Keep up with the latest news from the NCI Center for Biomedical Informatics and Information Technology (CBIIT) and the data science communities.

Data scientists, informaticists, and medical physicists are invited to develop the best, most generalizable models, algorithms, and approaches for breast density estimation using image-based distributed or federated learning.

Two breast cancer imaging data collections have recently been released and made publicly available by The Cancer Imaging Archive. Together, these two collections comprise the first subgroup of publicly released imaging data from the I-SPY2 clinical trial.

Over the past several years, scientists have made exciting advances in the realm of artificial intelligence (AI) and its integration into the cancer imaging field. New AI tools could make cancer imaging faster, more accurate, and more informative. But are they ready for real-world implementation?

CBIIT Director, Dr. Tony Kerlavage, sat down recently for a podcast examining the evolution of NCI’s Data Commons. He tracked the development of the Cancer Research Data Commons, from its early pilots to today’s cloud-based infrastructure, with repositories of diverse data and more than 1,000 tools and resources.

More than 70,000 CT scans from the National Lung Screening Trial (NLST) are now publicly available (no data access request needed). Read more to learn how to access this data through NCI resources.

The NCI Cancer Research Data Commons’ Imaging Data Commons (IDC) has updated to include more features and 16 terabytes of medical imaging data files for cancer researchers and imaging informaticists.

A new publication using NCI funding and resources shows that a machine learning model, called Panoptes, allowed cancer researchers to reliably predict subtypes of endometrial cancer. Such “computational pathology” offers a useful framework for supporting human pathologists, trimming the labor needed to interpret histological findings to under 4 minutes per slide, and eliminating the time and cost of genetic sequencing.

This RFI will inform the development of an NIH initiative on the use of Artificial Intelligence/Machine Learning to address health disparities and inequities and enhance diversity within the AI/ML workforce.

NCI Director Dr. Ned Sharpless and CBIIT Director Dr. Tony Kerlavage recently published an article, “The Potential of AI in Cancer Care and Research,” which takes stock of current advances in artificial intelligence and machine learning and outlines some of the areas NCI hopes to explore in greater depth in the future.

This innovative team approach connects use cases with data sets to provide artificial intelligence (AI) developers with another set of resources for building AI tools to benefit radiology.