MRI LI-RADS Version 2018: Impact of and Reduction in Ancillary Features

Pol, Christian B. van der; Dhindsa, Kiret; Shergill, Ravi; Zha, Nanxi; Ferri, Melanie; Kagoma, Yoan K.; Lee, Stefanie Y.; Satkunasingham, Janakan; Wat, Josephine; Tsai, Scott S. H.

doi:10.2214/ajr.20.23031

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…In some previous studies, some larger lesions have a mosaic structure due to intralesional fatty components, necrosis, or hemorrhage at histopathology [ 8 , 24 , 29 ]. Similarly, the lesion contains lipids to change the degree and method of the arterial stage of the lesion, which also makes the lesion classify into the LR-3 or LR-4 categories due to the lack of nonrim APHE [ 9 , 24 ], which is similar to the results of Christian B. van der Pol’s study for filtering features by machine learning [ 15 ]. In this study, when AF-HCC and AF-LR were used to adjust the categories of LR-3/LR-4 lesions, the consistency between the two methods was good, and both the sensitivity and specificity for AF-HCC is higher than that for AF-LR, which makes a significant difference.…”

Section: Discussionmentioning

confidence: 81%

“…In previous LI-RADS-related studies, researchers were interested in screening out AFs to replace MFs and use AFs to improve the classification of small lesions or LR-3/4 lesions [ 15 , 19 – 23 ]. However, most of these studies screened features based on all lesions, which may have overlooked the impact of the frequency and diagnostic ability of AFs in LR-5 lesions on the results.…”

Section: Discussionmentioning

confidence: 99%

“…At the same time, although AFs can be selectively used for classification adjustment, improve detection and increase confidence, the number of AFs is as high as 21, and most of the features appear less frequently [ 15 ], taking into account that both MFs and AFs make the LI-RADS system more complex and significantly increase the workload of radiologists. Therefore, in this study, we tried to screen different combinations of high diagnostic efficiency AFs to adjust the categories of LR-3 and LR-4 lesions, hoping to improve the diagnostic ability of the above two types of lesions.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing LI-RADS: ancillary features screened from LR-3/4 categories can improve the diagnosis of HCC on MRI

Zhang,

Xv,

et al. 2024

BMC Gastroenterol

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Objective To determine the high-efficiency ancillary features (AFs) screened from LR-3/4 lesions and the HCC/non-HCC group and the diagnostic performance of LR3/4 observations. Materials and methods We retrospectively analyzed a total of 460 patients (with 473 nodules) classified into LR-3-LR-5 categories, including 311 cases of hepatocellular carcinoma (HCC), 6 cases of non-HCC malignant tumors, and 156 cases of benign lesions. Two faculty abdominal radiologists with experience in hepatic imaging reviewed and recorded the major features (MFs) and AFs of the Liver Imaging Reporting and Data System (LI-RADS). The frequency of the features and diagnostic performance were calculated with a logistic regression model. After applying the above AFs to LR-3/LR-4 observations, the sensitivity and specificity for HCC were compared. Results The average age of all patients was 54.24 ± 11.32 years, and the biochemical indicators ALT (P = 0.044), TBIL (P = 0.000), PLT (P = 0.004), AFP (P = 0.000) and Child‒Pugh class were significantly higher in the HCC group. MFs, mild-moderate T2 hyperintensity, restricted diffusion and AFs favoring HCC in addition to nodule-in-nodule appearance were common in the HCC group and LR-5 category. AFs screened from the HCC/non-HCC group (AF-HCC) were mild–moderate T2 hyperintensity, restricted diffusion, TP hypointensity, marked T2 hyperintensity and HBP isointensity (P = 0.005, < 0.001, = 0. 032, p < 0.001, = 0.013), and the AFs screened from LR-3/4 lesions (AF-LR) were restricted diffusion, mosaic architecture, fat in mass, marked T2 hyperintensity and HBP isointensity (P < 0.001, = 0.020, = 0.036, < 0.001, = 0.016), which were not exactly the same. After applying AF-HCC and AF-LR to LR-3 and LR-4 observations in HCC group and Non-HCC group, After the above grades changed, the diagnostic sensitivity for HCC were 84.96% using AF-HCC and 85.71% using AF-LR, the specificity were 89.26% using AF-HCC and 90.60% using AF-LR, which made a significant difference (P = 0.000). And the kappa value for the two methods of AF-HCC and AF–LR were 0.695, reaching a substantial agreement. Conclusion When adjusting for LR-3/LR-4 lesions, the screened AFs with high diagnostic ability can be used to optimize LI-RADS v2018; among them, AF-LR is recommended for better diagnostic capabilities.

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Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimizing LI-RADS: ancillary features screened from LR-3/4 categories can improve the diagnosis of HCC on MRI

Zhang,

Xv,

et al. 2024

BMC Gastroenterol

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“…A frequent issue with unrestricted databases is the incorrect documentation of imaging features as 'absent' that were either not applicable (eg, different imaging modality or technique used), or not evaluable (eg, imaging artifact or suboptimal contrast timing). [44][45][46] This can impact the calculation of metrics such as odds ratios. For example, LI-RADS can be applied to MRI using extracellular or hepatobiliary contrast agents, however several features of MRI LI-RADS can only be assessed using hepatobiliary agents such as hepatobiliary phase hypointensity.…”

Section: Data Documentationmentioning

confidence: 99%

“…A frequent issue with unrestricted databases is the incorrect documentation of imaging features as ‘absent’ that were either not applicable (eg, different imaging modality or technique used), or not evaluable (eg, imaging artifact or suboptimal contrast timing) 44–46 . This can impact the calculation of metrics such as odds ratios.…”

Section: Data Documentationmentioning

confidence: 99%

Best Practice for MRI Diagnostic Accuracy Research With Lessons and Examples from the LI‐RADS Individual Participant Data Group

van der Pol,

Costa,

Lam

et al. 2023

Magnetic Resonance Imaging

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Medical imaging diagnostic test accuracy research is strengthened by adhering to best practices for study design, data collection, data documentation, and study reporting. In this review, key elements of such research are discussed, and specific recommendations provided for optimizing diagnostic accuracy study execution to improve uniformity, minimize common sources of bias and avoid potential pitfalls. Examples are provided regarding study methodology and data collection practices based on insights gained by the liver imaging reporting and data system (LI‐RADS) individual participant data group, who have evaluated raw data from numerous MRI diagnostic accuracy studies for risk of bias and data integrity. The goal of this review is to outline strategies for investigators to improve research practices, and to help reviewers and readers better contextualize a study's findings while understanding its limitations.Level of Evidence5Technical EfficacyStage 3

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LI-RADS ancillary features favoring benignity: is there a role in LR-5 observations?

et al. 2021

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MRI LI-RADS Version 2018: Impact of and Reduction in Ancillary Features

Cited by 14 publications

References 15 publications

Optimizing LI-RADS: ancillary features screened from LR-3/4 categories can improve the diagnosis of HCC on MRI

Optimizing LI-RADS: ancillary features screened from LR-3/4 categories can improve the diagnosis of HCC on MRI

Best Practice for MRI Diagnostic Accuracy Research With Lessons and Examples from the LI‐RADS Individual Participant Data Group

LI-RADS ancillary features favoring benignity: is there a role in LR-5 observations?

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