PICaSSO Histologic Remission Index (PHRI) in ulcerative colitis: development of a novel simplified histological score for monitoring mucosal healing and predicting clinical outcomes and its applicability in an artificial intelligence system.
Xianyong Gui, Alina Bazarova, Rocìo Del Amor, Michael Vieth, Gert de Hertogh, Vincenzo Villanacci, Davide Zardo, Tommaso Lorenzo Parigi, Elin Synnøve Røyset, Uday N Shivaji, Melissa Anna Teresa Monica, Giulio Mandelli, Pradeep Bhandari, Silvio Danese, Jose G Ferraz, Bu'Hussain Hayee, Mark Lazarev, Adolfo Parra-Blanco, Luca Pastorelli, Remo Panaccione, Timo Rath, Gian Eugenio Tontini, Ralf Kiesslich, Raf Bisschops, Enrico Grisan, Valery Naranjo, Subrata Ghosh, Marietta Iacucci
Published in May 2022, Gastroenterology
Histological remission is evolving as an important treatment target in UC. We aimed to develop a simple histological index, aligned to endoscopy, correlated with clinical outcomes, and suited to apply to an artificial intelligence (AI) system to evaluate inflammatory activity.
Using a set of 614 biopsies from 307 patients with UC enrolled into a prospective multicentre study, we developed the Paddington International virtual ChromoendoScopy ScOre (PICaSSO) Histologic Remission Index (PHRI). Agreement with multiple other histological indices and validation for inter-reader reproducibility were assessed. Finally, to implement PHRI into a computer aided diagnosis system, we trained and tested a novel deep learning strategy based on a CNN architecture to detect neutrophils, calculate PHRI and identify active from quiescent UC using a subset of 138 biopsies.
PHRI is strongly correlated with endoscopic scores (Mayo Endoscopic Score and UC Endoscopic Index of Severity and PICaSSO) and with clinical outcomes (hospitalisation, colectomy and initiation or changes in medical therapy due to UC flare-up). A PHRI score of 1 could accurately stratify patients’ risk of adverse outcomes (hospitalisation, colectomy and treatment optimisation due to flare-up) within 12 months. Our inter-reader agreement was high (intraclass correlation 0.84). Our preliminary AI algorithm differentiated active from quiescent UC with 78% sensitivity, 91.7% specificity and 86% accuracy.
PHRI is a simple histological index in UC, and it exhibits the highest correlation with endoscopic activity and clinical outcomes. A PHRIbased AI system was accurate in predicting histological remission.