News

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

See how NCI-funded researchers built on previous studies to create a new model (called SMuRF) for head and neck cancer. Their model offers a new, more human-like, perspective to assessing head and neck 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!

Learn how CGS-Net can assist in diagnosing cancer by incorporating contextual information for more accurate segmentation in medical images.

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.

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.

Here at NCI, how are we maximizing data utility? Two of our leaders comment on the ways we’re making data ready for use with artificial intelligence (AI), more valuable to the cancer research community, and more!

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.

An NCI-funded study shows that KBP models can automate radiation therapy, thereby producing high-quality plans to improve cancer treatment efficiency and reduce human workload.