%0 Journal Article
%A Bitto, Verena
%A Jiang, Xiaofeng
%A Baumann, Michael
%A Kather, Jakob Nikolas
%A Kurth, Ina
%T Deep Learning Predicts Survival Across Squamous Tumor Entities From Routine Pathology: Insights from Head and Neck, Esophagus, Lung and Cervical Cancer.
%J Modern pathology
%V 38
%N 12
%@ 0893-3952
%C London
%I Nature Publishing Group
%M DKFZ-2025-01454
%P 100845
%D 2025
%Z #EA:E220#LA:E220# / Volume 38, Issue 12, December 2025, 100845
%X Computational pathology-based models are becoming increasingly popular for extracting biomarkers from images of cancer tissue. However, their validity is often only demonstrated on a single unseen validation cohort, limiting insights into their generalizability and posing challenges for explainability. In this study, we developed models to predict overall survival using haematoxylin and eosin (H</td><td width="150">
%X E) slides from formalin-fixed paraffin-embedded (FFPE) samples in head and neck squamous cell carcinoma (HNSCC). By validating our models across diverse squamous tumor entities, including head and neck (hazard ratio [HR] = 1.58, 95
%K computational pathology (Other)
%K feature extraction (Other)
%K histopathology (Other)
%K squamous cell carcinomas (Other)
%K survival models (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:40680853
%R 10.1016/j.modpat.2025.100845
%U https://inrepo02.dkfz.de/record/303007