Barrett's esophagus, Gastro-Esophageal carcinoma, Artificial Intelligence,  Endoscopic Resection Therapy, Immunotherapy

My current research aims to improve the clinical care of patients with Barrett’s Esophagus (BE) and  esophageal cancer. To achieve this we integrate expert histopathology images assessment with Artificial Intelligence (AI) for objective diagnosis and patient tailored risk stratification.

Nationwide Barrett’s expert histopathology review: We coordinate a nationwide expert histopathology revision platform (LANS) for BE-patients. So far, panel consensus assessment serves as basis for treatment of >850 BE patients, and results of studies have implications for clinical guidelines on diagnosis and treatment of BE.

AI-based detection and progression prediction of Barrett’s Esophagus: our computational pathology group together with AMLab & qurAI from the Informatics Institute (UvA) aims to I) develop an automated AI-based expert-histopathology classifier diagnosing BE biopsies based on the unique LANS database, and II) to identify BE lesions that progress to EAC, using AI with integrated clinical-pathology analysis, automated microenvironment analysis and time series approach.

Use AI-based histopathology image analysis to identify esophago-gastric cancer patients that benefit from immunotherapy:  with Geometric and Causal Deep Learning methods we will quantify spatial distribution of multiple predictive tissue biomarkers and the tumor-immune microenvironment directly on standard pathology images that relate to clinical outcome of immunotherapy and identify features indicative of successful response.