Early histological findings may predict the clinical phenotype in Crohn's colitis.

Background And Aims: Predicting the clinical course of Crohn's disease (CD) is relevant for treatment selection. Currently, such diagnostic tools are lacking. In a previous pilot study, morphometric tissue image analysis showed promise in predicting the clinical phenotype and need for surgery. In this study, we aimed to validate our previous results on a larger cohort.

Methods: Colonic biopsies from CD patients with colonic or ileocolonic disease and at least five years of post-biopsy clinical follow-up were analyzed. The results were used to predict post-biopsy clinical phenotypes and outcomes. Data analysis was performed using multivariate regression models, discriminant score (DS) computations and Neural Network (NNET).

Results: Multivariate analysis of morphometric variables differentiated between B1 and B2 phenotypes (sensitivity 81%, specificity 74%, accuracy on cross-validation 75%; area under the curve (AUC) of 0.74 (CI 0.6-0.84; NNET model sensitivity 87%, specificity 67% on the testing population)). Differentiation between B1 and B3 phenotypes was also possible (sensitivity 69%, specificity 76%, accuracy 70.5% on cross-validation; AUC 0.78 (CI 0.68-0.89); NNET model sensitivity 78%, specificity 77% on the testing population)). Differentiating between B2 and B3 phenotypes was not possible using morphometric variables. Multivariate analysis predicted surgery (sensitivity 67%, specificity 72.5%, accuracy 69%; AUC 0.72 (CI 0.61-0.82); NNET model sensitivity 80%, specificity 91% on the testing population)).

Conclusions: This study validates previous results and suggests that morphometric image analysis of early biopsies from Crohn's colitis patients may contribute to the prediction of future outcomes such as clinical phenotype and surgery. Prospective validation on larger cohorts is still needed.