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早期骨关节炎模型中自动层状软骨T2弛豫时间分析方法的评估

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

作者信息

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

机构信息

Chondrometrics GmbH, Freilassing, Germany.

Research Program for Musculoskeletal Imaging, Institute of Imaging & Functional Musculoskeletal Research, Center of Anatomy & Cell Biology, Paracelsus Medical University, Strubergasse 21, 5020, Salzburg, Austria.

出版信息

Skeletal Radiol. 2025 Mar;54(3):571-584. doi: 10.1007/s00256-024-04786-1. Epub 2024 Sep 4.

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), or from the 1st echo only (1) of multi-echo-spin-echo (MESE) MRIs acquired by the Osteoarthritis Initiative (OAI). Because of its greater accuracy, only the All 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/1 and 0.88 ± 0.03/0.88 ± 0.03 (All/1) 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/1). The All 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 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.

摘要

目的

通过比较影像学正常的膝关节(对侧关节间隙变窄或有其他影像学骨关节炎(OA)体征的CL-JSN)和无对侧关节间隙变窄或其他影像学OA体征的膝关节(CL-noROA),在技术和临床层面验证一种全自动层状软骨成分(基于MRI的T2)分析方法。

材料与方法

使用骨关节炎倡议(OAI)采集的多回波自旋回波(MESE)MRI的所有7个回波(全部)或仅第1个回波(1)中手动分割的股胫软骨(n = 72)训练二维U-Net。由于其更高的准确性,随后仅将全部U-Net应用于来自OAI健康参考队列(n = 10)、CL-JSN(n = 39)以及(1:1)匹配的CL-noROA膝关节(n = 39)的膝关节,这些膝关节均有手动专家分割,并且应用于982个无专家分割的不匹配CL-noROA膝关节。

结果

在股胫软骨板上,自动与手动专家软骨分割之间的一致性(Dice相似系数)在0.82±0.05/0.79±0.06(全部/1)之间,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(全部/1)。在匹配(Cohen's D≤0.54)和不匹配(D≤0.54)的比较中,全部U-Net对CL-JSN和CL-noROA膝关节之间的横断面层状T2差异显示出与匹配的手动分析(D≤0.48)相似的敏感性。纵向来看,在匹配(D≤0.51)和不匹配(D≤0.43)的比较中,全部U-Net对CL-JSN与CL-noROA差异也显示出与匹配的手动分析(D≤0.41)相似的敏感性。

结论

与手动专家分析相比,全自动T2分析在早期OA模型中显示出高度一致性、可接受的准确性以及对横断面和纵向层状T2差异相似的敏感性。

试验注册

Clinicaltrials.gov标识符:NCT00080171。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d886/11769870/10f5194e3acb/256_2024_4786_Fig1_HTML.jpg

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