Differentiating true glioblastoma progression from pseudoprogression is a vexing challenge with big implications for management. New research reveals the promise of taking an algorithmic approach to MRI evaluation.
A novel biomarker derived via machine learning analysis of perivascular fat predicts cardiac risk better than current risk stratification methods, a new study finds.
Cleveland Clinic researchers have trained an advanced computer network to find subtle radiation sensitivity features in the CT scans of individual lung cancer patients that can predict the likelihood of successful radiotherapy outcomes. The network can generate a personalized radiation dose plan that reduces the probability of treatment failure to less than 5%.