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Longitudinal evolution of CT and MRI LI-RADS v2014 category 1, 2, 3, and 4 observations

  • Hepatobiliary-Pancreas
  • Published:
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Abstract

Objectives

This study assesses the risk of progression of Liver Imaging Reporting and Data System (LI-RADS) categories, and the effects of inter-exam changes in modality or radiologist on LI-RADS categorization.

Methods

Clinical LI-RADS v2014 CT and MRI exams at our institution between January 2014 and September 2017 were retrospectively identified. Untreated LR-1, LR-2, LR-3, and LR-4 observations with at least one follow-up exam were included. Three hundred and seventy-two observations in 214 patients (149 male, 65 female, mean age 61 ± 10 years) were included during the study period (715 exams total). Cumulative incidence curves for progression to malignant LI-RADS categories (LR-5 or LR-M) and to LR-4 or higher were generated for each index category and compared using log-rank tests with a resampling extension. Relationships between inter-exam changes in LI-RADS category and modality or radiologist, adjusted for inter-exam time intervals, were modeled using mixed effect logistic regressions.

Results

Median inter-exam follow-up interval and total follow-up duration were 123 and 227 days, respectively. Index LR-1, LR-2, LR-3, and LR-4 differed significantly in their cumulative incidences of progression to malignant categories (p < 0.0001), which were 0%, 2%, 7%, and 32% at 6 months, respectively. Index LR-1, LR-2, and LR-3 differed significantly in cumulative incidences of progression to LR-4 or higher (p = 0.003). MRI-MRI exam pairs had more stable LI-RADS categorization compared to CT-CT (OR = 0.460, p = 0.0018).

Conclusions

LI-RADS observations demonstrate increasing risk of progression to malignancy with increasing category ranging from 0% for LR-1 to 32% for LR-4 at 6 months. Inter-exam modality changes are associated with LI-RADS category changes.

Key Points

• While the majority of LR-2 observations remain stable over long-term follow-up, LR-3 and especially LR-4 observations have a higher risk for category progression.

• Category transitions between sequential exams using different modalities (CT vs. MRI) may reflect modality differences rather than biological change. MRI, especially with the same type of contrast agent, may provide the most reproducible categorization, although this needs additional validation.

• In a clinical practice setting, in which radiologists refer to prior imaging and reports, there was no significant association between changes in radiologist and changes in LI-RADS categorization.

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Abbreviations

HCC:

Hepatocellular carcinoma

IQR:

Inter-quartile range

OR:

Odds ratio

LI-RADS:

Liver Imaging Reporting and Data System

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Acknowledgements

A part of these results was presented at the 2017 RSNA Annual Meeting as a paper presentation.

Funding

The authors acknowledge grant support from National Institutes of Health T32 EB005970-09.

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Authors and Affiliations

  1. Liver Imaging Group, Department of Radiology, University of California San Diego, San Diego, CA, USA

    Cheng William Hong, Adrija Mamidipalli, Jonathan C. Hooker, Soudabeh Fazeli Dehkordy, Saya Igarashi, Mohanad Alhumayed, Paul Murphy & Claude B. Sirlin

  2. School of Medicine, University of California San Diego, San Diego, CA, USA

    Charlie C. Park

  3. Computational and Applied Statistics Laboratory, University of California San Diego, San Diego, CA, USA

    Yuko Kono & Rohit Loomba

  4. Division of Gastroenterology, Department of Medicine, University of California San Diego, San Diego, CA, USA

    Tanya Wolfson & Anthony Gamst

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  1. Cheng William Hong
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Correspondence to Claude B. Sirlin.

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Guarantor

The scientific guarantor of this publication is Cheng William Hong, MD MS.

Conflict of interest

The authors of this manuscript declare relationships with the following companies:

Claude B Sirlin, MD, is a member of the external advisory board of AMRA Medical and Guerbet and speaker for GE Healthcare and Bayer (fees paid to University of California Regents); a consultant for Boehringer Ingelheim; receives research grants from Gilead, GE Healthcare, Siemens, GE MRI, Bayer AMRI, GE Digital, GE US, and ACR Innovation; gives education presentations for Medscape; and performs contracted research for ICON Medical Imaging/Enanta, Philips, Gilead, Shire, Virtualscopics, Intercept, and Synageva.

Statistics and biometry

Tanya Wolfson, MA, and Anthony Gamst, PhD, kindly provided statistical advice for this manuscript. Both of these authors have significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• performed at one institution

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Hong, C.W., Park, C.C., Mamidipalli, A. et al. Longitudinal evolution of CT and MRI LI-RADS v2014 category 1, 2, 3, and 4 observations. Eur Radiol 29, 5073–5081 (2019). https://doi.org/10.1007/s00330-019-06058-2

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