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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.

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NCI’s Division of Cancer Biology Supports Diverse Cancer Data Science That Uses Machine Learning and Artificial Intelligence

A few of NCI’s Division of Cancer Biology grantees recently released publications on topics such as machine learning and artificial intelligence. These research results hold clues to how we research and develop various cancer treatments.

New Terms Added to NCI Thesaurus

Get the Latest Updates to NCI’s Thesaurus and CDISC Controlled Terminology

The new terminology for female reproductive neoplasms in NCI’s Thesaurus aligns with the latest World Health Organization standards. Other updates support CDISC’s standards and are intended to further facilitate the collection, management, and analysis of research data from human clinical trials and other studies.

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CBIIT, NCI, and Colleagues Set New Bioinformatic Benchmarks for Genetic Variants in Two Distinct Cell Lines Linked to Breast Cancer

Staff from CBIIT and NCI, alongside partners from NIH, FDA, and a consortium of scientists from across the world, joined forces to create reference samples and data call sets to help the cancer community further decipher cancer-related gene mutations. Their findings were recently published in Nature Biotechnology.

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.

The SEER Logo. Text Reads SEER. The text is set in a blue decortive box.

New Webinar “SEERies” Features NCI Analytic Tools for Surveillance, Epidemiology, and End Results (SEER) Data and Other Cancer Surveillance Data

The NCI Surveillance Research Program is hosting several webinars in October and November on resources for the analysis of SEER data and other cancer surveillance data. These webinars will include information on SEER*Stat, statistics, variables, methods, and software.

CAT scan image of brain; scan shows four different images of a brain with tumor from different perspectives (side and top).

Using Data Science Matching Approaches Open Opportunities for Understanding Cancer in Veterans

Dr. Jill Barnholtz-Sloan, CBIIT’s associate director of Informatics and Data Science and DCEG senior investigator, together with research colleagues, used a direct data matching approach to compare brain tumors in U.S. Veteran and non-Veteran populations. The study indicates that direct and deterministic data matching approaches have the potential to compare the distribution of tumors, treatment trajectories, and clinical outcomes of other cancers and rare diseases among these populations.

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.

An all-white puzzle with one piece pulled up that has a red magnifying glass on it. The area underneath the raised puzzle piece is red, showing stark contrast against the white puzzle pieces.

NIH Issues RFI for Input on “Search” Capabilities That Could Enhance NIH-Wide Data and Information Discovery

Interested in making data discoverable to the larger research community? Share your perspective with NIH, who wants to know how to improve data searchability and discovery. Cancer researchers, data submitters/generators, data users, and technology providers should respond by December 3, 2021.

Abstract image of DNA on blue background with text in the foreground stating: “New Genomic Data Available”

Genomic Data from Chernobyl Studies Is Now Available in the Genomic Data Commons

The NCI Genomic Data Commons now has two new projects from studies about the potential health effects of exposure to ionizing radiation from the 1986 accident at the Chernobyl nuclear power plant in northern Ukraine.

Image depicts a futuristic-looking head outlined in electric blue with circuits running throughout, representing brain activity. The head is surrounded by numbers, ones and zeros, and there are flashes of hot pink around the head and going through it.

Submit Input to NIH and FDA About Next Generation Sequencing and Radiomics Resource Requirements

Cancer researchers and data scientists have the opportunity to provide NIH and the FDA input on the requirements for accelerating clinical applications of next generation sequencing and radiomics (including those using artificial intelligence and machine learning). Responses to these Requests for Information are due Monday, November 1, 2021.

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