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LI-RADS Treatment Response Algorithm: Performance and Diagnostic Accuracy.

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The 2017 Liver Imaging Reporting and Data System (LI-RADS) algorithm shows high predictive value for assessing hepatocellular carcinoma (HCC) treatment response after bland arterial embolization, though interreader agreement is moderate.

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • The 2017 Liver Imaging Reporting and Data System (LI-RADS) introduced a treatment response algorithm for hepatocellular carcinoma (HCC).
  • This algorithm aims to standardize evaluation of treatment response to guide subsequent therapies.
  • Validation of the LI-RADS 2017 Treatment Response algorithm in literature is limited.

Purpose of the Study:

  • To evaluate the performance of the LI-RADS 2017 Treatment Response algorithm.
  • To assess its accuracy in predicting histopathologic viability of HCC after bland arterial embolization.

Main Methods:

  • Retrospective study of patients who underwent bland arterial embolization for HCC and subsequent liver transplantation.
  • Three radiologists independently applied the LI-RADS 2017 Treatment Response algorithm to CT/MRI scans.
  • Comparison of radiologic assessment with posttransplant histopathology findings.

Main Results:

  • The LR-TR Viable category showed 60%-65% accuracy and 86%-96% positive predictive value for predicting incomplete tumor necrosis.
  • The LR-TR Nonviable category demonstrated 67%-71% accuracy and 81%-87% negative predictive value for predicting complete tumor necrosis.
  • Moderate interreader agreement (κ = 0.55) was observed for the LR-TR category, with 27% of lesions deemed Equivocal.

Conclusions:

  • The LI-RADS 2017 Treatment Response algorithm exhibits high predictive value for assessing HCC viability post-bland arterial embolization.
  • The algorithm demonstrates moderate interreader association when lesions are categorized as Viable or Nonviable.
  • Further validation and refinement may be needed, especially for Equivocal cases.