Journal: NPJ precision oncology
This publication describes the creation of the APOLLO11 consortium, a nationwide Italian network focused on lung cancer, designed to support AI-driven precision oncology.
Key points:
- Clinical need: There is a major unmet need for predictive and resistance biomarkers in lung cancer. AI has the potential to generate patient-specific predictive models, but only if supported by large, high-quality, and continuously updated datasets.
- Problem: Existing infrastructures are inadequate for federated, multi-omic, standardized, prospective, large-scale collection and analysis of real-world clinical and biological data in lung cancer.
- APOLLO11 solution:
- • A distributed, population-based lung cancer network across Italy.
- • Creation of a decentralized, long-term real-world data repository.
- • Establishment of a multilevel biobank, with samples stored locally but annotated centrally.
- • Emphasis on federated data management to allow analysis while maintaining local data control and promoting standardization.
- Goal: To provide the structural backbone needed for robust AI model development and implementation in routine practice, supporting data-driven research and advancing precision oncology for lung cancer patients.