Rectal MRI radiomics inter- and intra-reader reliability: should we worry about that?

Abdominal Radiology(2022)

引用 5|浏览9
暂无评分
摘要
Purpose The aim of this review paper is to summarize the current literature regarding inter- and intra-reader reliability of radiomics on rectal MRI. Methods Original studies examining treatment response prediction in patients with rectal cancer following neoadjuvant therapy using rectal MRI-based radiomics between January 2010 and December 2021 were identified via a PubMed/Medline search. Studies in which intra- and/or inter-reader reliability had been reported were included in this review. Results Thirteen studies were selected, with an average number of patients of 145 (range, 20–649). All included studies evaluated T2-weighted imaging (T2WI) and/or diffusion-weighted imaging (DWI) sequences, while 3/13 (23%) also evaluated the contrast-enhanced T1-weighted imaging (T1WI) sequence. Most of the selected studies involved two readers (10/13, 77%), 6/13 (46%) studies used baseline MRI only, 1/13 (8%) study used restaging MRI only, and 6/13 (46%) used both. Segmentation was performed manually in 10/13 (77%) studies, and in a slight majority of studies (7/13, 54%), the entire tumor volume (3D VOI) was segmented, while 4/13 (31%) studies segmented the 2D ROI and 2/13 (15%) segmented both. Intraclass correlation coefficient (ICC) on intra-reader agreement varied from 0.73 to 0.93. ICC to assess inter-reader varied from 0.60 to 0.99. Overall, features obtained from baseline rectal MRI, using 3D VOI and first-order features, had higher agreement. Conclusion Based on our qualitative assessment of a small number of non-dedicated studies, there seems to be good reliability, particularly among low-order features extracted from the entire tumor volume using baseline MRI; however, direct evidence remains scarce. More targeted research in this area is required to quantitatively verify reliability, and before these novel radiomic techniques can be clinically adopted. Graphical abstract
更多
查看译文
关键词
Rectal cancer,Magnetic resonance imaging,Agreement,Reliability,Machine learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要