A fully automated body composition analysis derived from CT imaging can be a valuable pretreatment tool for patients with colorectal cancer. 

In a close collaboration with radiation oncologists, experts trained their model on the CT lung images of 787 patients and tested it on the scans of more than 1,300 patients from external datasets.

Nearly 20% of the women included in the study did not complete their follow-up imaging at all, and out of the 3,648 women who did adhere to additional imaging, 23.3% delayed their exams beyond the recommended 60-day period.

Though the participants’ actual ages ranged from 69 to 72 years old, the experts’ predictive analysis revealed their estimated current brain ages to be between 46 and 93.

Experts arrived at this conclusion after comparing incidents of hematoma between patients who did and did not receive antithrombotic therapy prior to their procedure.

A new paper in the Journal of the American College of Radiology digs into the emergence of physician assistants and nurse practitioners in radiology and how it has impacted clinical and procedural volumes and imaging interpretation.

The experts maintained that these MRI parameters support the diagnosis of IIH in clinically suspicious cases and could help reduce the use of invasive procedures when aligned with symptoms.

When applied to MRI and clinical features, different machine learning models were recently shown to reliably predict post-treatment recurrences of hepatocellular carcinoma.