Here's what to expect this week on Diagnostic Imaging.
In this week’s preview, here are some highlights of what you can expect to see coming soon on Diagnostic Imaging:
November is Lung Cancer Awareness month, and the past few weeks have provided new details about utilization rates of low-dose CT screening, as well as how effective the scan is in reducing the number of lung cancer-related deaths. This week, Diagnostic Imaging spoke with Debra S. Dyer, M.D., chair of the American College of Radiology’s Lung Cancer Screening 2.0 Committee, about the challenges that exist to increasing utilization with high-risk populations and what radiologists can do to help bring more patients in for screening.
For additional video interviews on lung cancer screening, click here.
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As the United States anticipates the Thanksgiving holiday this week, the rates of COVID-19 infection continue to rise – with the potential for even greater spikes during the holiday season. Consequently, efforts are still ongoing to improve diagnostic techniques. This week, look for new coverage on the role that artificial intelligence can play in improving detection of the virus.
For more artificial intelligence news, click here.
Healthcare disparities continue to be problems that plague several groups, particularly racial and ethnic minorities. With every new published study, radiologists learn more about how to address these issues. Within the next few days, keep an eye out from the latest findings about racial and ethnic minorities and stroke.
For additional stroke coverage, click here.
Could AI-Powered Abbreviated MRI Reinvent Detection for Structural Abnormalities of the Knee?
April 24th 2025Employing deep learning image reconstruction, parallel imaging and multi-slice acceleration in a sub-five-minute 3T knee MRI, researchers noted 100 percent sensitivity and 99 percent specificity for anterior cruciate ligament (ACL) tears.
Meta-Analysis Shows Merits of AI with CTA Detection of Coronary Artery Stenosis and Calcified Plaque
April 16th 2025Artificial intelligence demonstrated higher AUC, sensitivity, and specificity than radiologists for detecting coronary artery stenosis > 50 percent on computed tomography angiography (CTA), according to a new 17-study meta-analysis.
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).