LI-RADS Treatment Response Algorithm: Performance and Diagnostic Accuracy

• Published in: Radiology Vol. 292; no. 1; pp. 226 - 234
• Main Authors: Shropshire, Erin L., Chaudhry, Mohammad, Miller, Chad M., Allen, Brian C., Bozdogan, Erol, Cardona, Diana M., King, Lindsay Y., Janas, Gemini L., Do, Richard K., Kim, Charles Y., Ronald, James, Bashir, Mustafa R.
• Format: Journal Article
• Language: English
• Published: United States Radiological Society of North America 01.07.2019
• Subjects: Adult; Aged; Algorithms; Carcinoma, Hepatocellular - diagnostic imaging; Carcinoma, Hepatocellular - therapy; Embolization, Therapeutic - methods; Female; Humans; Liver - diagnostic imaging; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - therapy; Magnetic Resonance Imaging - methods; Male; Middle Aged; Multimodal Imaging - methods; Original Research; Radiology Information Systems; Reproducibility of Results; Retrospective Studies; Tomography, X-Ray Computed - methods; Treatment Outcome
• ISSN: 0033-8419; 1527-1315; 1527-1315
• DOI: 10.1148/radiol.2019182135

Background In 2017, the Liver Imaging Reporting and Data System (LI-RADS) included an algorithm for the assessment of hepatocellular carcinoma (HCC) treated with local-regional therapy. The aim of the algorithm was to enable standardized evaluation of treatment response to guide subsequent therapy. However, the performance of the algorithm has not yet been validated in the literature. Purpose To evaluate the performance of the LI-RADS 2017 Treatment Response algorithm for assessing the histopathologic viability of HCC treated with bland arterial embolization. Materials and Methods This retrospective study included patients who underwent bland arterial embolization for HCC between 2006 and 2016 and subsequent liver transplantation. Three radiologists independently assessed all treated lesions by using the CT/MRI LI-RADS 2017 Treatment Response algorithm. Radiology and posttransplant histopathology reports were then compared. Lesions were categorized on the basis of explant pathologic findings as either completely (100%) or incompletely (<100%) necrotic, and performance characteristics and predictive values for the LI-RADS Treatment Response (LR-TR) Viable and Nonviable categories were calculated for each reader. Interreader association was calculated by using the Fleiss κ. Results A total of 45 adults (mean age, 57.1 years ± 8.2; 13 women) with 63 total lesions were included. For predicting incomplete histopathologic tumor necrosis, the accuracy of the LR-TR Viable category for the three readers was 60%-65%, and the positive predictive value was 86%-96%. For predicting complete histopathologic tumor necrosis, the accuracy of the LR-TR Nonviable category was 67%-71%, and the negative predictive value was 81%-87%. By consensus, 17 (27%) of 63 lesions were categorized as LR-TR Equivocal, and 12 of these lesions were incompletely necrotic. Interreader association for the LR-TR category was moderate (κ = 0.55; 95% confidence interval: 0.47, 0.67). Conclusion The Liver Imaging Reporting and Data System 2017 Treatment Response algorithm had high predictive value and moderate interreader association for the histopathologic viability of hepatocellular carcinoma treated with bland arterial embolization when lesions were assessed as Viable or Nonviable. © RSNA, 2019 See also the editorial by Gervais in this issue.