Deep Learning–Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast‐Enhanced MRI

作者：Hao‐Ran Dai, Yuyao Xiao, Caixia Fu, Robert Grimm, Heinrich von Busch, Bram Stieltjes, Moon Hyung Choi, Zhoubing Xu, Guillaume Chabin, Chun Yang, Mengsu Zeng · 发表于：Journal of Magnetic Resonance Imaging · 年份：2024 · DOI：10.1002/jmri.29404 · 被引用次数：15 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、MRI in cancer diagnosis、Radiomics and Machine Learning in Medical Imaging

BACKGROUND: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. PURPOSE: To assess the performance of the deep learning-based artificial intelligence (AI) software in identifying and measuring lesions on contrast-enhanced magnetic resonance imaging (MRI) images in patients with FLLs. STUDY TYPE: Retrospective. SUBJECTS: 395 patients with 1149 FLLs. FIELD STRENGTH/SEQUENCE: The 1.5 T and 3 T scanners, including T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. ASSESSMENT: The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut-off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10-20, 20-40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. STATISTICAL TESTS: McNemar test, Bland-Altman analyses, Friedman test, Pearson's chi-squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P-value <0.05 was considered statistically significant. RESULTS: The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radio...