Cancer Data Science Pulse

Read the blogs that topped our charts in 2022 and see if your favorite made #1!

In honor of National Lung Cancer Awareness Month, we’re highlighting the “data deets” (details) for the National Lung Screening Trial, a large-scale effort that collected imaging data for more than 53,000 heavy smokers. In this blog, we’ll cover the research that drove this data, specific metrics about the data set, how to access it, and some of the exciting data science projects using the data.

Ever wonder what it’s like to work on a data ecosystem? Meet software engineer Ming Ying, and website specialists Hannah Stogsdill and Ambar Rana, as they describe what it’s like to design, develop, implement, and maintain NCI’s Integrated Canine Data Commons.

As NCI recognizes Breast Cancer Awareness Month this October, we highlight several data science resources to assist in your breast cancer research.

Watch our time capsule video to learn about the current status of the field and new technologies that are sure to be important as we embark on the next era of cancer data research.

Meet the people who are breaking new ground in the data science field, whether it’s a new tool, a new model, or a completely new way of using data. Here, we’re featuring Svitlana Volkova, Ph.D., chief scientist at Pacific Northwest National Laboratory. She’ll describe how she’s using “foundation models” to give scientists and analysts a new tool for unleashing the power of artificial intelligence (AI).

CBIIT’s NIH Data and Technology Advancement (DATA) Scholar, Dr. Jay G. Ronquillo, offers a bird’s-eye view of cloud computing, including tips for managing costs, access, and training to help advance precision medicine and cancer research.

In this blog, Dr. Elana J. Fertig describes how she is using artificial intelligence, blended with spatial and single cell technologies, to better understand how cancer will respond to treatment. Predicting the changes that occur in the tumor during treatment may someday enable us to select therapies in advance, essentially stopping the disease in its tracks before it reaches the next stage in its evolution.

In this blog, University of Maryland's Mrs. Aya Abdelsalam Ismail examines the use of Deep Learning in medical applications, especially as a means for following a disease or disorder over time. She’ll describe how a “wrong turn” in her research on forecasting Alzheimer’s Disease led her to question her model’s performance. Her findings are particularly relevant for Deep Learning models in the cancer field, which use images obtained from patients, often at different points in time.

To commemorate the National Cancer Act’s 50th anniversary, we’ve pulled together Five Data Science Technologies poised to make a difference in how cancer is diagnosed, treated, and prevented.