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Case Studies for Overcoming Challenges in Using Big Data in Cancer.

Shawn M Sweeney1, Hisham K Hamadeh2, Natalie Abrams3

  • 1American Association for Cancer Research, Philadelphia, Pennsylvania.

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|January 10, 2023
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Summary
This summary is machine-generated.

Leveraging big healthcare data accelerates oncology research and precision medicine. Successful data sharing requires robust systems for aggregation, standardization, and integration, overcoming current accessibility challenges.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Health Informatics

Background:

  • Big healthcare data offers significant potential for advancing cancer research and precision medicine.
  • Challenges exist in organizing, sharing, and integrating large datasets for research accessibility.
  • Effective data management is crucial for unlocking insights in oncology drug development and patient care.

Approach:

  • This review examines five successful initiatives addressing big healthcare data challenges in oncology.
  • Case studies include CancerLinQ, AACR Project GENIE, Project Data Sphere, NCI Genomic Data Commons, and VHA Clinical Data Initiative.
  • The review highlights critical success factors for developing data-sharing systems.

Key Points:

  • Robust data aggregation pipelines and common data models are essential.
  • Data deidentification, workflow integration, and terminology standardization facilitate data utility.
  • Interoperability, quality control, and multi-type data incorporation are vital for comprehensive analysis.
  • Understanding optimal application of data resources is key to maximizing impact.

Conclusions:

  • Emerging data resources demonstrate successful strategies for managing big healthcare data in oncology.
  • Early consideration of secondary data use and proper sharing practices are encouraged.
  • These efforts aim to generate insights that advance cancer understanding and treatment through data sharing.