Report from SNM: PET/CT produces Image of the Year
The ability to simultaneously display 3D anatomic and molecular information to evaluate lung cancer earned Dr. Andrew Quon recognition for the Society of Nuclear Medicine’s Image of the Year.
The ability to simultaneously display 3D anatomic and molecular information to evaluate lung cancer earned Dr. Andrew Quon recognition for the Society of Nuclear Medicine's Image of the Year. Dr. Henry Wagner singled out an example of 3D FDG-PET/CT bronchoscopy produced by Quon, an assistant professor of radiology at the Institute for Molecular Imaging at Stanford University. The image employs metabolic FDG-PET to identify the position of a primary cancer lesion in a lung and an avid mediastinal lymph node partially obscured by the bronchus. Three-D-rendered multislice CT contributes a stunning view of surrounding anatomy. It allows the physician to rule out endobronchial involvement. Wagner, a professor of environmental health sciences at Johns Hopkins University, announced the selection Monday at the SNM meeting in Toronto.
What is the Best Use of AI in CT Lung Cancer Screening?
April 18th 2025In 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.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
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