Background/Objectives:Cancer remains a leading global cause of death, with breast, lung, colorectal, and prostate cancers being among the most prevalent. The integration of Artificial Intelligence (AI) into cancer imaging research offers opportunities for earlier diagnosis and personalized treatment. However, the effectiveness of AI models depends critically on the quality, standardization, and fairness of the input data. The EU-funded INCISIVE project aimed to create a federated, pan-European repository of imaging and clinical data for cancer cases, with a key objective to develop a robust framework for pre-validating data prior to its use in AI development.Methods:We propose a data validation framework to assess clinical (meta)data and imaging data across five dimensions: completeness, validity, consistency, integrity, and fairness. The framework includes procedures for deduplication, annotation verification, DICOM metadata analysis, and anonymization compliance.Results:The pre-validation process identified key data quality issues, such as missing clinical information, inconsistent formatting, and subgroup imbalances, while also demonstrating the added value of structured data entry and standardized protocols.Conclusions:This structured framework addresses common challenges in curating large-scale, multimodal medical data. By applying this approach, the INCISIVE project ensures data quality, interoperability, and equity, providing a transferable model for future health data repositories supporting AI research in oncology.
**背景/目的：** 癌症仍是全球主要死亡原因，其中乳腺癌、肺癌、结直肠癌和前列腺癌最为常见。将人工智能（AI）整合到癌症影像研究中，为早期诊断和个体化治疗提供了机遇。然而，AI模型的有效性高度依赖于输入数据的质量、标准化程度及公平性。欧盟资助的INCISIVE项目旨在建立一个针对癌症病例的联邦式、泛欧洲影像与临床数据存储库，其核心目标是为AI开发前的数据预验证构建一个稳健的框架。 **方法：** 我们提出一个数据验证框架，从五个维度评估临床（元）数据和影像数据：完整性、有效性、一致性、完整性及公平性。该框架包括去重、标注验证、DICOM元数据分析及匿名化合规性检查等流程。 **结果：** 预验证过程识别出关键的数据质量问题，如临床信息缺失、格式不一致及亚组不平衡，同时也证明了结构化数据录入和标准化流程的附加价值。 **结论：** 这一结构化框架解决了大规模多模态医学数据整理中的常见挑战。通过应用该方法，INCISIVE项目确保了数据质量、互操作性与公平性，为未来支持肿瘤学AI研究的健康数据存储库提供了可推广的模型。
A Multi-Dimensional Framework for Data Quality Assurance in Cancer Imaging Repositories
肿瘤(癌症)患者之家