Use of nuclear myocardial perfusion imaging exams dropped sharply between 2006 and 2011, but substitute imaging studies haven’t increased.
Use of nuclear myocardial perfusion imaging (MPI) has decreased significantly, according to a recent study in JAMA.
Researchers from Kaiser Permanente Medical Center in San Francisco, Calif., examined trends of MPI use from 2000 to 2011. The researchers obtained data regarding patients, aged 30 years or older, who underwent MPI within the healthcare delivery system.
The findings showed that MPI was used for 302,506 patients at 19 facilities. The imaging use increased by a relative 41 percent from 2000 to 2006. However, from 2006 to 2011, MPI use dropped by a relative 51 percent. Use dropped by 58 percent among outpatients and 31 percent among inpatients and for patients under age 65.
When looking to see if other imaging tests increased as a result of the MPI decrease, the researchers found that use of cardiac CT did increase and could have accounted for 5 percent of the observed decline in overall MPI use if performed as a substitute.
“The substantial reduction in MPI use demonstrates the ability to reduce testing on a large scale with anticipated reductions in health care costs,” wrote authors Edward J. McNulty, MD, and colleagues.
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?
April 14th 2025In 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).
Could Lymph Node Distribution Patterns on CT Improve Staging for Colon Cancer?
April 11th 2025For 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.