Data-centric artificial intelligence and cancer research: construction of a real-world head and neck treatment data repository.

ABSTRACT:

Background and purpose

The performance and generalisability of machine learning (ML) models relies on high-quality data. Retrospective and prospective collection of high-quality data for research use while respecting data protection and patient privacy remains a challenge in the clinical environment. Currently, months of laborious extraction and clinical annotation are often necessary before data analysis can begin. We present a novel institutional federated data lake, utilising open-source software, to facilitate efficient production of ML models from head and neck cancer (HNC) imaging and radiotherapy (RT) data. This structured pipeline dramatically reduces the time associated with the production of ML models and real-world evidence generation. This paper describes our governance

SUBMITTER: Butterworth V 

PROVIDER: S-EPMC12836594 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature