BACKGROUND/AIMS: The existing histological classifications for the interpretation of small intestinal biopsies are based on qualitative parameters with high intraobserver and interobserver variations. We have developed and propose a quantitative histological classification system for the assessment of intestinal mucosal biopsies. METHODS: We performed a computer-assisted quantitative histological assessment of digital images of duodenal biopsies from 137 controls and 124 patients with celiac disease (CeD) (derivation cohort). From the receiver-operating curve analysis, followed by multivariate and logistic regression analyses, we identified parameters for differentiating control biopsies from those of the patients with CeD. We repeated the quantitative histological analysis in a validation cohort (105 controls and 120 patients with CeD). On the basis of the results, we propose a quantitative histological classification system. The new classification was compared with the existing histological classifications for interobserver and intraobserver agreements by a group of qualified pathologists. RESULTS: Among the histological parameters, intraepithelial lymphocyte count of ≥25/100 epithelial cells, adjusted villous height fold change of ≤0.7, and crypt depth-to-villous height ratio of ≥0.5 showed good discriminative power between the mucosal biopsies from the patients with CeD and those from the controls, with 90.3% sensitivity, 93.5% specificity, and 96.2% area under the curve. Among the existing histological classifications, our quantitative histological classification showed the highest intraobserver (69.7%–85.03%) and interobserver (24.6%–71.5%) agreements. CONCLUSIONS: Quantitative assessment increases the reliability of the histological assessment of mucosal biopsies in patients with CeD. Such a classification system may be used for clinical trials in patients with CeD.
Biopsy ; Celiac Disease ; Classification ; Cohort Studies ; Epithelial Cells ; Humans ; Intestine, Small ; Logistic Models ; Lymphocyte Count ; Observer Variation ; Sensitivity and Specificity