TY  - JOUR
AU  - Bitto, Verena
AU  - Jiang, Xiaofeng
AU  - Baumann, Michael
AU  - Kather, Jakob Nikolas
AU  - Kurth, Ina
TI  - Deep Learning Predicts Survival Across Squamous Tumor Entities From Routine Pathology: Insights from Head and Neck, Esophagus, Lung and Cervical Cancer.
JO  - Modern pathology
VL  - 38
IS  - 12
SN  - 0893-3952
CY  - London
PB  - Nature Publishing Group
M1  - DKFZ-2025-01454
SP  - 100845
PY  - 2025
N1  - #EA:E220#LA:E220# / Volume 38, Issue 12, December 2025, 100845
AB  - 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">
AB  - 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
KW  - computational pathology (Other)
KW  - feature extraction (Other)
KW  - histopathology (Other)
KW  - squamous cell carcinomas (Other)
KW  - survival models (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40680853
DO  - DOI:10.1016/j.modpat.2025.100845
UR  - https://inrepo02.dkfz.de/record/303007
ER  -