X Liu et al. Gastroenterol 2024; 166:921-924. Machine Learning–Based Prediction of Pediatric Ulcerative Colitis Treatment Response Using Diagnostic Histopathology

M Iannucci et al. Gastroenterology 2024: 166: 730-732. Editorial. Open Access! A Baby Step or a Real Giant Stride: Histomic Enabled by Artificial Intelligence to Predict Treatment Response in Pediatric Patients With Ulcerative Colitis

In this article, their machine learning algorithm was trained on 187,571 informative patches from rectal hematoxylin and eosin biopsy samples from 292 treatment-naive pediatric patients with UC in a multicenter inception cohort (PROTECT) ²) study.

Key findings (summarized by editorial):

• The authors first trained the machine learning models on 250 histomic features at the patch level and achieved an area under the receiver operating characteristic curve (AUROC) of 0.87 (95% confidence interval [CI], 0.73–1.00) and an accuracy of 0.90 (95% CI, 0.80–1.00) at the WSI (whole slide images) level in predicting treatment response.
• A subset of 18 histomic features exhibited comparable performance with an AUROC of 0.89 (95% CI, 0.71–0.96) and accuracy of 0.90 (95% CI, 0.80–1.00) to model using the full set of 250 features, indicating the potential for standardized practical application in clinical settings.
• The authors confirmed that the set of 18 histomic features demonstrated comparable performance on the real-world independent SickKids cohort⁷ (University of Toronto) with an AUROC of 0.85 (95% CI, 0.75–0.95) and accuracy of 0.85 (95% CI, 0.75–0.95) at the WSI level.

An important limitation on this study was that the population was 83% white, indicating that it may have less applicability in other cohorts.

My take (borrowed from editorial): Although this study focused solely on the therapeutic outcomes of mesalamine on treatment-naïve patients, it is anticipated that a comparable methodology (based on the fusion of machine learning and digital histopathology) could be applied in subsequent research to elucidate the necessity for colectomy, evaluate responses to biological agents, and optimize drug selection from the current armamentarium. In other words, this approach utilizing routine biopsy specimens may offer a pathway toward a precision personalized approach to managing pediatric-onset UC (and possibly IBD more broadly).