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Title:

Histology-Based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning.

Document type:
Journal Article; Research Support, Non-U.S. Gov't
Author(s):
Hörst, Fabian; Ting, Saskia; Liffers, Sven-Thorsten; Pomykala, Kelsey L; Steiger, Katja; Albertsmeier, Markus; Angele, Martin K; Lorenzen, Sylvie; Quante, Michael; Weichert, Wilko; Egger, Jan; Siveke, Jens T; Kleesiek, Jens
Abstract:
PURPOSE: 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 bas...     »
Journal title abbreviation:
JCO Clin Cancer Inform
Year:
2023
Journal volume:
7
Fulltext / DOI:
doi:10.1200/CCI.23.00038
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/37527475
TUM Institution:
591; Institut für Allgemeine Pathologie und Pathologische Anatomie (Dr. Mogler komm.)
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