News

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

Read about a new collaboration between NCI and a company that develops AI-powered solutions for cancer diagnostics and therapeutics. NCI will be applying these tools to help advance research into personalized treatment for patients with cancer.

Thanks to this NCI-funded study, you now have a host of top-performing predictive models, data types, and training algorithms to help you better classify your patient samples.

Do you work with fusion oncoproteins? Explore a new protein language model (pLM) that NCI-funded researchers trained on fusion oncoproteins to advance discoveries in fusion-driven cancers!

Thanks to a new AI model called “SCORPIO,” you may someday be able to use a simple blood test and routine clinical information to decide which patients will benefit from immunotherapy.

Screening for the best medication for your patient could be more efficient thanks to a new model called “BATCHIE.” See how machine learning is helping boost precision medicine for cancer.

Would you use AI to help make decisions about cancer treatment? Check out this recent study on the complex AI-human interaction that’s at play when making decisions about treatment.

NCI-funded researchers are applying AI to digital pathology images to better understand cellular features, such as “nuclear wrinkling.” Such extreme wrinkling and folding is a hallmark of cancer.

NCI researchers debuted a new deep learning model that could help you decipher the cancer tumor’s microenvironment. The model holds a lot of promise for predicting which patients are most likely to benefit from checkpoint inhibitors.

See how NCI researchers are working to find new ways of interpreting biopsies and managing prostate cancer. This recent model could lead to a more precise approach, reducing the need for additional and often unnecessary biopsies.

Wondering how to use AI for radiology? In a new study, NCI researchers found that AI may work best as an adjunct to the radiologist rather than a standalone solution, allowing radiologists to focus on cases that need more critical assessment.