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MR perfusion measures coronary stenoses
Quantitative myocardial first-pass perfusion can distinguish coronary artery stenoses with a high degree of specificity and negative predictive value. The test offers an alternative to diagnostic catheterization to grade the severity of coronary artery disease, according to a presentation at the North American Society of Cardiac Imaging meeting.
Quantitative myocardial first-pass perfusion can distinguish coronary artery stenoses with a high degree of specificity and negative predictive value. The test offers an alternative to diagnostic catheterization to grade the severity of coronary artery disease, according to a presentation at the North American Society of Cardiac Imaging meeting.
Dr. Prasad M. Panse and colleagues at the University of Florida at Jacksonville studied 41 patients who underwent catheter angiography and MR first-pass perfusion. The sensitivity, specificity, PPV, and NPV for MR imaging to detect severe stenosis as determined visually from coronary angiograms were 67.6%, 90.1%, 56.5%, and 94.5%, respectively, for each sector. The low per-sector sensitivity was likely the result of microvascular disease, small-vessel disease in diabetic patients, subendocardial ischemia, or other cardiac risk factors. On a per-patient basis, sensitivity rose to 92%.
FDA Approves Fluorescence Imaging System for Detecting Residual Breast Cancer
April 18th 2024The combination of the optical imaging agent Lumisight and the fluorescence imaging device Lumicell Direct Visualization System, collectively known as LumiSystem, reportedly offers 84 percent accuracy with real-time detection of residual breast cancer after lumpectomy procedures.
Study of Ofatumumab for Multiple Sclerosis Shows 'Profoundly Suppressed MRI Lesion Activity'
April 17th 2024The use of continuous ofatumumab in patients within three years of a relapsing multiple sclerosis diagnosis led to substantial reductions in associated lesions on brain MRI scans, according to research recently presented at the American Academy of Neurology (AAN) conference.
Could a Deep Learning Model for Mammography Improve Prediction of DCIS and Invasive Breast Cancer?
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