Abstract
Myenteric plexitis is correlated with postoperative recurrence of Crohn's disease when relying on traditional statistical methods. However, comprehensive assessment of myenteric plexus remains challenging. This study aimed to develop and validate a deep learning system to predict postoperative recurrence through automatic screening and identification of features of the muscular layer and myenteric plexus. We retrospectively reviewed 205 patients who underwent bowel resection surgery from two hospitals. Patients were divided into a training cohort [n = 108], an internal validation cohort [n = 47], and an external validation cohort [n = 50]. A total of 190 960 patches from 278 whole-slide images of surgical specimens were analysed using the ResNet50 encoder, and 6144 features were extracted after transfer learning. We used five robust algorithms to construct classification models. The performances of the models were evaluated based on the area under the receiver operating characteristic curve [AUC] in three cohorts. The stacking model achieved satisfactory accuracy in predicting postoperative recurrence of CD in the training cohort (AUC: 0.980; 95% confidence interval [CI] 0.960-0.999), internal validation cohort [AUC: 0.908; 95% CI 0.823-0.992], and external validation cohort [AUC: 0.868; 95% CI 0.761-0.975]. The accuracy for identifying the severity of myenteric plexitis was 0.833, 0.745, and 0.694 in the training, internal validation and external validation cohorts, respectively. Our work initially established an interpretable stacking model based on features of the muscular layer and myenteric plexus extracted from histological images to identify the severity of myenteric plexitis and predict postoperative recurrence of CD.
Published Version
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