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

Abstract

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.

Methods: 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.

Results: 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.

Conclusions: PHRI is a simple histological index in UC, and it exhibits the highest correlation with endoscopic activity and clinical outcomes. A PHRI-based AI system was accurate in predicting histological remission.

Keywords: computerised image analysis; histopathology; inflammatory bowel disease; ulcerative colitis.

Figure 1

Framework of the proposed deep learning approach. The framework is composed of two models with different but related tasks. The first model predicts patches with neutrophils using a pretrained architecture in histological images. The second model uses the feature extractor and the feature refinement used by the first model to the prediction of UC at the patient level. GAP, global average pooling; SE, squeeze and excitation (feature refinement).

Figure 2

PICaSSO Histologic Remission Index (PHRI) correlation. Histological-endoscopic correlation demonstrated by heatmaps showing Spearman’s correlation coefficients between different histological and endoscopic scores in the rectum (A) and in the sigmoid (B) and between the endoscopic-histological scores and the specified clinical outcomes at 12 months (0.8–1.0: very strong correlation, 0.6–0.79: strong, 0.40–0.59: moderate, 0.2–0.39: weak) (*p<0.05 as compared with PHRI with regard to the correlation strength in the same category of correlation analysis). ECAP, extent, chronicity, activity and plus; PHRI, PICaSSO Histologic Remission Index; PICaSSO, Paddington International virtual ChromoendoScopy ScOre; RHI, Robarts Histological Index; UCEIS, UC Endoscopic Index of Severity.

Figure 3

Correlation of histopathological components. Heatmaps showing the correlations of histopathological components with PICaSSO endoscopic subscores and with the rates of 12-month adverse outcomes in all patients. Biopsies from the rectum (A) and the sigmoid (B) (0.8–1.0: very strong correlation, 0.6–0.79: strong, 0.40–0.59: moderate, 0.2–0.39: weak) (*p<0.05 as compared with neutrophils in the lamina propria, !p<0.05 as compared with and total neutrophils infiltration and #p<0.05 as compared with neutrophils in epithelium, with regard to the correlation strength in the same category of correlation analysis) (neutrophils total: neutrophil infiltration in both lamina propria and epithelium). PICaSSO, Paddington International virtual ChromoendoScopy ScOre.

Figure 4

Receiver operating characteristic (ROC) curve and Paddington International virtual ChromoendoScopy ScOre Histogic Remission Index (PHRI) thresholds to predict specified clinical outcomes and histological remission (HR). AUROC, area under the receiver operating characteristic curve; CI, chronic inflammation; Neu-LP, neutrophil infiltration in lamina propria; Neu-Epi, neutrophil infiltration in epithelium; PHRI_rec, PHRI scores of rectum; PHRI_sig, PHRI scores of sigmoid.

Figure 5

Cox proportional hazard curves of Paddington International virtual ChromoendoScopy ScOre Histogic Remission Index (PHRI) in stratifying risk of specified clinical outcomes up to 12 months of follow-up. A and B for all patients, C for Mayo Endoscopic Score 0 patients. (A) Using PHRI=0 (blue) vs >0 (red). (B) Using PHRI ≤1 (blue) vs >1 (red). (C) 12 months of follow-up, using PHRI=0 (blue) vs >0 (red).

Figure 6

Original images (first column), annotation of the pathologist (second column) and class activation maps (CAMs) (third column). Note that in this case, the first row corresponds to the lamina propria while the second row corresponds to the surface of the epithelium.