Cancer Data Science Pulse

Are you new to the cancer research lab and have realized how important it is to have basic data science knowledge? See how many of these cancer data science questions you answer correctly. After, you can use our training resources to improve your score!

Read the blogs that topped our charts in 2023, and see if your favorite made #1!

NCI recently hosted a two-day workshop with more than 600 developers, researchers, and data scientists from the United States, Canada, and the European Union. Participants addressed some of the challenges of removing personal information from medical images—a process called de-identification. This blog features highlights from the workshop.

If you can see it, you can treat it. In this blog, Dr. Baris Turkbey, senior clinician in NCI’s Molecular Imaging Branch, Center for Cancer Research, explores the field of theranostics. He describes how artificial intelligence and data are helping researchers “see” cancer in a new way, resulting in a more precise way of targeting cancer treatment.

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

Learn more about new streamlined access to broad-use data sets within the database of Genotypes and Phenotypes (dbGaP).

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.

Data and Artificial Intelligence (AI) are a match seemingly made in heaven. By joining data and AI, scientists are able to shift a lot of the burden associated with using data from human to machine. See why the data-AI relationship works so well for cancer research in this offbeat blog featuring two fictitious characters—Datum and his pal Aida.

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.

What do winter storms, airplanes, and cancer research have in common? In this blog, experts on meteorology, aerospace engineering, and radiation oncology explore what we can learn from these very different fields to further advance how we target and apply radiation to more effectively treat cancerous tumors.