Watch the New Childhood Cancer Data Initiative Vision Video
The NCI’s Childhood Cancer Data Initiative (CCDI) has released a new video about the initiative’s vision of building a community around childhood cancer and research data. Featured in the video are advocates such as Amanda Haddock, president and co-founder of Dragon Master Foundation, and pediatric cancer researchers like Dr. Ned Sharpless, NCI Director, and Dr. Sam Volchenboum, Director of the Pediatric Cancer Data Commons. This video shares the importance of CCDI and its aspirations to improve outcomes for treating children and adolescents and young adults (AYAs) with cancer.
CCDI’s goal is to maximize every opportunity to improve treatments and outcomes for children and AYAs with cancer. To increase data use and sharing in pursuit of progress against childhood and AYA cancers, the CCDI aims to:
- Establish a connected data infrastructure of repositories, registries, and tools to enable sharing of childhood and AYA cancer data from multiple sources (e.g., molecular, preclinical, biological, imaging, clinical trial data)
- Create meaningful data sets that have the proper information about each patient to generate novel discoveries as well as comprehensively address key questions in childhood and AYA cancers
- Identify opportunities to make data work better for patients, clinicians, and researchers
- Develop and enhance tools and methods to extract knowledge from data so different types of questions can be adequately addressed
The CCDI is a federal investment of $50 million that was initiated in December 2019 and proposed to be extended in equal amounts per year for the next 9 years. These funds will allow NCI to enhance data sharing, collection, analysis, and access for ongoing and planned childhood and AYA cancer and survivorship research throughout the Institute. NCI Office of Data Sharing Director Dr. Jaime Guidry Auvil shared, “Our goal in optimizing how we collect, use, and share data is to truly learn all we can from every child so that every child can maximally benefit from what we learn.”