본문으로 건너뛰기
← 뒤로

APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer.

1/5 보강
NPJ precision oncology 📖 저널 OA 90.7% 2023: 1/1 OA 2024: 6/6 OA 2025: 82/82 OA 2026: 76/93 OA 2023~2026 2026 Vol.10(1)
Retraction 확인
출처

Prelaj A, Provenzano L, Miskovic V, Ganzinelli M, Mazzeo L, Gemelli M, Silvestri C, Spagnoletti A, Romanò R, Brambilla M, Occhipinti M, Beninato T, Ambrosini P, Sottotetti E, Favali M, Zec A, Ferrarin A, Corrao G, Prina MM, Ruggirello M, Marino MB, Dumitrascu AD, Di Mauro RM, Giani C, Cavalli C, Serino R, Catania C, Panzardi A, Metro G, Bennati C, Ferrara R, Macerelli M, Servetto A, Cona MS, La Verde N, Toschi L, Baili P, Corso F, Zito E, Cinieri S, Berardi R, Scoazec G, Inno A, Gori S, Pisconti S, Buzzacchino F, Brighenti M, Biello F, Tartarone A, Pruneri G, Belfiore A, Agnelli L, Guidi A, Invernizzi L, Salmistraro N, Filippi AR, Solli P, Galli G, Lorenzini D, Pizzutilo EG, De Braud F, Pedrocchi A, Trovò F, Genova C, Corte CMD, Viscardi G, Garassino MC, Cortellini A, Mingo E, Russano M, Signorelli D, Proto C, Vingiani A, Sangaletti S, Lo Russo G

📝 환자 설명용 한 줄

Identifying predictive and resistance biomarkers remains one of the most relevant unmet needs in clinical cancer research.

이 논문을 인용하기

↓ .bib ↓ .ris
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

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.
🟢 PMC 전문 열기