Evaluation of an automated laminar cartilage T2 relaxation time analysis method in an early osteoarthritis model.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-09-04 DOI:10.1007/s00256-024-04786-1

Wolfgang Wirth, Susanne Maschek, Anna Wisser, Jana Eder, Christian F Baumgartner, Akshay Chaudhari, Francis Berenbaum, Felix Eckstein

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引用次数: 0

Abstract

Objective: A fully automated laminar cartilage composition (MRI-based T2) analysis method was technically and clinically validated by comparing radiographically normal knees with (CL-JSN) and without contra-lateral joint space narrowing or other signs of radiographic osteoarthritis (OA, CL-noROA).

Materials and methods: 2D U-Nets were trained from manually segmented femorotibial cartilages (n = 72) from all 7 echoes (All_E), or from the 1st echo only (1^st_E) of multi-echo-spin-echo (MESE) MRIs acquired by the Osteoarthritis Initiative (OAI). Because of its greater accuracy, only the All_E U-Net was then applied to knees from the OAI healthy reference cohort (n = 10), CL-JSN (n = 39), and (1:1) matched CL-noROA knees (n = 39) that all had manual expert segmentation, and to 982 non-matched CL-noROA knees without expert segmentation.

Results: The agreement (Dice similarity coefficient) between automated vs. manual expert cartilage segmentation was between 0.82 ± 0.05/0.79 ± 0.06 (All_E/1^st_E) and 0.88 ± 0.03/0.88 ± 0.03 (All_E/1^st_E) across femorotibial cartilage plates. The deviation between automated vs. manually derived laminar T2 reached up to - 2.2 ± 2.6 ms/ + 4.1 ± 10.2 ms (All_E/1^st_E). The All_E U-Net showed a similar sensitivity to cross-sectional laminar T2 differences between CL-JSN and CL-noROA knees in the matched (Cohen's D ≤ 0.54) and the non-matched (D ≤ 0.54) comparison as the matched manual analyses (D ≤ 0.48). Longitudinally, the All_E U-Net also showed a similar sensitivity to CL-JSN vs. CS-noROA differences in the matched (D ≤ 0.51) and the non-matched (D ≤ 0.43) comparison as matched manual analyses (D ≤ 0.41).

Conclusion: The fully automated T2 analysis showed a high agreement, acceptable accuracy, and similar sensitivity to cross-sectional and longitudinal laminar T2 differences in an early OA model, compared with manual expert analysis.

Trial registration: Clinicaltrials.gov identification: NCT00080171.

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在早期骨关节炎模型中评估层状软骨T2弛豫时间自动分析方法

目的通过比较有（CL-JSN）和无反外侧关节间隙狭窄或其他影像学骨关节炎（OA，CL-noROA）迹象的影像学正常膝关节，对全自动层状软骨成分（基于 MRI 的 T2）分析方法进行技术和临床验证。材料与方法：在骨关节炎倡议组织（OAI）获得的多回波-自旋回波（MESE）MRI 图像中，从全部 7 个回波（AllE）或仅从第 1 个回波（1stE）人工分割的股胫骨软骨（n = 72）中训练二维 U-Nets。由于 AllE U-Net 的准确性更高，因此只将其应用于 OAI 健康参考队列（n = 10）、CL-JSN（n = 39）和（1:1）匹配的 CL-noROA 膝关节（n = 39），所有这些膝关节都进行了人工专家分割，还将其应用于 982 个未进行专家分割的非匹配 CL-noROA 膝关节：结果：在股胫骨软骨板上，自动与人工专家软骨分割的一致性（Dice相似系数）分别为0.82 ± 0.05/0.79 ± 0.06（AllE/1stE）和0.88 ± 0.03/0.88 ± 0.03（AllE/1stE）。自动与手动得出的层状 T2 之间的偏差高达 - 2.2 ± 2.6 ms/ + 4.1 ± 10.2 ms（AllE/1stE）。在匹配（Cohen's D ≤ 0.54）和非匹配（D ≤ 0.54）比较中，AllE U-Net 对 CL-JSN 和 CL-noROA 膝关节横截面层状 T2 差异的敏感性与匹配人工分析（D ≤ 0.48）相似。纵向来看，AllE U-Net在匹配（D≤0.51）和非匹配（D≤0.43）对比中对CL-JSN与CS-noROA差异的敏感度也与匹配人工分析（D≤0.41）相似：结论：与专家人工分析相比，全自动T2分析在早期OA模型中显示出较高的一致性、可接受的准确性以及对横截面和纵向层状T2差异相似的敏感性：试验注册：Clinicaltrials.gov identification：试验注册：Clinicaltrials.gov 识别：NCT00080171。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.