%0 Journal Article
%A Hörst, Fabian
%A Ting, Saskia
%A Liffers, Sven-Thorsten
%A Pomykala, Kelsey L
%A Steiger, Katja
%A Albertsmeier, Markus
%A Angele, Martin K
%A Lorenzen, Sylvie
%A Quante, Michael
%A Weichert, Wilko
%A Egger, Jan
%A Siveke, Jens
%A Kleesiek, Jens
%T Histology-Based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning.
%J JCO clinical cancer informatics
%V 7
%N 7
%@ 2473-4276
%C Alexandria, VA
%I American Society of Clinical Oncology
%M DKFZ-2023-01560
%P e2300038
%D 2023
%X Quantifying treatment response to gastroesophageal junction (GEJ) adenocarcinomas is crucial to provide an optimal therapeutic strategy. Routinely taken tissue samples provide an opportunity to enhance existing positron emission tomography-computed tomography (PET/CT)-based therapy response evaluation. Our objective was to investigate if deep learning (DL) algorithms are capable of predicting the therapy response of patients with GEJ adenocarcinoma to neoadjuvant chemotherapy on the basis of histologic tissue samples.This diagnostic study recruited 67 patients with I-III GEJ adenocarcinoma from the multicentric nonrandomized MEMORI trial including three German university hospitals TUM (University Hospital Rechts der Isar, Munich), LMU (Hospital of the Ludwig-Maximilians-University, Munich), and UME (University Hospital Essen, Essen). All patients underwent baseline PET/CT scans and esophageal biopsy before and 14-21 days after treatment initiation. Treatment response was defined as a ≥35
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:37527475
%R 10.1200/CCI.23.00038
%U https://inrepo02.dkfz.de/record/277900