26.05.2022 | Imaging Informatics and Artificial Intelligence
Intestinal fibrosis classification in patients with Crohn’s disease using CT enterography–based deep learning: comparisons with radiomics and radiologists
Jixin Meng, Zixin Luo, Zhihui Chen, Jie Zhou, Zhao Chen, Baolan Lu, Mengchen Zhang, Yangdi Wang, Chenglang Yuan, Xiaodi Shen, Qinqin Huang, Zhuya Zhang, Ziyin Ye, Qinghua Cao, Zhiyang Zhou, Yikai Xu, Ren Mao, Minhu Chen, Canhui Sun, Ziping Li, Shi-Ting Feng, Xiaochun Meng, Bingsheng Huang, Xuehua Li
Einloggen, um Zugang zu erhalten
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.
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.
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).
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.
Could computed tomography enterography (CTE)–based deep learning model (DLM) accurately distinguish intestinal fibrosis severity in patients with Crohn’s disease (CD)?
In this cross-sectional study that included 235 patients with CD, DLM achieved better performance than that of two radiologists’ interpretation and was not inferior to RM with significant differences and much shorter processing time.
This DLM may accurately distinguish the degree of intestinal fibrosis in patients with CD and guide gastroenterologists to formulate individualized treatment strategies for those with bowel strictures.