Artificial Intelligence enabled histological prediction of remission or activity and clinical outcomes in ulcerative colitis

Journal article

Iacucci, M., Parigi, T.L., del Amor, R., Meseguer, P., Mandelli, G., Bozzola, A., Bazarova, A., Bhandari, P., Bisschops, R., Danese, S., De Hertogh, G., Ferraz, J.G., Goetz, M., Grisan, E., Gui, X., Hayee, B., Kiesslich, R., Lazarev, M., Panaccione, R., Parra-Blanco, A., Pastorelli, L., Rath, T., Royset, E.S., Tontini, G.E., Vath, M., Zardo, D., Ghosh, S., Naranjo, V. and Villanacci, V. (2023). Artificial Intelligence enabled histological prediction of remission or activity and clinical outcomes in ulcerative colitis. Gastroenterology. https://doi.org/10.1053/j.gastro.2023.02.031
AuthorsIacucci, M., Parigi, T.L., del Amor, R., Meseguer, P., Mandelli, G., Bozzola, A., Bazarova, A., Bhandari, P., Bisschops, R., Danese, S., De Hertogh, G., Ferraz, J.G., Goetz, M., Grisan, E., Gui, X., Hayee, B., Kiesslich, R., Lazarev, M., Panaccione, R., Parra-Blanco, A., Pastorelli, L., Rath, T., Royset, E.S., Tontini, G.E., Vath, M., Zardo, D., Ghosh, S., Naranjo, V. and Villanacci, V.
Abstract

Background
Microscopic inflammation has significant prognostic value in ulcerative colitis (UC); however, its assessment is complex with high interobserver variability. We aimed to develop and validate an artificial intelligence (AI) Computer-Aided Diagnosis System to evaluate UC biopsies and predict prognosis.
Methods
535 digitalized biopsies (273 patients) were graded according to the PICaSSO Histologic Remission Index (PHRI), Robarts’ (RHI), and Nancy Histological Index (NHI). A convolutional neural network classifier was trained to distinguish remission from activity on a subset of 118 biopsies, calibrated on 42 and tested on 375. The model was additionally tested to predict the corresponding endoscopic assessment and occurrence of flares at 12 months. The system output was compared with human assessment. Diagnostic performance was reported as sensitivity, specificity; prognostic prediction through Kaplan-Meier and hazard ratios of flares between active and remission groups. We externally validated the model in 154 biopsies (58 patients) with similar characteristics but more histologically active patients.
Results
The system distinguished histological activity/remission with sensitivity and specificity of 89% and 85% (PHRI), 94% and 76% (RHI), and 89% and 79% (NHI). The model predicted the corresponding endoscopic remission/activity with 79% and 82% accuracy for UCEIS and PICaSSO, respectively. The hazard ratio for disease flare-up between histological activity/remission groups according to pathologist-assessed PHRI was 3.56, and 4.64 for AI-assessed PHRI. Both histology and outcome prediction were confirmed in the external validation cohort.
Conclusion
We developed and validated an AI model that distinguishes histological remission/activity in biopsies of UC and predicts flare-ups. This can expedite, standardize and enhance histological assessment in practice and trials.

KeywordsUlcerative Colitis ; Picasso Histologic Remission Index ; Computer-aided diagnosis ; Convolutional Neural Network ; Robarts Histopathology index
Year2023
JournalGastroenterology
PublisherElsevier
ISSN0016-5085
Digital Object Identifier (DOI)https://doi.org/10.1053/j.gastro.2023.02.031
Publication dates
Print03 Mar 2023
Publication process dates
Accepted15 Feb 2023
Deposited14 Mar 2023
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Growth abnormalities of fetuses and infants
Cosmi, E, Grisan, E, Fanos, V, Rizzo, G, Sivanandam, S and Visentin, S (2017). Growth abnormalities of fetuses and infants. BioMed Research International. 2017. https://doi.org/https://www.doi.org/10.1155/2017/3191308
A possible new approach in the prediction of late gestational hypertension: The role of the fetal aortic intima-media thickness
Visentin, S, Londero, AP, Camerin, M, Grisan, E and Cosmi, E (2017). A possible new approach in the prediction of late gestational hypertension: The role of the fetal aortic intima-media thickness. Medicine (United States). 96. https://doi.org/https://www.doi.org/10.1097/MD.0000000000005515
Automatic classification of small bowel mucosa alterations in celiac disease for confocal laser endomicroscopy
Boschetto, D, Di Claudio, G, Mirzaei, H, Leong, R and Grisan, E (2016). Automatic classification of small bowel mucosa alterations in celiac disease for confocal laser endomicroscopy. Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. San Diego, United States 27 Feb - 03 Mar 2016 SPIE. https://doi.org/10.1117/12.2217183
Automatic classification of endoscopic images for premalignant conditions of the esophagus
Boschetto, D, Gambaretto, G and Grisan, E (2016). Automatic classification of endoscopic images for premalignant conditions of the esophagus. Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. San Diego, United States 27 Feb - 03 Mar 2016 https://doi.org/10.1117/12.2216826
Superpixel-based automatic segmentation of villi in confocal endomicroscopy
Boschetto, D, Mirzaei, H, Leong, RWL and Grisan, E (2016). Superpixel-based automatic segmentation of villi in confocal endomicroscopy. 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). Las Vegas, NV, USA 24 - 27 Feb 2016 pp. 168-171 https://doi.org/10.1109/BHI.2016.7455861
Quantification of kidneys from 3D ultrasound in pediatric hydronephrosis
Cerrolaza, J.J., Grisan, E., Safdar, N., Myers, E., Jago, J., Peters, C.A. and Linguraru, M.G. (2015). Quantification of kidneys from 3D ultrasound in pediatric hydronephrosis. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/embc.2015.7318324