Towards Data Integration for AI in Cancer Research

Kosvyra, Alexandra; Filos, Dimitrios; Fotopoulos, Dimitrios; Tsave, Olga; Chouvarda, Ioanna

doi:10.1109/embc46164.2021.9629675

Cited by 6 publications

(7 citation statements)

References 8 publications

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“…213 The integration of AI in cancer research is not only transformative but also necessitates a concerted effort to address inherent biases and technical challenges. 214 As AI continues to evolve, its applications in cancer research, from pathology to drug discovery, promise to significantly enhance our understanding and treatment of cancer. The future of cancer care will likely be shaped by these advanced technologies, provided they are developed and applied with a focus on equity and inclusivity.…”

Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Khorsandi,

Rezayat,

Sezen

et al. 2024

J. Mater. Chem. B

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Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Khorsandi,

Rezayat,

Sezen

et al. 2024

J. Mater. Chem. B

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“…Eventually, the protocol and data collection procedure for a harmonised data storage was defined. After the data collection and the images deidentification step, which is implemented via the CTP DICOM Anonymizer [68] and a configuration following the DICOM PS3.15 [69] standard, and before data uploading to the repository, a quality check takes place at the local level using Data Integration Quality Check Tool [70], a rule-based engine, implementing domain knowledge, and aims to identify whether data follow the data harmonisation requirements defined within the project, as well as the integrity and consistency of the data.…”

Section: Chaimeleon: Accelerating the Lab To Market Transition Of Ai ...mentioning

confidence: 99%

Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks

et al. 2022

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A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 “AI for Health Imaging” projects, which are all dedicated to the creation of imaging biobanks.

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“…All data providers were given instructions on the steps to follow to make data collection as uniform as possible. In addition, a data quality check was also performed on a local level to detect possible problems and support the data provider in correcting them before uploading data to the INCISIVE repository [27]. These included checks of the structured data (for structural and semantic compliance with the template, the existence of duplicates, and the absence of mandatory values), as well as checks of the imaging data hierarchy and Digital Imaging and Communications in Medicine (DICOM) files.…”

Section: Data Integration and Quality Check Toolmentioning

confidence: 99%

“…INCISIVE imaging datasets are in the DICOM format, which is a common format that facilitates interoperability between medical imaging devices [27,30]. The DICOM format contains meta-data that often possess identifiable information on the patient, study, institution, etc.…”

Section: De-identificationmentioning

confidence: 99%

The Holistic Perspective of the INCISIVE Project—Artificial Intelligence in Screening Mammography

et al. 2022

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Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions.

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Towards Data Integration for AI in Cancer Research

Cited by 6 publications

References 8 publications

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks

The Holistic Perspective of the INCISIVE Project—Artificial Intelligence in Screening Mammography

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