DOP080 Artificial Intelligence-Based Prediction of Nancy Grade Activity Using Digital Pathology in Ulcerative Colitis Patients

Background

Therapeutic goals of Ulcerative colitis have shifted from symptom control to achieving endoscopic and histologic remission as a key predictor of long-term outcomes. The Nancy Histological Index is widely used to assess histologic activity in UC, but it relies on subjective pathologist evaluation, leading to potential variability and increased workload. While Artificial Intelligence (AI) models have been explored for histologic assessment, few utilize comprehensive, cell-level annotations across biopsy slides. This study aims to develop AI model for predicting Nancy grades directly from whole-slide images, supporting the need for more objective, scalable, and efficient histologic grading in patients of UC.

Methods

A dataset of 174 digital pathology slides from UC patients, collected between January 2018 and December 2019 at Samsung Medical Center, was analyzed. A two-stage AI model was developed to predict Nancy grade. In the first stage, a U-Net-based segmentation model classified neutrophils, plasma cells, lymphocytes, and eosinophils, assessing correlation between Nancy grade and each cell type. In the second stage, predicted neutrophil counts were used to predict Nancy grade activity, comparing logistic regression (LR), random forest (RF), and extreme gradient boosting models (XGB). Model performance was evaluated using area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity.

Results

The segmentation model at a 200 threshold achieved a Mean IoU 0.67, and Dice score 0.70 for testing slides. Spearman correlations for activity on the Nancy grade were 0.71 for neutrophils, 0.56 for eosinophils, 0.34 for lymphocytes, and 0.31 for plasma cells, all statistically significant (P <0.05). The activity prediction models (neutrophils alone vs. incorporating eosinophils) achieved test the AUROCs of 0.90 vs. 0.95 for LR, 0.85 vs. 0.94 for RF, and 0.86 vs. 0.92 for XGB, respectively. The best-performing model was LR incorporating eosinophils, with an accuracy of 0.92, sensitivity of 0.88, Positive Predictive Value of 0.96, Positive Likelihood Raio of 21, and F1 score of 0.91 (Figure 1). Our model identified additional active disease in 22 out of 76 cases that had been classified as inactive by pathologists.

Conclusion

This study demonstrates that an AI model predicting Nancy grade can serve as a clinically valuable tool in UC management. The model, which incorporates neutrophil and eosinophil counts, achieved high accuracy and sensitivity, providing an efficient and objective method for histologic assessment. Notably, the model identified additional active cases in slides initially rated as inactive by pathologists, underscoring its potential to enhance detection accuracy in clinical settings.

References

1.Iacucci, M., et al., Artificial Intelligence Enabled Histological Prediction of Remission or Activity and Clinical Outcomes in Ulcerative Colitis. Gastroenterology, 2023. 164(7): p. 1180-1188.e2.

2.Gui, X., et al., PICaSSO Histologic Remission Index (PHRI) in ulcerative colitis: development of a novel simplified histological score for monitoring mucosal healing and predicting clinical outcomes and its applicability in an artificial intelligence system. Gut, 2022. 71(5): p. 889-898.

3.Parigi, T.L., et al., Neutrophil-only Histological Assessment of Ulcerative Colitis Correlates with Endoscopic Activity and Predicts Long-term Outcomes in a Multicentre Study. J Crohns Colitis, 2023. 17(12): p. 1931-1938.