Recently published research projected that 103,000 future cases of radiation-induced cancer would result from 93 million computed tomography (CT) exams performed in the United States in 2023.
In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.
In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).
For patients with microsatellite instability-high colon cancer, distribution-based clinical lymph node staging (dCN) with computed tomography (CT) offered nearly double the accuracy rate of clinical lymph node staging in a recent study.
LI-RADS category 5 (LR-5) assessment had an 11 percent higher AUC for detecting hepatocellular carcinoma (HCC) in patients with non-cirrhotic chronic hepatitis C (CHC) in comparison to those with cirrhotic CHC.