The University of Texas MD Anderson Cancer Center and Springer Nature will co-host a conference to explore the latest research on cancer therapeutics, highlighting advances in the development of next-generation treatment modalities and the technologies enabling them. “Next Generation Cancer Therapeutics” is scheduled for Oct. 6-8, 2026, on the UT MD Anderson campus. 

Study compares three deep learning approaches for finding stroke lesions on contrast-free MRI, a step toward faster, more automated screening.

When someone may be having a stroke, every minute counts, and doctors often turn first to MRI scans that do not require an injected contrast dye; these are faster to obtain and more widely available in everyday clinical settings. The catch is that these contrast-free scans, known as FLAIR sequences, can be harder for computer programs to read automatically than more detailed scans, because the damaged brain tissue often shows up faintly, in irregular shapes, and looks somewhat different from one MRI machine to the next. A new study, published ahead of print in the Journal of Intelligent Systems in Current Computer Engineering, tested three different artificial intelligence approaches to see which handles this challenge best.