Radiomics to Detect Inflammation and Fibrosis on Magnetic Resonance Enterography in Stricturing Crohn's Disease.

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
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Abstract

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.

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。
背景和目的:通过磁共振肠造影(MRE)进行的无创横断面成像在诊断克罗恩病(CD)狭窄并发症方面具有极高的准确性,但在确定狭窄内纤维化和炎症程度方面却有局限性。我们开发并验证了一种基于放射组学的机器学习模型,该模型可分别描述 CD 狭窄处组织病理学炎症和纤维化的程度,并将其与放射科医师对 MRE 的集中读片目视评分进行比较:这项单中心横断面研究共纳入了 51 名 CD 患者(发现时为 34 人;验证时为 17 人),这些患者在切除术后 15 周内经 MRE 诊断确诊为回肠末端狭窄。对组织病理学标本的炎症和纤维化进行评分,并与相应的术前 MRE 序列进行空间连接。放射科医生对 MRE 上标注的狭窄区域进行目测评分,并进行基于三维放射组学的机器学习分析;两者均对照组织病理学进行评估:在发现队列(曲线下面积(AUC)=0.69,0.83)和验证队列(AUC=0.67,0.78)中,两组不同的放射组学特征分别与严重炎症或严重纤维化相关,这两组特征捕捉到了狭窄内的纹理异质性。放射医师目测评分识别严重炎症的AUC=0.67,识别严重纤维化的AUC=0.35。与单独的方法相比,联合使用放射组学和放射医师评分可有力地增强对严重炎症的识别(AUC=0.79),并适度改善对严重纤维化的评估(严重纤维化的AUC=0.79):结论:MRE上CD狭窄的放射学特征可准确识别严重的组织病理学炎症和严重的组织病理学纤维化,并提高放射医师在狭窄特征描述中的视觉评分性能。
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