{"title":"基于核磁共振成像的全髋关节置换术附近软组织反应放射学分析","authors":"Kevin M. Koch, Hollis G. Potter, Matthew F. Koff","doi":"10.1002/jor.25970","DOIUrl":null,"url":null,"abstract":"<p>This study applied radiomics to MRI data for automated classification of soft tissue abnormalities near total hip arthroplasty (THA). A total of 126 subjects with 1.5 T MRI of symptomatic THA were included in the analysis. Peri-prosthetic soft tissue regions of interest were manually segmented and classified by an expert radiologist. An established radiomics library was used to extract 96 features from 2D image patches across segmented regions. Logistic regression was employed as the primary radiomic classifier, achieving an average area under curve (AUC) of 0.71 in differentiating tissue classifications spanning normal, infected, and several inflammatory, noninfectious categories. Notably, infection cases were identified with the highest accuracy, attaining an AUC of 0.79. Statement of Clinical Significance: This study demonstrates that radiomics applied to MRI data can effectively automate the classification of soft tissue abnormalities in symptomatic total hip arthroplasty, particularly in differentiating periprosthetic infections.</p>","PeriodicalId":16650,"journal":{"name":"Journal of Orthopaedic Research®","volume":"43 1","pages":"183-191"},"PeriodicalIF":2.1000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MRI-based radiomic analysis of soft tissue reactions near total hip arthroplasty\",\"authors\":\"Kevin M. Koch, Hollis G. Potter, Matthew F. Koff\",\"doi\":\"10.1002/jor.25970\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This study applied radiomics to MRI data for automated classification of soft tissue abnormalities near total hip arthroplasty (THA). A total of 126 subjects with 1.5 T MRI of symptomatic THA were included in the analysis. Peri-prosthetic soft tissue regions of interest were manually segmented and classified by an expert radiologist. An established radiomics library was used to extract 96 features from 2D image patches across segmented regions. Logistic regression was employed as the primary radiomic classifier, achieving an average area under curve (AUC) of 0.71 in differentiating tissue classifications spanning normal, infected, and several inflammatory, noninfectious categories. Notably, infection cases were identified with the highest accuracy, attaining an AUC of 0.79. Statement of Clinical Significance: This study demonstrates that radiomics applied to MRI data can effectively automate the classification of soft tissue abnormalities in symptomatic total hip arthroplasty, particularly in differentiating periprosthetic infections.</p>\",\"PeriodicalId\":16650,\"journal\":{\"name\":\"Journal of Orthopaedic Research®\",\"volume\":\"43 1\",\"pages\":\"183-191\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Orthopaedic Research®\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jor.25970\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ORTHOPEDICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Orthopaedic Research®","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jor.25970","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
MRI-based radiomic analysis of soft tissue reactions near total hip arthroplasty
This study applied radiomics to MRI data for automated classification of soft tissue abnormalities near total hip arthroplasty (THA). A total of 126 subjects with 1.5 T MRI of symptomatic THA were included in the analysis. Peri-prosthetic soft tissue regions of interest were manually segmented and classified by an expert radiologist. An established radiomics library was used to extract 96 features from 2D image patches across segmented regions. Logistic regression was employed as the primary radiomic classifier, achieving an average area under curve (AUC) of 0.71 in differentiating tissue classifications spanning normal, infected, and several inflammatory, noninfectious categories. Notably, infection cases were identified with the highest accuracy, attaining an AUC of 0.79. Statement of Clinical Significance: This study demonstrates that radiomics applied to MRI data can effectively automate the classification of soft tissue abnormalities in symptomatic total hip arthroplasty, particularly in differentiating periprosthetic infections.
期刊介绍:
The Journal of Orthopaedic Research is the forum for the rapid publication of high quality reports of new information on the full spectrum of orthopaedic research, including life sciences, engineering, translational, and clinical studies.