The diagnostic imaging field is experiencing considerable growth, followed by increasing production of massive amounts of data. The lack of standardization and privacy concerns are considered the main barriers to big data capitalization. This work aims to verify whether the advanced features of the DICOM standard, beyond imaging data storage, are effectively used in research practice. This issue will be analyzed by investigating the publicly shared medical imaging databases and assessing how much the most common medical imaging software tools support DICOM in all its potential. Therefore, 100 public databases and ten medical imaging software tools were selected and examined using a systematic approach. In particular, the DICOM fields related to privacy, segmentation and reporting have been assessed in the selected database; software tools have been evaluated for reading and writing the same DICOM fields. From our analysis, less than a third of the databases examined use the DICOM format to record meaningful information to manage the images. Regarding software, the vast majority does not allow the management, reading and writing of some or all the DICOM fields. Surprisingly, if we observe chest computed tomography data sharing to address the COVID-19 emergency, there are only two datasets out of 12 released in DICOM format. Our work shows how the DICOM can potentially fully support big data management; however, further efforts are still needed from the scientific and technological community to promote the use of the existing standard, encouraging data sharing and interoperability for a concrete development of big data analytics.

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BCU Imaging Biobank (BCU-IB) is a non-profit biorepository aimed at the collection, storage and retrieval of diagnostic images, derived descriptors and clinical data. The main scope of BCU-IB is to foster scientific advances in imaging and analysis, opening up new ways for biomedical research to diagnose, treat and potentially prevent diseases.BCU-IB collects a vast amount of images of the human body, including healthy and pathological subjects. Diagnostic images, clinical, anamnestic and demographic data are made available to study the associations between imaging phenotypes, diagnostic and prognostic factors. Curated datasets are stored and organized in a secure and reliable dedicated information systems based on the Extensible Neuroimaging Archive Toolkit (XNAT), hosted by Bio Check Up Srl.

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