Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study
Although deep learning models trained on larger cohorts show improved robustness and generalizability in predicting oncogenic mutations, they cannot replace comprehensive molecular profiling. However, they may support patient pre-selection for clinical trials and deepen the insight in genotype-phenotype relationships.
Authors
- Maximilian Alber
- Lukas Ruff (ORCID: https://orcid.org/0000-0002-9707-297X)
- Cleo‐Aron Weis (ORCID: https://orcid.org/0000-0002-1831-3406)
- Petros Christopoulos (ORCID: https://orcid.org/0000-0002-7966-8980)
- Eva Romanovsky (ORCID: https://orcid.org/0000-0002-0059-1977)
- Simon Schallenberg (ORCID: https://orcid.org/0000-0002-7897-7116)
- Frederick Klauschen (ORCID: https://orcid.org/0000-0002-9131-2389)
- Marc A. Schneider (ORCID: https://orcid.org/0000-0001-8269-3821)
- Gabriel Dernbach
- Mike Thomas (ORCID: https://orcid.org/0000-0001-5939-1155)
- Peter Schirmacher (ORCID: https://orcid.org/0000-0002-0950-3339)
- Thomas Muley (ORCID: https://orcid.org/0000-0002-8141-0604)
- Jan Budczies (ORCID: https://orcid.org/0000-0002-6668-5327)
- Daniel Kazdal
- Albrecht Stenzinger
- Klaus‐Robert Müller
Institutions
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- European Journal of Cancer
- Published
- 2024-08-23
- DOI
- https://doi.org/10.1016/j.ejca.2024.114292
- Citations
- 11
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.45
Funders
- Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie