Prathyush Chirra, Joseph Sleiman, Namita S Gandhi, Ilyssa O Gordon, Mohsen Hariri, Mark Baker, Ronald Ottichilo, David H Bruining, Jacob A Kurowski, Satish E Viswanath, Florian Rieder
{"title":"用放射组学检测克罗恩病限制型患者磁共振肠造影中的炎症和纤维化。","authors":"Prathyush Chirra, Joseph Sleiman, Namita S Gandhi, Ilyssa O Gordon, Mohsen Hariri, Mark Baker, Ronald Ottichilo, David H Bruining, Jacob A Kurowski, Satish E Viswanath, Florian Rieder","doi":"10.1093/ecco-jcc/jjae073","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and aims: </strong>Non-invasive cross-sectional imaging via magnetic resonance enterography [MRE] offers excellent accuracy for the diagnosis of stricturing complications in Crohn's disease [CD] but is limited in determining the degrees of fibrosis and inflammation within a stricture. We developed and validated a radiomics-based machine-learning model for separately characterizing the degree of histopathological inflammation and fibrosis in CD strictures and compared it to centrally read visual radiologist scoring of MRE.</p><p><strong>Methods: </strong>This single-centre, cross-sectional study included 51 CD patients [n = 34 for discovery; n = 17 for validation] with terminal ileal strictures confirmed on diagnostic MRE within 15 weeks of resection. Histopathological specimens were scored for inflammation and fibrosis and spatially linked with corresponding pre-surgical MRE sequences. Annotated stricture regions on MRE were scored visually by radiologists as well as underwent 3D radiomics-based machine learning analysis; both were evaluated against histopathology.</p><p><strong>Results: </strong>Two distinct sets of radiomic features capturing textural heterogeneity within strictures were linked with each of severe inflammation or severe fibrosis across both the discovery (area under the curve [AUC = 0.69, 0.83] and validation [AUC = 0.67, 0.78] cohorts. Radiologist visual scoring had an AUC = 0.67 for identifying severe inflammation and AUC = 0.35 for severe fibrosis. Use of combined radiomics and radiologist scoring robustly augmented identification of severe inflammation [AUC = 0.79] and modestly improved assessment of severe fibrosis [AUC = 0.79 for severe fibrosis] over individual approaches.</p><p><strong>Conclusions: </strong>Radiomic features of CD strictures on MRE can accurately identify severe histopathological inflammation and severe histopathological fibrosis, as well as augment performance of the radiologist visual scoring in stricture characterization.</p>","PeriodicalId":94074,"journal":{"name":"Journal of Crohn's & colitis","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Radiomics to Detect Inflammation and Fibrosis on Magnetic Resonance Enterography in Stricturing Crohn's Disease.\",\"authors\":\"Prathyush Chirra, Joseph Sleiman, Namita S Gandhi, Ilyssa O Gordon, Mohsen Hariri, Mark Baker, Ronald Ottichilo, David H Bruining, Jacob A Kurowski, Satish E Viswanath, Florian Rieder\",\"doi\":\"10.1093/ecco-jcc/jjae073\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background and aims: </strong>Non-invasive cross-sectional imaging via magnetic resonance enterography [MRE] offers excellent accuracy for the diagnosis of stricturing complications in Crohn's disease [CD] but is limited in determining the degrees of fibrosis and inflammation within a stricture. We developed and validated a radiomics-based machine-learning model for separately characterizing the degree of histopathological inflammation and fibrosis in CD strictures and compared it to centrally read visual radiologist scoring of MRE.</p><p><strong>Methods: </strong>This single-centre, cross-sectional study included 51 CD patients [n = 34 for discovery; n = 17 for validation] with terminal ileal strictures confirmed on diagnostic MRE within 15 weeks of resection. Histopathological specimens were scored for inflammation and fibrosis and spatially linked with corresponding pre-surgical MRE sequences. Annotated stricture regions on MRE were scored visually by radiologists as well as underwent 3D radiomics-based machine learning analysis; both were evaluated against histopathology.</p><p><strong>Results: </strong>Two distinct sets of radiomic features capturing textural heterogeneity within strictures were linked with each of severe inflammation or severe fibrosis across both the discovery (area under the curve [AUC = 0.69, 0.83] and validation [AUC = 0.67, 0.78] cohorts. Radiologist visual scoring had an AUC = 0.67 for identifying severe inflammation and AUC = 0.35 for severe fibrosis. Use of combined radiomics and radiologist scoring robustly augmented identification of severe inflammation [AUC = 0.79] and modestly improved assessment of severe fibrosis [AUC = 0.79 for severe fibrosis] over individual approaches.</p><p><strong>Conclusions: </strong>Radiomic features of CD strictures on MRE can accurately identify severe histopathological inflammation and severe histopathological fibrosis, as well as augment performance of the radiologist visual scoring in stricture characterization.</p>\",\"PeriodicalId\":94074,\"journal\":{\"name\":\"Journal of Crohn's & colitis\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Crohn's & colitis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/ecco-jcc/jjae073\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Crohn's & colitis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/ecco-jcc/jjae073","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Radiomics to Detect Inflammation and Fibrosis on Magnetic Resonance Enterography in Stricturing Crohn's Disease.
Background and aims: Non-invasive cross-sectional imaging via magnetic resonance enterography [MRE] offers excellent accuracy for the diagnosis of stricturing complications in Crohn's disease [CD] but is limited in determining the degrees of fibrosis and inflammation within a stricture. We developed and validated a radiomics-based machine-learning model for separately characterizing the degree of histopathological inflammation and fibrosis in CD strictures and compared it to centrally read visual radiologist scoring of MRE.
Methods: This single-centre, cross-sectional study included 51 CD patients [n = 34 for discovery; n = 17 for validation] with terminal ileal strictures confirmed on diagnostic MRE within 15 weeks of resection. Histopathological specimens were scored for inflammation and fibrosis and spatially linked with corresponding pre-surgical MRE sequences. Annotated stricture regions on MRE were scored visually by radiologists as well as underwent 3D radiomics-based machine learning analysis; both were evaluated against histopathology.
Results: Two distinct sets of radiomic features capturing textural heterogeneity within strictures were linked with each of severe inflammation or severe fibrosis across both the discovery (area under the curve [AUC = 0.69, 0.83] and validation [AUC = 0.67, 0.78] cohorts. Radiologist visual scoring had an AUC = 0.67 for identifying severe inflammation and AUC = 0.35 for severe fibrosis. Use of combined radiomics and radiologist scoring robustly augmented identification of severe inflammation [AUC = 0.79] and modestly improved assessment of severe fibrosis [AUC = 0.79 for severe fibrosis] over individual approaches.
Conclusions: Radiomic features of CD strictures on MRE can accurately identify severe histopathological inflammation and severe histopathological fibrosis, as well as augment performance of the radiologist visual scoring in stricture characterization.