Impact of LI-RADS CT and MRI Ancillary Features on Diagnostic Performance: An Individual Participant Data Meta-Analysis

作者：Nicole Abedrabbo, Eric Lam, Matthew D. F. McInnes, Haben Dawit, Diana Kadi, Christian B. van der Pol, Jean‐Paul Salameh, Brooke Levis, Haresh Naringrekar, Emily Lerner, Robert G. Adamo, Mostafa Alabousi, Adam Polikoff, Alessandro Furlan, An Tang, Andrea S. Kierans, Amit G. Singal, Ashwini Arvind, Ayman S. Alhasan, Bin Song, Brian C. Allen, Caecilia S. Reiner, Christopher Clarke, Daniel R. Ludwig, Federico Díaz Telli, Federico Piñero, Grzegorz Rosiak, Hanyu Jiang, Heejin Kwon, Hong Wei, Hyo‐Jin Kang, Ijin Joo, Jeong Ah Hwang, Ji Hye Min, Ji Soo Song, Jin Wang, Joanna Podgórska, John R. Eisenbrey, Krzysztof Bartnik, Li‐Da Chen, Maxime Ronot, Milena Cerny, Nieun Seo, Shengxiang Rao, Roberto Cannella, Sang Hyun Choi, So Yeon Kim, Tyler J. Fraum, Wentao Wang, Woo Kyoung Jeong, Xiang Jing, Yeun‐Yoon Kim, Zhen Kang, Mustafa R. Bashir, Andreu F. Costa · 发表于：Radiology · 年份：2025 · DOI：10.1148/radiol.242278 · 被引用次数：7 · 研究领域：Radiomics and Machine Learning in Medical Imaging、MRI in cancer diagnosis

In an individual participant data meta-analysis, applying individual CT and MRI ancillary features to Liver Imaging Reporting and Data System categories 1–5 observations did not improve diagnostic performance compared with major features.