DOP045 Novel AI-Driven Detection, Localisation and Quantification of Neutrophils for Prediction of Early Response to Therapy in a Phase 2 Ulcerative Colitis Clinical Trial
Authors: Iacucci M , Vadori V , Meseguer P , Zammarchi I , Santacroce G , Del Amor R , Zardo D , Hayes B , Crotty R , Burke L , Kolawole B B , Chaudhuri U , Owen C , Gottlieb K T , Eastman W J , Ghosh S , Naranjo V , Grisan E 
Published: January 2025

Background

Achieving histological remission is a key treatment goal in Ulcerative Colitis (UC) as it is associated with better disease management and predicts response to therapy. Neutrophils are the key drivers of disease activity in UC and are, therefore, integral to many histological scoring systems. However, current scoring methods remain complex and subject to inter-rater variability. Thus, our study aimed to develop a novel AI-driven model to standardise detection, localisation, and quantification of neutrophils, supporting evaluation of histological activity and enabling prediction of early therapy response in a cohort of UC patients from the Phase 2 clinical trial of Mirikizumab (NCT02589665).

Methods

An AI-driven system was developed by integrating two deep learning models to [1] segment whole slide images (WSI) into epithelium, lamina propria, other tissue, and background and [2] detect neutrophils. Outputs from these models were combined to compute neutrophil densities in the lamina propria, epithelium, and overall tissue. Optimal cell density cut-offs were determined by the Youden Index to assess disease activity (Geboes score &gt;2B.0) and to predict early response to therapy at weeks 12 (W12) and 52 (W52), defined as histological improvement (Geboes score &lt;3.1) and histological remission (Geboes score ≤2B.0). Validation performance was assessed using 5-fold cross-validation.

Results

266 WSIs from active UC patients were considered. The model for segmentation and detection reached an average Dice similarity coefficient of 67.6% and F1 score of 72.0%, respectively. Table 1 shows the AI diagnostic performance in assessing disease activity and predicting response to therapy at W12 and W52. The system identified an overall optimal neutrophil density cut-off (cells/mm²) predictive of disease activity of 112.0 (53.8 and 153 for epithelium and lamina propria, respectively), with an accuracy of 80.8% (77.4% and 81.6%, respectively). Moreover, the overall optimal cut-off of neutrophils to predict histological remission at W52 was &lt;71.8 (&lt;65.9 and &lt;113.9 for epithelium and lamina propria, respectively), with an accuracy of 85.0% (71.3% and 81.3%, respectively).

Conclusion

Our AI-based system shows strong potential as an automated tool for detecting, localising, and quantifying neutrophils to assess histological activity and predict response to therapy at weeks 12 and 52 in a clinical trial UC cohort. Although further refinement and extensive validation are needed, this framework could be applied in clinical trials and routine clinical practice, offering objective guidance for personalised management in UC patients.

References

[1]Vadori, Valentina, et al. "CISCA and CytoDArk0: a Cell Instance Segmentation and Classification method for histo (patho) logical image Analyses and a new, open, Nissl-stained dataset for brain cytoarchitecture studies." arXiv preprint arXiv:2409.04175 (2024).

[2]Santacroce G, Meseguer P, Zammarchi I, et al. P406 A novel active learning-based digital pathology protocol annotation for histologic assessment in Ulcerative Colitis using PICaSSO Histologic Remission Index (PHRI). Journal of Crohn's and Colitis, Volume 18, Issue Supplement_1, January 2024, Pages i843–i844.