APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer.
1/5 보강
Identifying predictive and resistance biomarkers remains one of the most relevant unmet needs in clinical cancer research.
APA
Prelaj A, Provenzano L, et al. (2026). APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer.. NPJ precision oncology, 10(1). https://doi.org/10.1038/s41698-026-01295-3
MLA
Prelaj A, et al.. "APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer.." NPJ precision oncology, vol. 10, no. 1, 2026.
PMID
41611893 ↗
Abstract 한글 요약
Identifying predictive and resistance biomarkers remains one of the most relevant unmet needs in clinical cancer research. Artificial Intelligence (AI) represents a powerful tool to develop predictive algorithms tailored to individual patients. Thanks to its ability to process large quantities of heterogeneous, patient-level information, the AI-based approach is progressively fostering the growth of a data-driven paradigm to complement traditional, hypothesis-driven clinical research. However, the development of reliable AI models requires access to large, high-quality, and continuously updated datasets. Despite this necessity, no infrastructure currently exists to enable federated, multi-omic, standardized, prospective, and large-scale collection and analysis of real-world clinical and biological data in the context of lung cancer. We established the APOLLO11 consortium, a distributed, nationwide, updated Italian lung cancer network designed to build a decentralized, long-term, population-based, real-world data repository and a multilevel biobank, locally stored and centrally annotated. This strategy seeks to lay the foundation for the clinical implementation of data-driven research, ultimately advancing precision oncology.