PURPOSE: To evaluate a simplified Liver Imaging Reporting and Data System (LI-RADS) algorithm to improve interreader agreement while maintaining diagnostic performance for HCC. MATERIALS AND METHODS: MRI scans of 84 cirrhotic patients with 104 distinct liver observations were retrospectively selected to equivocally match each of the LI-RADS grades (LR1-5) using histopathology and imaging follow up as standard of reference. Four independent radiologists categorized all observations as benign (LR1-2) or potentially malignant (LR3-5) and determined LI-RADS based imaging features including observation size, arterial phase hyperenhancement, washout, capsule appearance and threshold growth for LR3-5 observations and timed their readouts. LR3-5 observations were categorized according to the LI-RADS v2014 algorithm and according to a modified LI-RADS (mLI-RADS) version. Diagnostic performance and Interreader agreement were determined for LI-RADS and mLI-RADS using receiver operating characteristics (ROC) and Fleiss' and Cohen's Kappa analysis respectively. RESULTS: ROC analysis revealed equal diagnostic performance for LI-RADS and mLI-RADS (area under the ROC curve=0.91). Interreader agreement was higher using mLI-RADS as compared to current LI-RADS showing an improved overall (κ=0.53±0.04 vs. 0.45±0.04), and pair-wise agreement between most readers (κ range 0.44-0.62 vs. 0.35-0.60) at a reduced median evaluation time (51 vs. 62s per observation, p<0.0001). CONCLUSION: Focusing on observation size and washout criteria using a modified, stepwise LI-RADS decision tree for LR3-5 observations results in higher interobserver reliability and faster categorization while maintaining diagnostic accuracy.
PURPOSE: To evaluate a simplified Liver Imaging Reporting and Data System (LI-RADS) algorithm to improve interreader agreement while maintaining diagnostic performance for HCC. MATERIALS AND METHODS: MRI scans of 84 cirrhotic patients with 104 distinct liver observations were retrospectively selected to equivocally match each of the LI-RADS grades (LR1-5) using histopathology and imaging follow up as standard of reference. Four independent radiologists categorized all observations as benign (LR1-2) or potentially malignant (LR3-5) and determined LI-RADS based imaging features including observation size, arterial phase hyperenhancement, washout, capsule appearance and threshold growth for LR3-5 observations and timed their readouts. LR3-5 observations were categorized according to the LI-RADS v2014 algorithm and according to a modified LI-RADS (mLI-RADS) version. Diagnostic performance and Interreader agreement were determined for LI-RADS and mLI-RADS using receiver operating characteristics (ROC) and Fleiss' and Cohen's Kappa analysis respectively. RESULTS: ROC analysis revealed equal diagnostic performance for LI-RADS and mLI-RADS (area under the ROC curve=0.91). Interreader agreement was higher using mLI-RADS as compared to current LI-RADS showing an improved overall (κ=0.53±0.04 vs. 0.45±0.04), and pair-wise agreement between most readers (κ range 0.44-0.62 vs. 0.35-0.60) at a reduced median evaluation time (51 vs. 62s per observation, p<0.0001). CONCLUSION: Focusing on observation size and washout criteria using a modified, stepwise LI-RADS decision tree for LR3-5 observations results in higher interobserver reliability and faster categorization while maintaining diagnostic accuracy.
Authors: Daniel R Ludwig; Tyler J Fraum; Roberto Cannella; David H Ballard; Richard Tsai; Muhammad Naeem; Maverick LeBlanc; Amber Salter; Allan Tsung; Anup S Shetty; Amir A Borhani; Alessandro Furlan; Kathryn J Fowler Journal: Abdom Radiol (NY) Date: 2019-06
Authors: Victoria Chernyak; Kathryn J Fowler; Aya Kamaya; Ania Z Kielar; Khaled M Elsayes; Mustafa R Bashir; Yuko Kono; Richard K Do; Donald G Mitchell; Amit G Singal; An Tang; Claude B Sirlin Journal: Radiology Date: 2018-09-25 Impact factor: 11.105
Authors: Katherine S Cools; Andrew M Moon; Lauren M B Burke; Katrina A McGinty; Paula D Strassle; David A Gerber Journal: Liver Transpl Date: 2019-12-20 Impact factor: 5.799
Authors: Mario Amrehn; Stefan Steidl; Reinier Kortekaas; Maddalena Strumia; Markus Weingarten; Markus Kowarschik; Andreas Maier Journal: Int J Biomed Imaging Date: 2019-09-05