TY - JOUR
T1 - Artificial Intelligence Enabled Histological Prediction of Remission or Activity and Clinical Outcomes in Ulcerative Colitis
AU - Iacucci, Marietta
AU - Parigi, Tommaso Lorenzo
AU - Del Amor, Rocio
AU - Meseguer, Pablo
AU - Mandelli, Giulio
AU - Bozzola, Anna
AU - Bazarova, Alina
AU - Bhandari, Pradeep
AU - Bisschops, Raf
AU - Danese, Silvio
AU - De Hertogh, Gert
AU - Ferraz, Jose G.
AU - Goetz, Martin
AU - Grisan, Enrico
AU - Gui, Xianyong
AU - Hayee, Bu
AU - Kiesslich, Ralf
AU - Lazarev, Mark
AU - Panaccione, Remo
AU - Parra-Blanco, Adolfo
AU - Pastorelli, Luca
AU - Rath, Timo
AU - Røyset, Elin S.
AU - Tontini, Gian Eugenio
AU - Vieth, Michael
AU - Zardo, Davide
AU - Ghosh, Subrata
AU - Naranjo, Valery
AU - Villanacci, Vincenzo
PY - 2023/6
Y1 - 2023/6
N2 - Background & Aims 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 A total of 535 digitalized biopsies (273 patients) were graded according to the PICaSSO Histologic Remission Index (PHRI), Robarts, and Nancy Histological Index. 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% (Robarts Histological Index), and 89% and 79% (Nancy Histological Index). The model predicted the corresponding endoscopic remission/activity with 79% and 82% accuracy for UC endoscopic index of severity and Paddington International virtual ChromoendoScopy ScOre, 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 histologic remission/activity in biopsies of UC and predicts flare-ups. This can expedite, standardize, and enhance histologic assessment in practice and trials.
AB - Background & Aims 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 A total of 535 digitalized biopsies (273 patients) were graded according to the PICaSSO Histologic Remission Index (PHRI), Robarts, and Nancy Histological Index. 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% (Robarts Histological Index), and 89% and 79% (Nancy Histological Index). The model predicted the corresponding endoscopic remission/activity with 79% and 82% accuracy for UC endoscopic index of severity and Paddington International virtual ChromoendoScopy ScOre, 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 histologic remission/activity in biopsies of UC and predicts flare-ups. This can expedite, standardize, and enhance histologic assessment in practice and trials.
U2 - 10.1053/j.gastro.2023.02.031
DO - 10.1053/j.gastro.2023.02.031
M3 - Article
SN - 0016-5085
VL - 164
SP - 1180-1188.e2
JO - Gastroenterology
JF - Gastroenterology
IS - 7
ER -
