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Purpose <p>The study of brain and central nervous system (CNS) tumors has generated and relied upon abundant and diverse data. Robust and comprehensive real-world datasets (RWD) are crucial for understanding risk factors, prognostic factors and clinical outcomes of those diagnosed with these tumors. RWD contains clinically-rich data collected in the context of routine delivery of care and includes an array of variables derived from electronic health records. These data can be leveraged to gain further insight into disease states, treatments and outcomes.</p> Methods/Results <p>Here, we provide a brief overview of considerations when picking a dataset to utilize, and we describe various sources of neuro-oncology data including cancer registries, administrative claims, and commercial RWD.</p> Conclusions <p>Future work will undoubtably include the integration of artificial intelligence and machine learning for the analysis of RWD and include linkage to imaging and unstructured clinical data. Unfortunately most RWD sources lack histopathology characteristics which emphasizes the need for linkage data elements that will allow for accurate tumor analyses.</p>

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Using data for novel discoveries in neuro-oncology research

  • 期刊论文

  • 发表日期：

    2025年10月20日

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  • Mackenzie Price,
  • Christine Ann Pittman Ballard,
  • Kristin A. Waite,
  • Quinn T. Ostrom,
  • Jill S. Barnhnoltz-Sloan

摘要

Purpose

The study of brain and central nervous system (CNS) tumors has generated and relied upon abundant and diverse data. Robust and comprehensive real-world datasets (RWD) are crucial for understanding risk factors, prognostic factors and clinical outcomes of those diagnosed with these tumors. RWD contains clinically-rich data collected in the context of routine delivery of care and includes an array of variables derived from electronic health records. These data can be leveraged to gain further insight into disease states, treatments and outcomes.

Methods/Results

Here, we provide a brief overview of considerations when picking a dataset to utilize, and we describe various sources of neuro-oncology data including cancer registries, administrative claims, and commercial RWD.

Conclusions

Future work will undoubtably include the integration of artificial intelligence and machine learning for the analysis of RWD and include linkage to imaging and unstructured clinical data. Unfortunately most RWD sources lack histopathology characteristics which emphasizes the need for linkage data elements that will allow for accurate tumor analyses.