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A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

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A Multi-Dimensional Framework for Data Quality Assurance in Cancer Imaging Repositories.

Olga Tsave¹, Alexandra Kosvyra¹, Dimitrios T Filos¹

¹Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece.

|October 16, 2025

Summary

This summary is machine-generated.

A new data validation framework ensures high-quality, fair, and standardized data for Artificial Intelligence (AI) in cancer research. This approach improves data for AI development, supporting earlier diagnosis and personalized cancer treatments.

Keywords:

cancer imaging clinical metadata data quality data validation harmonization imaging data repository multi-site derived data

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Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Cancer is a leading cause of death globally, necessitating advancements in diagnosis and treatment.
Artificial Intelligence (AI) integration in cancer imaging holds promise for early detection and personalized medicine.
AI model performance relies heavily on the quality, standardization, and fairness of input data.

Purpose of the Study:

To develop a robust framework for pre-validating imaging and clinical data for AI development.
To create a federated, pan-European repository of cancer imaging and clinical data (INCISIVE project).
To ensure data quality, interoperability, and equity in health data repositories.

Main Methods:

A data validation framework assessing clinical (meta)data and imaging data across five dimensions: completeness, validity, consistency, integrity, and fairness.
Procedures included deduplication, annotation verification, DICOM metadata analysis, and anonymization compliance.
Framework applied to data within the INCISIVE project.

Main Results:

Identified critical data quality issues, including missing clinical information and inconsistent formatting.
Detected subgroup imbalances in the dataset.
Demonstrated the benefits of structured data entry and standardized protocols for data quality.

Conclusions:

The structured framework effectively addresses challenges in curating large-scale, multimodal medical data.
The INCISIVE project's approach ensures data quality, interoperability, and equity.
This framework offers a transferable model for future health data repositories supporting AI research in oncology.