Published online Apr 14, 2026. doi: 10.3748/wjg.v32.i14.116041
Revised: December 31, 2025
Accepted: February 6, 2026
Published online: April 14, 2026
Processing time: 153 Days and 3.7 Hours
The Liver Imaging Reporting and Data System (LI-RADS) is widely used for the diagnosis of hepatocellular carcinoma, but feature scoring by radiologists is subjective and time-consuming. An urgent need exists for an objective and efficient radiologist-supervised automated LI-RADS categorization system.
To develop an evidence-based radiologist-supervised automated LI-RADS grade 3 (LR-3), 4 (LR-4) and 5 (LR-5) categorization system (Evi-LIRADS) through quantitative feature characterization, following LI-RADS v2018.
This retrospective multicenter study (April 2012-November 2022) included untreated patients with suspected hepatocellular carcinoma undergoing gadoxetic acid-enhanced magnetic resonance imaging. Lesions from center 1 were partitioned into a development set (275 lesions used for five-fold cross-validation) and an internal testing set (62 lesions). Lesions from centers 2 (85 lesions) and 3 (104 lesions) constituted two external testing sets. Evi-LIRADS was designed by emulating the decision-making process of radiologists through a series of image processing algorithms, to recognize nonrim arterial phase hyper-enhancement, nonperipheral washout, and enhancing capsule, which provided detailed assessments of feature locations and patterns, improving the transparency of feature classification. Based on the three major image features and the automatically segmented lesion size, LI-RADS categories were assigned using LI-RADS v2018 algorithm. Feature classification was evaluated using area under the receiver operating characteristic curve. LI-RADS categorization was assessed by accuracy.
The internal dataset included 337 patients from center 1, while external datasets comprised 76 patients from center 2 and 97 patients from center 3. For feature classification, areas under the receiver operating characteristic curves were 0.975, 0.898, and 0.940 for arterial phase hyper-enhancement; 0.803, 0.824, and 0.850 for washout; 0.759, 0.800, and 0.784 for capsule across three datasets. Three-class LI-RADS categorization among LR-3, LR-4 and LR-5 achieved accuracies of 80.6%, 74.1%, and 77.9%, respectively, surpassing comparison methods (58.6%-69.6%). LI-RADS categorization between LR-3 and combined LR-4/LR-5 achieved 95.2%, 88.2%, and 90.4% accuracies for the three datasets, respectively. The visualization provided detailed feature locations and patterns. Evi-LIRADS saved an average of 21.1 seconds per patient (58.8% of the time) compared with radiologists, excluding radiologists’ quality control time.
Following LI-RADS guidelines and radiologists’ decision-making process, Evi-LIRADS was developed through quantitative feature characterization, demonstrating good accuracy, robust generalization, improved efficiency, enhanced clinical relevance, and improved transparency.
Core Tip: This study developed transparent classifiers for three of the major Liver Imaging Reporting and Data System (LI-RADS) features: Arterial phase hyper-enhancement, washout, and capsule. Then, LI-RADS categories were assigned in accordance with the LI-RADS v2018 guidelines. By following LI-RADS guidelines and emulating the decision-making process of radiologists, the model achieved radiologist-supervised automated LI-RADS categorization, through specialized feature characterization algorithms that provide explicit evidence for feature classification, thereby improving transparency for radiologists and patients. Categorization among LI-RADS grade 3, 4 and 5 achieved accuracies of 80.6%, 74.1%, and 77.9% for the internal testing set and two external testing sets, respectively.