Machine Learning-Based Prediction of Pediatric Ulcerative Colitis Treatment Response Using Diagnostic Histopathology.

Background and Aims We previously reported clinical features associated with outcomes in pediatric ulcerative colitis (UC). Here we developed a histopathology model to predict corticosteroid-free remission (CSFR) on mesalamine therapy alone. Methods Pre-treatment rectal biopsy slides were digitized in training and validation groups of 292 and 113 pediatric UC patients, respectively. Whole slide images (WSI) underwent pre-processing. Thirteen machine learning (ML) models were trained using 250 histomic features including texture, color, histogram, and nuclei. Feature importance was determined by the Gini index with the classifier re-trained using the top features. Results 187571 informative patches from 292 training group patients (Male:53%; Age:13y (IQR:11-15); CSFR:41%) were trained on 13 ML classifiers. The best model was random forest (RF). Eighteen optimal histomic features were identified and trained, and the corresponding WSI AUROC was 0.89 (95%CI:0.71, 0.96), accuracy of 90% for CSFR. Features were re-trained on an independent real-world dataset of 113 patients and the model WSI AUROC was 0.85 (95%CI:0.75, 0.95), accuracy of 85%. Conclusion Routine histopathology obtained at diagnosis contains histomic features associated with UC treatment response. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by Crohn's & Colitis Foundations Clinical Research Investigator-Initiated Awards award number 879083 NIH P30 DK078392 of the Digestive Diseases Research Core Center in Cincinnati and PROCTER Scholar Award Cincinnati Childrens Hospital Medical Center ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of Cincinnati Children's Hospital Medical Center waived ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors * UC : Ulcerative colitis CSFR : Corticosteroid-free remission WSI : Whole slide images ML : Machine learning H+E : Hematoxylin and eosin PUCAI : Pediatric ulcerative colitis activity index HSV : Hue saturation value GLCM : Gray level co-occurrence matrix LBP : Local binary pattern MDG : Mean decrease in GINI AUROC : Area under the receiver operating characteristic curve SHAP : Shapley Additive explanation RF : Random forest ET : Extra trees