Wirth Wolfgang, Maschek Susanne, Wisser Anna, Eder Jana, Baumgartner Christian F, Chaudhari Akshay, Berenbaum Francis, Eckstein Felix
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.
Skeletal Radiol. 2025 Mar;54(3):571-584. doi: 10.1007/s00256-024-04786-1. Epub 2024 Sep 4.
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).
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.
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).
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.
Clinicaltrials.gov identification: NCT00080171.
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。
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。
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。
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。
Clinicaltrials.gov标识符:NCT00080171。