Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists

作者：Meng, Jixin; Luo, Zixin; Chen, Zhihui; Zhou, Jie; Chen, Zhao; Lu, Baolan; Zhang, Mengchen; Wang, Yangdi; Yuan, Chenglang; Shen, Xiaodi; Huang, Qinqin; Zhang, Zhuya; Ye, Ziyin; Cao, Qinghua; Zhou, Zhiyang; Xu, Yikai; Mao, Ren; Chen, Minhu; Sun, Canhui; Li, Ziping; Feng, Shi-Ting; Meng, Xiaochun; Huang, Bingsheng^*; Li, Xuehua^*

来源：European Radiology, 2022, 32(12): 8692-8705.

DOI：10.1007/s00330-022-08842-z

• Crohn disease
• Fibrosis
• Deep learning
• Tomography
• X-ray computed

Objectives Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed to develop and validate a CTE-based deep learning model (DLM) for characterizing bowel fibrosis more efficiently. Methods We enrolled 312 bowel segments of 235 CD patients (median age, 33 years old) from three hospitals in this retrospective study. A training cohort and test cohort 1 were recruited from center 1, while test cohort 2 from centers 2 and 3. All patients performed CTE within 3 months before surgery. The histological fibrosis was semi-quantitatively assessed. A DLM was constructed in the training cohort based on a 3D deep convolutional neural network with 10-fold cross-validation, and external independent validation was conducted on the test cohorts. The radiomics model (RM) was developed with 4 selected radiomics features extracted from CTE images by using logistic regression. The evaluation of CTE images was performed by two radiologists. DeLong's test and a non-inferiority test were used to compare the models' performance. Results DLM distinguished none-mild from moderate-severe bowel fibrosis with an area under the receiver operator characteristic curve (AUC) of 0.828 in the training cohort and 0.811, 0.808, and 0.839 in the total test cohort, test cohorts 1 and 2, respectively. In the total test cohort, DLM achieved better performance than two radiologists (*1 AUC = 0.579, *2 AUC = 0.646; both p < 0.05) and was not inferior to RM (AUC = 0.813, p < 0.05). The total processing time for DLM was much shorter than that of RM (p < 0.001). Conclusion DLM is better than radiologists in diagnosing intestinal fibrosis on CTE in patients with CD and not inferior to RM; furthermore, it is more time-saving compared to RM.

• 单位
  南方医科大学; 1; 中山大学; 6