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

An icon depicts a human entering a CT scanning machine. Foreground text reads, "New Imaging Data."

New Data Sets from The Cancer Imaging Archive—See What’s New in Glioblastoma and Colorectal Data

TCIA has released three new data collections for cancer research. The new collections feature data from glioblastoma multi-parametric magnetic resonance imaging (mpMRI), a glioblastoma-based MRI Digital Reference Object (DRO), and data from colorectal digital biopsy slides.

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Share Feedback on NCI’s Imaging Data Commons

Do you work with imaging data and tools? Share feedback on NCI’s Imaging Data Commons!

#MICCAI2022 Breast Density FL Challenge. White text with a dark blue background, colorful flares coming from the bottom up.

Compete in the MICCAI 2022 Federated Learning Breast Density Challenge

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.

New Data Set

New Breast Cancer Data Collections Released from the I-SPY2 Clinical Trial

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.

Abstract technical image of a brain with connected lines and blue, green, and red circles representing neurons and activity traversing the brain.

NCI Cancer Blog Defines Artificial Intelligence and Elaborates on its Potential for Cancer Imaging Research

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?

Image of datacommons.cancer.gov homepage on a computer with individual reviewing it. Reads "The NCI Cancer Research Data Commons (CRDC) is a cloud-based data science infrastructure that provides secure access to a large, comprehensive, and expanding collection of cancer research data. Users can explore and use analytical and visualization tools for data analysis in the cloud." Bottom reads "datacommons.cancer.gov"

The Evolution of NCI’s Data Commons

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.

Publicly Available Imaging Data | National Lung Screening Trial (NLST)

Access Publicly Available Imaging Data from the National Lung Screening Trial

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.

Abstract image with title: New Features and Data! NCI Imaging Data Commons. Icons below include: microscope, chest xray, laptop with coding symbol, API symbol

New Data and Updated Features at NCI's Imaging Data Commons

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.

Figure shows the graphical abstract from the recent article by Hong et al., “Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models,” which was published in Cell Reports Medicine. Using this machine learning model, researchers were able to interpret histological slides to identify four key subtypes of endometrial cancer (CNV-H, CNV-L, MSI-H, and POLE).

NCI-funded Machine Learning Gives New Insight into Endometrial Cancer

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.

Humaniod depiction of artificial intelligence/machine learning

NIH Issues RFI for Advancing AI/ML Technologies to Redress Health Disparities: Responses Due July 9

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.

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