Concurrent AI assistance with LI-RADS classification for contrast enhanced MRI of focal hepatic nodules: a multi-reader, multi-case study.

Journal: Abdominal radiology (New York)
Published Date: Sep 16, 2025

Abstract

PURPOSE: The Liver Imaging Reporting and Data System (LI-RADS) assessment is subject to inter-reader variability. The present study aimed to evaluate the impact of an artificial intelligence (AI) system on the accuracy and inter-reader agreement of LI-RADS classification based on contrast-enhanced magnetic resonance imaging among radiologists with varying experience levels. METHODS: This single-center, multi-reader, multi-case retrospective study included 120 patients with 200 focal liver lesions who underwent abdominal contrast-enhanced magnetic resonance imaging examinations between June 2023 and May 2024. Five radiologists with different experience levels independently assessed LI-RADS classification and imaging features with and without AI assistance. The reference standard was established by consensus between two expert radiologists. Accuracy was used to measure the performance of AI systems and radiologists. Kappa or intraclass correlation coefficient was utilized to estimate inter-reader agreement. RESULTS: The LI-RADS categories were as follows: 33.5% of LR-3 (67/200), 29.0% of LR-4 (58/200), 33.5% of LR-5 (67/200), and 4.0% of LR-M (8/200) cases. The AI system significantly improved the overall accuracy of LI-RADS classification from 69.9 to 80.1% (p < 0.001), with the most notable improvement among junior radiologists from 65.7 to 79.7% (p < 0.001). Inter-reader agreement for LI-RADS classification was significantly higher with AI assistance compared to that without (weighted Cohen's kappa, 0.655 vs. 0.812, p < 0.001). The AI system also enhanced the accuracy and inter-reader agreement for imaging features, including non-rim arterial phase hyperenhancement, non-peripheral washout, and restricted diffusion. Additionally, inter-reader agreement for lesion size measurements improved, with intraclass correlation coefficient changing from 0.857 to 0.951 (p < 0.001). CONCLUSION: The AI system significantly increases accuracy and inter-reader agreement of LI-RADS 3/4/5/M classification, particularly benefiting junior radiologists.

Authors

  • Xiang Qin
    Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou North Avenue No.1838, 510515, Guangzhou, China.
  • Lisheng Huang
    Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou North Avenue No.1838, 510515, Guangzhou, China.
  • Yuanfeng Wei
    Department of Hematology, The First Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China.
  • Hongxiang Li
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
  • Yuting Wu
  • Jingmeng Zhong
    Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou North Avenue No.1838, 510515, Guangzhou, China.
  • Mingjue Jian
    Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou North Avenue No.1838, 510515, Guangzhou, China.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Zeyu Zheng
    Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, PR China.
  • Yikai Xu
  • Chenggong Yan
    The D-Lab, Dept of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Center+, Maastricht, The Netherlands.

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