Introduction

The achievement of histological remission (HR) is a key treatment goal in ulcerative colitis (UC), as it is suggestive of better disease management and predicts response to therapy.
The absence of neutrophils in epithelium and lamina is crucial to define HR. However, the role of neutrophil number and localisation remains unclear. We aimed to develop a novel artificial intelligence (AI)-driven system to automate neutrophil detection, localisation, and quantification, supporting assessment of HR and enable the prediction of response to therapy.

Aims & Methods

We developed an AI-driven system by integrating two deep learning models to (1) segment whole slide images (WSIs) into epithelium, crypts and lamina propria and to (2) detect and quantify neutrophils. Outputs from these models were combined to compute neutrophil densities in the lamina propria, epithelium, and overall tissue. The optimal neutrophil density cut-offs to assess disease activity (Geboes score >2B.0) in UC patients were identified in the AMAC phase 2 Mirikizumab trial, and subsequently validated in the multicentre prospective PICaSSO cohort study. Cut-offs to determine treatment response at weeks 12 and 52 (histological improvement [HI; Geboes score ≤3.1] and histological remission [HR; Geboes score ≤2B.0]), were also evaluated.

Results

Table 1 shows the system diagnostic performance in assessing disease activity and predicting response to therapy at W12 and W52 in the Mirikizumab cohort.
303 WSIs from active UC patients from the multicentre, randomised, double-blind, parallel-arm, placebo-controlled phase 2 clinical trial of Mirikizumab were analysed. The model yielded a DICE SØrensen of 0.57 for segmentation, precision of 82.3% and recall of 77.0% for neutrophil detection. The system identified an overall optimal neutrophil density cut-off (cells/mm²) predictive of disease activity of >21.7.0 (>10.1 and >30.4 for epithelium and lamina propria, respectively), with an accuracy of 86% (85% and 85%, respectively). Further validation in the multicenter PICaSSO cohort yielded to similar results.
Moreover, the overall optimal cut-off to predict histological remission at W52 was <20.8 (<10.4 and <27.9 for epithelium and lamina propria, respectively), with an accuracy of 82.0% (81% and 80%, respectively).

Conclusion

Our novel AI-based system demonstrates strong potential as an automated tool to detect, localise, and quantify neutrophils to assess histological activity and predict response to therapy in a clinical trial UC cohort and real-world settings. This framework could be applied in clinical trials and routine clinical practice, offering objective guidance for personalised management in UC patients.

References

[1] 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
[2] Graham S, Vu QD, Jahanifar M, et al. CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting. Med Image Anal. 2024;92:103047.

Disclosure

MI received research grants and consultant fees from Olympus, Pentax, Eli Lilly, and Janssen. All other authors declare no competing interests.

Introduction

Prospective evidence of the long-term efficacy of endoscopic submucosal dissection (ESD) for T1 colorectal cancer (CRC) is insufficient.

Aims & Methods

We aimed to evaluate long-term ESD outcomes for T1 CRC, focusing on the risk stratification criteria and necessity of additional surgery.
This large-scale, multicenter, prospective cohort study was conducted by Hiroshima GI Endoscopy Research Group. All consecutive patients who underwent colorectal ESD between January 2014 and December 2018 were included, and those with pathologically confirmed T1 CRC were analyzed. Patients were classified into low-risk and high-risk groups based on histopathological risk factors for lymph node metastasis in JSCCR guidelines. High-risk patients were categorized into those who underwent additional surgery (surgery group) and those who were followed up without surgery (follow-up group).

Results

Among 2357 patients who underwent ESD for 2478 early colorectal neoplasms, 431 were diagnosed with T1 CRC. Ten recurrences (2.6%) and five T1 CRC-associated deaths (1.3%) occurred in the high-risk group, with none in the low-risk group. Five-year cumulative overall and local recurrence rates were significantly higher in the follow-up versus surgery group (7.7% and 1.9%; P=0.019 versus 6.4% and 0%; P<0.001), with no local recurrence in the surgery group. Five-year overall survival was significantly worse in the follow-up group (79.5% and 95.2%; P<0.001), while no significant difference was found in 5-y disease-specific survival (98.5% and 98.5%; P=0.447).

Conclusion

No recurrence or mortality occurred in the low-risk group, suggesting that intensive surveillance may be unnecessary for these patients after curative ESD. Additional surgery could prevent local recurrence for high-risk T1 CRC, supporting a risk-based treatment approach.