TY  - JOUR
AU  - Schallenberg, Simon
AU  - Dernbach, Gabriel
AU  - Ruane, Sharon
AU  - Jurmeister, Philipp
AU  - Böhm, Cornelius
AU  - Standvoss, Kai
AU  - Ghosh, Sandip
AU  - Frentsch, Marco
AU  - Dragomir, Mihnea-Paul
AU  - Keyl, Philipp G
AU  - Friedrich, Corinna
AU  - Na, Il-Kang
AU  - Merkelbach-Bruse, Sabine
AU  - Quaas, Alexander
AU  - Frost, Nikolaj
AU  - Boschung, Kyrill
AU  - Randerath, Winfried
AU  - Schlachtenberger, Georg
AU  - Heldwein, Matthias
AU  - Keilholz, Ulrich
AU  - Hekmat, Khosro
AU  - Rückert, Jens-Carsten
AU  - Büttner, Reinhard
AU  - Vasaturo, Angela
AU  - Horst, David
AU  - Ruff, Lukas
AU  - Alber, Maximilian
AU  - Müller, Klaus-Robert
AU  - Klauschen, Frederick
TI  - AI-powered spatial cell phenomics enhances risk stratification in non-small cell lung cancer.
JO  - Nature Communications
VL  - 16
IS  - 1
SN  - 2041-1723
CY  - [London]
PB  - Springer Nature
M1  - DKFZ-2025-02269
SP  - 9701
PY  - 2025
AB  - Risk stratification remains a critical challenge in non-small cell lung cancer patients for optimal therapy selection. In this study, we develop an artificial intelligence-powered spatial cellomics approach that combines histology, multiplex immunofluorescence imaging and multimodal machine learning to characterize the complex cellular relationships of 43 cell phenotypes in the tumor microenvironment in a real-world retrospective cohort of 1168 non-small cell lung cancer patients from two large German cancer centers. The model identifies cell niches associated with survival and achieves a 14
KW  - Humans
KW  - Carcinoma, Non-Small-Cell Lung: pathology
KW  - Carcinoma, Non-Small-Cell Lung: mortality
KW  - Carcinoma, Non-Small-Cell Lung: diagnostic imaging
KW  - Lung Neoplasms: pathology
KW  - Lung Neoplasms: mortality
KW  - Tumor Microenvironment
KW  - Female
KW  - Male
KW  - Artificial Intelligence
KW  - Retrospective Studies
KW  - Phenomics: methods
KW  - Middle Aged
KW  - Aged
KW  - Risk Assessment: methods
KW  - Machine Learning
KW  - Carcinoma, Squamous Cell: pathology
LB  - PUB:(DE-HGF)16
C6  - pmid:41184299
DO  - DOI:10.1038/s41467-025-65783-z
UR  - https://inrepo02.dkfz.de/record/305621
ER  -