TY  - JOUR
AU  - Klein, Sebastian
AU  - Wuerdemann, Nora
AU  - Demers, Imke
AU  - Kopp, Christopher
AU  - Quantius, Jennifer
AU  - Charpentier, Arthur
AU  - Tolkach, Yuri
AU  - Brinker, Klaus
AU  - Sharma, Shachi Jenny
AU  - George, Julie
AU  - Hess, Jochen
AU  - Stögbauer, Fabian
AU  - Lacko, Martin
AU  - Struijlaart, Marijn
AU  - van den Hout, Mari F C M
AU  - Wagner, Steffen
AU  - Wittekindt, Claus
AU  - Langer, Christine
AU  - Arens, Christoph
AU  - Buettner, Reinhard
AU  - Quaas, Alexander
AU  - Reinhardt, Hans Christian
AU  - Speel, Ernst-Jan
AU  - Klussmann, Jens Peter
TI  - Predicting HPV association using deep learning and regular H</td><td width="150">
TI  - amp;E stains allows granular stratification of oropharyngeal cancer patients.
JO  - npj digital medicine
VL  - 6
IS  - 1
SN  - 2398-6352
CY  - [Basingstoke]
PB  - Macmillan Publishers Limited
M1  - DKFZ-2023-01684
SP  - 152
PY  - 2023
AB  - Human Papilloma Virus (HPV)-associated oropharyngeal squamous cell cancer (OPSCC) represents an OPSCC subgroup with an overall good prognosis with a rising incidence in Western countries. Multiple lines of evidence suggest that HPV-associated tumors are not a homogeneous tumor entity, underlining the need for accurate prognostic biomarkers. In this retrospective, multi-institutional study involving 906 patients from four centers and one database, we developed a deep learning algorithm (OPSCCnet), to analyze standard H</td><td width="150">
AB  - E stains for the calculation of a patient-level score associated with prognosis, comparing it to combined HPV-DNA and p16-status. When comparing OPSCCnet to HPV-status, the algorithm showed a good overall performance with a mean area under the receiver operator curve (AUROC) = 0.83 (95
LB  - PUB:(DE-HGF)16
C6  - pmid:37598255
C2  - pmc:PMC10439941
DO  - DOI:10.1038/s41746-023-00901-z
UR  - https://inrepo02.dkfz.de/record/278640
ER  -