Usefulness of Arterial Subtraction in Applying Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Algorithm to Gadoxetic Acid-Enhanced MRI

Inter-reader agreement of LI-RADS treatment response algorithm among three readers with different seniorities for hepatocellular carcinoma after locoregional therapy

BACKGROUND

The accurate evaluation of tumor response after locoregional therapy is crucial for adjusting therapeutic strategy and guiding individualized follow-up.

PURPOSE

To determine the inter-reader agreement of the LR-TR algorithm for hepatocellular carcinoma treated with locoregional therapy among radiologists with different seniority.

MATERIAL AND METHODS

A total of 275 treated observations on 249 MRI scans from 99 patients were retrospectively collected. Three readers of different seniorities (senior, intermediate, and junior with 10, 6, and 2 years of experience in hepatic imaging, respectively) analyzed the presence or absence of features (arterial-phase hyperenhancement and washout) and evaluated LR-TR category.

RESULTS

There were substantial inter-reader agreements for overall LR-TR categorization (kappa = 0.704), LR-TR viable (kappa = 0.715), and LR-TR non-viable (kappa = 0.737), but fair inter-reader agreement for LR-TR equivocal (kappa = 0.231) among three readers. The inter-reader agreement was substantial for arterial-phase hyperenhancement (kappa = 0.725), but moderate for washout (kappa = 0.443) among three readers. The inter-reader agreements between two readers were substantial for overall LR-TR categorization (kappa = 0.734, 0.727, 0.652), LR-TR viable (kappa = 0.719, 0.752, 0.678), and LR-TR non-viable (kappa = 0.758, 0.760, 0.694), which were at the same level as the inter-reader agreements among three readers. In addition, the inter-reader agreements between two readers were substantial for arterial-phase hyperenhancement (kappa = 0.733, 0.766, 0.678), but moderate for washout (kappa = 0.473, 0.422, 0.446), which were at the same level as the inter-reader agreements among three readers.

CONCLUSION

LR-TR algorithm demonstrated overall substantial inter-reader agreement among radiologists with different seniority.

Deep learning-based arterial subtraction images improve the detection of LR-TR algorithm for viable HCC on extracellular agents-enhanced MRI

PURPOSE

To determine the role of deep learning-based arterial subtraction images in viability assessment on extracellular agents-enhanced MRI using LR-TR algorithm.

METHODS

Patients diagnosed with HCC who underwent locoregional therapy were retrospectively collected. We constructed a deep learning-based subtraction model and automatically generated arterial subtraction images. Two radiologists evaluated LR-TR category on ordinary images and then evaluated again on ordinary images plus arterial subtraction images after a 2-month washout period. The reference standard for viability was tumor stain on the digital subtraction hepatic angiography within 1 month after MRI.

RESULTS

286 observations of 105 patients were ultimately enrolled. 157 observations were viable and 129 observations were nonviable according to the reference standard. The sensitivity and accuracy of LR-TR algorithm for detecting viable HCC significantly increased with the application of arterial subtraction images (87.9% vs. 67.5%, p < 0.001; 86.4% vs. 75.9%, p < 0.001). And the specificity slightly decreased without significant difference when the arterial subtraction images were added (84.5% vs. 86.0%, p = 0.687). The AUC of LR-TR algorithm significantly increased with the addition of arterial subtraction images (0.862 vs. 0.768, p < 0.001). The arterial subtraction images also improved inter-reader agreement (0.857 vs. 0.727).

CONCLUSION

Extended application of deep learning-based arterial subtraction images on extracellular agents-enhanced MRI can increase the sensitivity of LR-TR algorithm for detecting viable HCC without significant change in specificity.

Prospective evaluation of Gadoxetate-enhanced magnetic resonance imaging and computed tomography for hepatocellular carcinoma detection and transplant eligibility assessment with explant histopathology correlation

BACKGROUND

We aimed to prospectively compare the diagnostic performance of gadoxetic acid-enhanced MRI (EOB-MRI) and contrast-enhanced Computed Tomography (CECT) for hepatocellular carcinoma (HCC) detection and liver transplant (LT) eligibility assessment in cirrhotic patients with explant histopathology correlation.

METHODS

In this prospective, single-institution ethics-approved study, 101 cirrhotic patients were enrolled consecutively from the pre-LT clinic with written informed consent. Patients underwent CECT and EOB-MRI alternately every 3 months until LT or study exclusion. Two blinded radiologists independently scored hepatic lesions on CECT and EOB-MRI utilizing the liver imaging reporting and data system (LI-RADS) version 2018. Liver explant histopathology was the reference standard. Pre-LT eligibility accuracies with EOB-MRI and CECT as per Milan criteria (MC) were assessed in reference to post-LT explant histopathology. Lesion-level and patient-level statistical analyses were performed.

RESULTS

Sixty patients (49 men; age 33-72 years) underwent LT successfully. One hundred four non-treated HCC and 42 viable HCC in previously treated HCC were identified at explant histopathology. For LR-4/5 category lesions, EOB-MRI had a higher pooled sensitivity (86.7% versus 75.3%, p <  0.001) but lower specificity (84.6% versus 100%, p <  0.001) compared to CECT. EOB-MRI had a sensitivity twice that of CECT (65.9% versus 32.2%, p <  0.001) when all HCC identified at explant histopathology were included in the analysis instead of imaging visible lesions only. Disregarding the hepatobiliary phase resulted in a significant drop in EOB-MRI performance (86.7 to 72.8%, p <  0.001). EOB-MRI had significantly lower pooled sensitivity and specificity versus CECT in the LR5 category with lesion size < 2 cm (50% versus 79%, p = 0.002 and 88.9% versus 100%, p = 0.002). EOB-MRI had higher sensitivity (84.8% versus 75%, p <  0.037) compared to CECT for detecting < 2 cm viable HCC in treated lesions. Accuracies of LT eligibility assessment were comparable between EOB-MRI (90-91.7%, p = 0.156) and CECT (90-95%, p = 0.158).

CONCLUSION

EOB-MRI had superior sensitivity for HCC detection; however, with lower specificity compared to CECT in LR4/5 category lesions while it was inferior to CECT in the LR5 category under 2 cm. The accuracy for LT eligibility assessment based on MC was not significantly different between EOB-MRI and CECT.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03342677 , Registered: November 17, 2017.

Optional MRI sequences for LI-RADS: why, what, and how?

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver worldwide. Noninvasive diagnosis of HCC is possible based on imaging features, without the need for tissue diagnosis. Liver Imaging Reporting and Data System (LI-RADS) CT/MRI diagnostic algorithm allows for standardized radiological interpretation and reporting of imaging studies for patients at high risk for HCC. Diagnostic categories of LR-1 to LR-5 designate each liver observation to reflect the probability of overall malignancy, HCC, or benignity based on imaging features, where LR-5 category has > 95% probability of HCC. Optimal imaging protocol and scanning technique as described by the technical recommendations for LI-RADS are essential for the depiction of features to accurately characterize liver observations. The LI-RADS MRI technical guidelines recommend the minimum required sequences of T1-weighted out-of-phase and in-phase Imaging, T2-weighted Imaging, and multiphase T1-weighted Imaging. Additional sequences, including diffusion-weighted imaging, subtraction imaging, and the hepatobiliary phase when using gadobenate dimeglumine as contrast, improve diagnostic confidence, but are not required by the guidelines. These optional sequences can help differentiate true lesions from pseudolesions, detect additional observations, identify parenchymal observations when other sequences are suboptimal, and improve observations conspicuity. This manuscript reviews the optional sequences, the advantages they offer, and discusses technical optimization of these sequences to obtain the highest image quality and to avoid common artifacts.

[CT/MRI Liver Imaging Reporting and Data System (LI-RADS): Standardization, Evidence, and Future Direction]

The liver imaging reporting and data system (LI-RADS) has been developed with the support of the American College of Radiology to standardize the diagnosis and evaluation of treatment response of hepatocellular carcinoma (HCC). The CT/MRI LI-RADS version 2018 has been incorporated in the American Association for the Study of Liver Diseases guidance. This review examines the effect of CT/MRI LI-RADS on the standardized reporting of liver imaging, and the evidence in diagnosing HCC and evaluating treatment response after locoregional treatment using CT/MRI LI-RADS. The results are compared with other HCC diagnosis guidelines, and future directions are described.

Use of "Diagnostic Yield" in Imaging Research Reports: Results from Articles Published in Two General Radiology Journals

OBJECTIVE

"Diagnostic yield," also referred to as the detection rate, is a parameter positioned between diagnostic accuracy and diagnosis-related patient outcomes in research studies that assess diagnostic tests. Unfamiliarity with the term may lead to incorrect usage and delivery of information. Herein, we evaluate the level of proper use of the term "diagnostic yield" and its related parameters in articles published in Radiology and Korean Journal of Radiology (KJR).

MATERIALS AND METHODS

Potentially relevant articles published since 2012 in these journals were identified using MEDLINE and PubMed Central databases. The initial search yielded 239 articles. We evaluated whether the correct definition and study setting of "diagnostic yield" or "detection rate" were used and whether the articles also reported companion parameters for false-positive results. We calculated the proportion of articles that correctly used these parameters and evaluated whether the proportion increased with time (2012-2016 vs. 2017-2022).

RESULTS

Among 39 eligible articles (19 from Radiology and 20 from KJR), 17 (43.6%; 11 from Radiology and 6 from KJR) correctly defined "diagnostic yield" or "detection rate." The remaining 22 articles used "diagnostic yield" or "detection rate" with incorrect meanings such as "diagnostic performance" or "sensitivity." The proportion of correctly used diagnostic terms was higher in the studies published in Radiology than in those published in KJR (57.9% vs. 30.0%). The proportion improved with time in Radiology (33.3% vs. 80.0%), whereas no improvement was observed in KJR over time (33.3% vs. 27.3%). The proportion of studies reporting companion parameters was similar between journals (72.7% vs. 66.7%), and no considerable improvement was observed over time.

CONCLUSION

Overall, a minority of articles accurately used "diagnostic yield" or "detection rate." Incorrect usage of the terms was more frequent without improvement over time in KJR than in Radiology. Therefore, improvements are required in the use and reporting of these parameters.

LI-RADS Treatment Response versus Modified RECIST for Diagnosing Viable Hepatocellular Carcinoma after Locoregional Therapy: A Systematic Review and Meta-Analysis of Comparative Studies

Purpose

To systematically compare the performance of liver imaging reporting and data system treatment response (LR-TR) with the modified Response Evaluation Criteria in Solid Tumors (mRECIST) for diagnosing viable hepatocellular carcinoma (HCC) treated with locoregional therapy (LRT).

Materials and Methods

Original studies of intra-individual comparisons between the diagnostic performance of LR-TR and mRECIST using dynamic contrast-enhanced CT or MRI were searched in MEDLINE and EMBASE, up to August 25, 2021. The reference standard for tumor viability was surgical pathology. The meta-analytic pooled sensitivity and specificity of the viable category using each criterion were calculated using a bivariate random-effects model and compared using bivariate meta-regression.

Results

For five eligible studies (430 patients with 631 treated observations), the pooled per-lesion sensitivities and specificities were 58% (95% confidence interval [CI], 45%–70%) and 93% (95% CI, 88%–96%) for the LR-TR viable category and 56% (95% CI, 42%–69%) and 86% (95% CI, 72%–94%) for the mRECIST viable category, respectively. The LR-TR viable category provided significantly higher pooled specificity (p < 0.01) than the mRECIST but comparable pooled sensitivity (p = 0.53).

Conclusion

The LR-TR algorithm demonstrated better specificity than mRECIST, without a significant difference in sensitivity for the diagnosis of pathologically viable HCC after LRT.

Per-Feature Accuracy of Liver Imaging Reporting and Data System Locoregional Treatment Response Algorithm: A Systematic Review and Meta-Analysis

We aimed to investigate the accuracy of each imaging feature of LI-RADS treatment response (LR-TR) viable category for diagnosing tumor viability of locoregional therapy (LRT)-treated HCC. Studies evaluating the per feature accuracy of the LR-TR viable category on dynamic contrast-enhanced CT or MRI were identified in databases. A bivariate random-effects model was used to calculate the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of LR-TR viable features. Ten studies assessing the accuracies of LR-TR viable features (1153 treated observations in 971 patients) were included. The pooled sensitivities and specificities for diagnosing viable HCC were 81% (95% confidence interval [CI], 63-92%) and 95% (95% CI, 88-98%) for nodular, mass-like, or irregular thick tissue (NMLIT) with arterial phase hyperenhancement (APHE), 55% (95% CI, 34-75%) and 96% (95% CI, 94-98%) for NMLIT with washout appearance, and 21% (95% CI, 6-53%) and 98% (95% CI, 92-100%) for NMLIT with enhancement similar to pretreatment, respectively. Of these features, APHE showed the highest pooled DOR (81 [95% CI, 25-261]), followed by washout appearance (32 [95% CI, 13-82]) and enhancement similar to pretreatment (14 [95% CI, 5-39]). In conclusion, APHE provided the highest sensitivity and DOR for diagnosing viable HCC following LRT, while enhancement similar to pretreatment showed suboptimal performance.