Wolfgang Wirth, Susanne Maschek, Anna Wisser, Jana Eder, Christian F Baumgartner, Akshay Chaudhari, Francis Berenbaum, Felix Eckstein
{"title":"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","authors":"Wolfgang Wirth, Susanne Maschek, Anna Wisser, Jana Eder, Christian F Baumgartner, Akshay Chaudhari, Francis Berenbaum, Felix Eckstein","doi":"10.1007/s00256-024-04786-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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).</p><p><strong>Materials and methods: </strong>2D U-Nets were trained from manually segmented femorotibial cartilages (n = 72) from all 7 echoes (All<sub>E</sub>), or from the 1st echo only (1<sup>st</sup><sub>E</sub>) of multi-echo-spin-echo (MESE) MRIs acquired by the Osteoarthritis Initiative (OAI). Because of its greater accuracy, only the All<sub>E</sub> 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.</p><p><strong>Results: </strong>The agreement (Dice similarity coefficient) between automated vs. manual expert cartilage segmentation was between 0.82 ± 0.05/0.79 ± 0.06 (All<sub>E</sub>/1<sup>st</sup><sub>E)</sub> and 0.88 ± 0.03/0.88 ± 0.03 (All<sub>E</sub>/1<sup>st</sup><sub>E</sub>) 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<sub>E</sub>/1<sup>st</sup><sub>E</sub>). The All<sub>E</sub> 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<sub>E</sub> 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).</p><p><strong>Conclusion: </strong>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.</p><p><strong>Trial registration: </strong>Clinicaltrials.gov identification: NCT00080171.</p>","PeriodicalId":21783,"journal":{"name":"Skeletal Radiology","volume":" ","pages":"571-584"},"PeriodicalIF":1.9000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11769870/pdf/","citationCount":"0","resultStr":"{\"title\":\"Evaluation of an automated laminar cartilage T2 relaxation time analysis method in an early osteoarthritis model.\",\"authors\":\"Wolfgang Wirth, Susanne Maschek, Anna Wisser, Jana Eder, Christian F Baumgartner, Akshay Chaudhari, Francis Berenbaum, Felix Eckstein\",\"doi\":\"10.1007/s00256-024-04786-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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).</p><p><strong>Materials and methods: </strong>2D U-Nets were trained from manually segmented femorotibial cartilages (n = 72) from all 7 echoes (All<sub>E</sub>), or from the 1st echo only (1<sup>st</sup><sub>E</sub>) of multi-echo-spin-echo (MESE) MRIs acquired by the Osteoarthritis Initiative (OAI). Because of its greater accuracy, only the All<sub>E</sub> 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.</p><p><strong>Results: </strong>The agreement (Dice similarity coefficient) between automated vs. manual expert cartilage segmentation was between 0.82 ± 0.05/0.79 ± 0.06 (All<sub>E</sub>/1<sup>st</sup><sub>E)</sub> and 0.88 ± 0.03/0.88 ± 0.03 (All<sub>E</sub>/1<sup>st</sup><sub>E</sub>) 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<sub>E</sub>/1<sup>st</sup><sub>E</sub>). The All<sub>E</sub> 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<sub>E</sub> 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).</p><p><strong>Conclusion: </strong>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.</p><p><strong>Trial registration: </strong>Clinicaltrials.gov identification: NCT00080171.</p>\",\"PeriodicalId\":21783,\"journal\":{\"name\":\"Skeletal Radiology\",\"volume\":\" \",\"pages\":\"571-584\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11769870/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Skeletal Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00256-024-04786-1\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/9/4 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"ORTHOPEDICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Skeletal Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00256-024-04786-1","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/4 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
Evaluation of an automated laminar cartilage T2 relaxation time analysis method in an early osteoarthritis model.
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 (AllE), or from the 1st echo only (1stE) of multi-echo-spin-echo (MESE) MRIs acquired by the Osteoarthritis Initiative (OAI). Because of its greater accuracy, only the AllE 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 (AllE/1stE) and 0.88 ± 0.03/0.88 ± 0.03 (AllE/1stE) 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 (AllE/1stE). The AllE 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 AllE 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.
期刊介绍：
Skeletal Radiology provides a forum for the dissemination of current knowledge and information dealing with disorders of the musculoskeletal system including the spine. While emphasizing the radiological aspects of the many varied skeletal abnormalities, the journal also adopts an interdisciplinary approach, reflecting the membership of the International Skeletal Society. Thus, the anatomical, pathological, physiological, clinical, metabolic and epidemiological aspects of the many entities affecting the skeleton receive appropriate consideration.
This is the Journal of the International Skeletal Society and the Official Journal of the Society of Skeletal Radiology and the Australasian Musculoskelelal Imaging Group.
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