A new CT- and PET-imaging-based approach—one that entails applying big data to personalizing treatment protocols—is needed to better identify which head and neck carcinoma (HNC) patient subgroups respond to which specific therapies.

In a world of networked medical devices, it’s not hard to imagine a radiology-heavy cyberattack that is not only malicious but also ingenious.
 

A recent study validating the 2017 version of the ultrasound Liver Imaging Reporting and Data System (US LI-RADS) for detecting hepatocellular carcinoma (HCC) identified a few limitations in its scoring.

“The fact that CAD significantly shortened interpretation time is important, especially if either state or federal legislation ends up mandating, or even recommending, additional screening with US for women with dense tissue on mammograms," wrote Priscilla J. Slanetz, MD, MPH, in an accompanying editorial.

The American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) was originally created to improve patient management and avoid unnecessary fine needle aspiration biopsy in patients with thyroid nodules. However, its clinical use is still questioned.

Compressed sensing (CS) can reduce the typically long acquisition time of conventional ankle MRIs while preserving imaging quality, according to a small study published in the European Journal of Radiology.

A 2017 update to LI-RADS included an algorithm for hepatocellular carcinoma (HCC) screening and surveillance on ultrasound. While HCC remains the sixth most common cancer, the clinical performance of US LI-RADS for diagnosing the disease has yet to be studied.

The Machine Learning Challenge on Pneumothorax Detection and Localization will kick-off at the SIIM 2019 Annual Meeting starting June 26 in Aurora, Colorado.