostic algorithm assesses hepatocellular carcinoma (HCC) probability of liver observations in high‐risk individuals, but complexity may limit its adoption. Purpose: To evaluate whether LI‐RADS v2018 can be simplified without impairing diagnostic performance by removing threshold growth (TG) and reducing size cut‐offs. Study Type: Retrospective meta‐analysis. Population: Eight thousand five hundred eighty‐two patients (mean age: 59 ± 11 years; 6610 male) at risk for HCC with CT/MRI (986 CT, 6554 MRI, and 1042 CT/MRI). Field Strength/Sequence: Field strength: 1.5T and 3.0T; sequences varied by institution per LI‐RADS technical requirements. Assessment: LI‐RADS v2018 using major features only was compared with seven simplifications removing TG and/or applying single size cut‐offs (≥ 10, ≥ 15, and ≥ 20 mm) in individual participant data (IPD) of 64 studies (29 institutions, 11 countries) from the LI‐RADS Living Systematic Review and IPD Meta‐analysis Project. Risk of bias was assessed with QUADAS‐2. Statistical Tests: Summary receiver operating characteristic curves estimated area under the curve (AUC) across LR‐3 to LR‐5; LR‐5 sensitivity, specificity, and positive predictive value (PPV) were estimated using a one‐step bivariate mixed‐effects model. Differences were assessed by cluster bootstrap. p < 0.05 indicated significance, uncorrected for multiple comparisons. Results: Nine thousand four hundred six observations were included. Excluding TG with a 10‐mm cut‐off showed AUC similar to LI‐RADS v2018 (0.80 vs. 0.81, p = 0.44) and significantly higher LR‐5 sensitivity (73.8% vs. 71.8%) with similar specificity (72.5% vs. 73.7%, p = 0.13) and PPV (97.8% vs. 97.8%, p = 0.73). Removing TG with 15‐mm and 20‐mm cut‐offs had significantly lower AUC than v2018 (−0.03 [95% CI: −0.041, −0.019]; −0.043 [−0.054, −0.032], respectively) and sensitivity (−13.2% [95% CI: −14.5%, −12.0%]; −30.1% [−31.7%, −28.5%], respectively). Of the 64 studies, 17 had low‐risk of bias, and 56 had low applicability concerns. Conclusion: Simplifying LI‐RADS v2018 by removing TG and using a 10‐mm size cut‐off preserved diagnostic table AUC while improving LR‐5 sensitivity without loss of specificity or PPV. Protocol Registration: Open Science Framework ( https://osf.io/cjwgp ). Evidence Level: 3. Technical Efficiency: Stage 2.

This meta-analysis showed that most major feature combinations in the same CT/MRI Liver Imaging Reporting and Data System category had similar positive predictive values for hepatocellular carcinoma in patients at high risk for cancer, with the exception of five combinations from LR-3 to LR-5.

Background A simplification of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 (v2018), revised LI-RADS (rLI-RADS), has been proposed for imaging-based diagnosis of hepatocellular carcinoma (HCC). Single-site data suggest that rLI-RADS category 5 (rLR-5) improves sensitivity while maintaining positive predictive value (PPV) of the LI-RADS v2018 category 5 (LR-5), which indicates definite HCC. Purpose To compare the diagnostic performance of LI-RADS v2018 and rLI-RADS in a multicenter data set of patients at risk for HCC by performing an individual patient data meta-analysis. Materials and Methods Multiple databases were searched for studies published from January 2014 to January 2022 that evaluated the diagnostic performance of any version of LI-RADS at CT or MRI for diagnosing HCC. An individual patient data meta-analysis method was applied to observations from the identified studies. Quality Assessment of Diagnostic Accuracy Studies version 2 was applied to determine study risk of bias. Observations were categorized according to major features and either LI-RADS v2018 or rLI-RADS assignments. Diagnostic accuracies of category 5 for each system were calculated using generalized linear mixed models and compared using the likelihood ratio test for sensitivity and the Wald test for PPV. Results Twenty-four studies, including 3840 patients and 4727 observations, were analyzed. The median observation size was 19 mm (IQR, 11-30 mm). rLR-5 showed higher sensitivity compared with LR-5 (70.6% [95% CI: 60.7, 78.9] vs 61.3% [95% CI: 45.9, 74.7]; P < .001), with similar PPV (90.7% vs 92.3%; P = .55). In studies with low risk of bias (n = 4; 1031 observations), rLR-5 also achieved a higher sensitivity than LR-5 (72.3% [95% CI: 63.9, 80.1] vs 66.9% [95% CI: 58.2, 74.5]; P = .02), with similar PPV (83.1% vs 88.7%; P = .47). Conclusion rLR-5 achieved a higher sensitivity for identifying HCC than LR-5 while maintaining a comparable PPV at 90% or more, matching the results presented in the original rLI-RADS study. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sirlin and Chernyak in this issue.

Background Various limitations have impacted research evaluating reader agreement for Liver Imaging Reporting and Data System (LI-RADS). Purpose To assess reader agreement of LI-RADS in an international multicenter multireader setting using scrollable images. Materials and Methods This retrospective study used deidentified clinical multiphase CT and MRI and reports with at least one untreated observation from six institutions and three countries; only qualifying examinations were submitted. Examination dates were October 2017 to August 2018 at the coordinating center. One untreated observation per examination was randomly selected using observation identifiers, and its clinically assigned features were extracted from the report. The corresponding LI-RADS version 2018 category was computed as a rescored clinical read. Each examination was randomly assigned to two of 43 research readers who independently scored the observation. Agreement for an ordinal modified four-category LI-RADS scale (LR-1, definitely benign; LR-2, probably benign; LR-3, intermediate probability of malignancy; LR-4, probably hepatocellular carcinoma [HCC]; LR-5, definitely HCC; LR-M, probably malignant but not HCC specific; and LR-TIV, tumor in vein) was computed using intraclass correlation coefficients (ICCs). Agreement was also computed for dichotomized malignancy (LR-4, LR-5, LR-M, and LR-TIV), LR-5, and LR-M. Agreement was compared between research-versus-research reads and research-versus-clinical reads. Results The study population consisted of 484 patients (mean age, 62 years ± 10 [SD]; 156 women; 93 CT examinations, 391 MRI examinations). ICCs for ordinal LI-RADS, dichotomized malignancy, LR-5, and LR-M were 0.68 (95% CI: 0.61, 0.73), 0.63 (95% CI: 0.55, 0.70), 0.58 (95% CI: 0.50, 0.66), and 0.46 (95% CI: 0.31, 0.61) respectively. Research-versus-research reader agreement was higher than research-versus-clinical agreement for modified four-category LI-RADS (ICC, 0.68 vs 0.62, respectively; P = .03) and for dichotomized malignancy (ICC, 0.63 vs 0.53, respectively; P = .005), but not for LR-5 (P = .14) or LR-M (P = .94). Conclusion There was moderate agreement for LI-RADS version 2018 overall. For some comparisons, research-versus-research reader agreement was higher than research-versus-clinical reader agreement, indicating differences between the clinical and research environments that warrant further study. © RSNA, 2023 Supplemental material is available for this article. See also the editorials by Johnson and Galgano and Smith in this issue.

Purpose To evaluate the performance, agreement, and efficiency of a fully convolutional network (FCN) for liver lesion detection and segmentation at CT examinations in patients with colorectal liver metastases (CLMs). Materials and Methods This retrospective study evaluated an automated method using an FCN that was trained, validated, and tested with 115, 15, and 26 contrast material–enhanced CT examinations containing 261, 22, and 105 lesions, respectively. Manual detection and segmentation by a radiologist was the reference standard. Performance of fully automated and user-corrected segmentations was compared with that of manual segmentations. The interuser agreement and interaction time of manual and user-corrected segmentations were assessed. Analyses included sensitivity and positive predictive value of detection, segmentation accuracy, Cohen κ, Bland-Altman analyses, and analysis of variance. Results In the test cohort, for lesion size smaller than 10 mm (n = 30), 10–20 mm (n = 35), and larger than ...

The Liver Imaging Reporting and Data System (LI-RADS) standardizes performance of liver imaging in patients at risk for hepatocellular carcinoma (HCC) as well as interpretation and reporting of the results. Developed by experts in liver imaging and supported by the American College of Radiology, LI-RADS assigns to observations categories that reflect the relative probability of benignity, HCC, or other malignancy. While category assignment is based mainly on major imaging features, ancillary features may be applied to improve detection and characterization, increase confidence, or adjust LI-RADS categories. Ancillary features are classified as favoring malignancy in general, HCC in particular, or benignity. Those favoring malignancy in general or HCC in particular may be used to upgrade by a maximum of one category up to LR-4; those favoring benignity may be used to downgrade by a maximum of one category. If there are conflicting ancillary features (ie, one or more favoring malignancy and one or more favoring benignity), the category should not be adjusted. Ancillary features may be seen at diagnostic CT, MRI performed with extracellular agents, or MRI performed with hepatobiliary agents, with the exception of one ancillary feature assessed at US. This article focuses on LI-RADS version 2018 ancillary features seen at MRI. Specific topics include rules for ancillary feature application; definitions, rationale, and illustrations with clinical MRI examples; summary of evidence and diagnostic performance; pitfalls; and future directions. ©RSNA, 2018.

LI-RADS major features have moderate to excellent specificity for HCC, except for arterial phase hyperenhancement. Ancillary features modify the final category in a substantial proportion of observations and increase sensitivity while preserving high specificity for the diagnosis of HCC. PurposeTo evaluate the performance of major features, ancillary features, and categories of Liver Imaging Reporting and Data System (LI-RADS) version 2014 at magnetic resonance (MR) imaging for the diagnosis of hepatocellular carcinoma (HCC).Materials and MethodsThis retrospective institutional review board–approved study included patients with liver MR imaging and at least one pathologically proved lesion. Between 2004 and 2016, 102 patients (275 observations including 113 HCCs) met inclusion criteria. Two radiologists independently assessed major and ancillary imaging features for each liver observation and assigned a LI-RADS category. Per-lesion estimates of diagnostic performance of major features, ancillary features, and LI-RADS categories were assessed by using generalized estimating equation models.ResultsMajor features (arterial phase hyperenhancement, washout, capsule, and threshold growth) had a sensitivity of 88.5%, 60.6%, 32.9%, and 41.6%, and a specificity of 18.6%, 84.8%, 98.8%, and 83.2% fo...

The computed tomography / magnetic resonance imaging (CT/MRI) Liver Imaging Reporting & Data System (LI‐RADS) is a standardized system for diagnostic imaging terminology, technique, interpretation, and reporting in patients with or at risk for developing hepatocellular carcinoma (HCC). Using diagnostic algorithms and tables, the system assigns to liver observations category codes reflecting the relative probability of HCC or other malignancies. This review article provides an overview of the 2017 version of CT/MRI LI‐RADS with a focus on MRI. The main LI‐RADS categories and their application will be described. Changes and updates introduced in this version of LI‐RADS will be highlighted, including modifications to the diagnostic algorithm and to the optional application of ancillary features. Comparisons to other major diagnostic systems for HCC will be made, emphasizing key similarities, differences, strengths, and limitations. In addition, this review presents the new Treatment Response algorithm, while introducing the concepts of MRI nonviability and viability. Finally, planned future directions for LI‐RADS will be outlined.Level of Evidence: 5Technical Efficacy: Stage 5J. Magn. Reson. Imaging 2018;47:1459–1474.