Integrating Biological and Radiological Data in a Structured Repository: a Data Model Applied to the COSMOS Case...

Area of Science:

• Bioinformatics
• Medical Informatics
• Data Science

Background:

• Precision medicine increasingly integrates biological samples and digital data (e.g., medical images).
• Managing large, diverse datasets requires standardized structures to facilitate institutional cooperation.
• Existing standards like MIABIS (Minimum Information About BIobank data Sharing) provide a foundation for data standardization.

Purpose of the Study:

• To propose an extended data model for standardizing collections of both biological and digital samples.
• To emphasize cause-effect relationships within the data model, crucial for clinical workflows.
• To present the Continuous Observation of SMOking Subjects (COSMOS) database structure as a case study for this model.

Main Methods:

• Development of an extended data model inspired by MIABIS.
• Incorporation of cause-effect relationships relevant to clinical data.
• Application and testing of the data model using the COSMOS dataset (lung cancer screening).

Main Results:

• A proposed data model designed for integrated biological and digital sample information.
• Demonstration of the model's applicability using the COSMOS dataset, a 10-year lung cancer screening cohort.
• Presentation of the COSMOS database structure optimized for data retrieval.

Conclusions:

• The proposed data model offers a standardized approach for managing complex biomedical data.
• Standardization facilitates data sharing and cooperation in precision medicine research.
• The COSMOS dataset structure provides a practical example for implementing such data models in lung cancer screening research.