DEEP LEARNING TISSUE ANALYSIS DIAGNOSES AND PREDICTS TREATMENT RESPONSE IN EOSINOPHILIC ESOPHAGITIS

ABSTRACT Background Eosinophilic Esophagitis (EoE) is a chronic inflammatory condition diagnosed by ≥15 eosinophils (Eos) per high-power field (HPF). There is no gold standard for clinical remission and Eo-associated metrics are poorly correlated with symptoms. Deep learning can be used to explore the relationships of tissue features with clinical response. Objectives To determine if deep learning can elucidate tissue patterns in EoE that predict treatments or symptoms at remission. Methods We created two deep learning models using esophageal biopsies from histologically normal and EoE patients: one to identify Eos in esophageal biopsies and a second to broadly classify esophageal tissue as EoE vs. normal. We used these models to analyze biopsies at diagnosis and first remission timepoint, as defined by <15 Eos/HPF, in a subset of 19 treatment-naïve patients. Differences in deep learning metrics between patient groups were assessed using Wilcoxon Rank-Sum tests. Results All initial patients were symptomatic at diagnosis and a majority were still suffering from dysphagia at remission. The Eo identification model had a low mean (SD) error of −0.3 (11.5) Eos/HPF. Higher peak and average Eo counts at diagnosis were associated with higher likelihood of being on a food-elimination diet at remission than steroids or proton-pump inhibitor (p<0.05). The EoE classification model had an F1-score of 0.97 for distinguishing normal tissue from EoE. There was a significant decrease from diagnosis in the percentage of EoE-classified tissue among asymptomatic remission patients (p<0.05). Conclusions Deep learning may have utility in diagnosing EoE and predicting future treatment response at diagnosis and resolution of symptoms at follow-up. Clinical Implications or Key Messages (for mechanistic article) We developed two deep learning approaches for tissue analysis in eosinophilic esophagitis, which may improve histologic assessment of patients at diagnosis and predict treatment response and symptoms at remission. Capsule summary Two deep learning approaches for eosinophilic esophagitis (EoE): (1) Quantification of eosinophils throughout an entire biopsy, which predicted treatment at remission (2) Classifying esophageal tissue as EoE or normal, which predicted symptoms at remission.