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Data harmonization is one of the greatest challenges in cancer imaging studies, especially when it comes to multi-source data provision. Properly integrated data deriving from various sources can ensure data fairness on one side and can lead to a trusted dataset that will enhance AI engine development on the other side. Towards this direction, we are presenting a data integration quality check tool that ensures that all data uploaded to the repository are homogenized and share the same principles. The tool&#x0027;s aim is to report any human-induced errors and propose corrective actions. It focuses on checking the data prior to their upload to the repository in five levels: (i) clinical metadata integrity, (ii) template-imaging consistency, (iii) anonymization protocol applied, (iv) imaging analysis requirements, (v) case completeness. The tool produces reports with the corrective actions that must be followed by the user. This way the tool ensures that the data that will become available to the developers of the AI engine are homogenized, properly structured and contain all the necessary information needed for the analysis. The tool was validated in two rounds, internal and external, and at the user experience level. Clinical Relevance&#x2014; Supporting the harmonized preparation and provision of medical imaging data and related clinical data will ensure data fairness and enhance the AI development

Data Quality Check in Cancer Imaging Research: Deploying and Evaluating the DIQCT Tool