AI-Assisted Glucocorticoid Treatment Response Prediction of Active Ulcerative Active Patients.

Abstract

Background: Glucocorticoids are recommended for the induction and remission phase of ulcerative colitis (UC). Early identification of glucocorticoid therapy response contributes to more precise treatment management. We aim to use deep learning model to predict glucocorticoid response prognosis in active UC.

Methods: From January 2006 to December 2023, 485 intestinal histological whole slide images (WSIs) of 212 UC patients from two medical centers in China was collected. We developed and validated a deep learning model (UCG-SwinT) based on WSI and clinical data to predict the treatment response of glucocorticoid induction therapy. Response was defined as steroid effectiveness and steroid dependence. We used area under the curves (AUCs) to evaluate the performance of the model and compared it to clinical factors. Grad-CAM was used to visualize the histological features the model focused when predicting treatment response.

Results: The AUCs of predicting response in training, validation, and external testing set were 0.750, 0.727, and 0.723, respectively. The UCG-SwinT model performs better while combining histopathological images with clinical data than simply inputting histopathological images, with AUCs of 0.826, 0.731, and 0.725 in predicting treatment response in the training, validation, and external testing cohorts and outperformed all clinical factors. Grad-CAM showed that increased inflammatory cells and intestinal mucosal microvascular dilation are related to glucocorticoid response in UC patients.

Conclusions: UCG-SwinT has the potential to predict glucocorticoid response in active UC patients and has guiding significance for individualized clinical treatment.