Deep Learning Analysis of Histologic Images from Intestinal Specimen Reveals Adipocyte Shrinkage and Mast Cell Infiltration to Predict Postoperative Crohn Disease

Abstract

Most patients with Crohn disease (CD), a chronic inflammatory gastrointestinal disease, experience recurrence despite treatment, including surgical resection. However, methods for predicting recurrence remain unclear. This study aimed to predict postoperative recurrence of CD by computational analysis of histopathologic images and to extract histologic characteristics associated with recurrence. A total of 68 patients who underwent surgical resection of the intestine were included in this study and were categorized into two groups according to the presence or absence of postoperative disease recurrence within 2 years after surgery. Recurrence was defined using the CD Activity Index and the Rutgeerts score. Whole-slide images of surgical specimens were analyzed using deep learning model EfficientNet-b5, which achieved a highly accurate prediction of recurrence (area under the curve, 0.995). Moreover, subserosal tissue images with adipose cells enabled highly accurate prediction. Adipose cell morphology showed significant between-group differences in adipose cell size, cell-to-cell distance, and cell flattening values. These findings suggest that adipocyte shrinkage is an important histologic characteristic associated with recurrence. Moreover, there was a significant between-group difference in the degree of mast cell infiltration in the subserosa. These findings show the importance of mesenteric adipose tissue in patient prognosis and CD pathophysiology. These findings also suggest that deep learning-based artificial intelligence enables the extraction of meaningful histologic features.