Medical Imaging

Physicians utilize medical imaging to see inside the body to diagnose and treat patients. This includes computed tomography (CT), magnetic resonance imaging (MRI), X-ray, ultrasound, fluoroscopy, angiography,  and the nuclear imaging modalities of PET and SPECT. 

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Example of the four types of breast tissue density. The density of fibroglandular tissue inside the breast impacts the ability to easily see cancers. Cancers are very easy to spot in fatty breasts, but are almost impossible to find in extremely dense breasts. These examples show craniocaudal mammogram findings characterized as almost entirely fatty (far left), scattered areas of fibroglandular density (second from left), heterogeneously dense (second from right), and extremely dense (far right). RSNA

Breast density notification laws blanket 90% of U.S. women, yet still no national reporting standard is at hand. Why is that?

Dense breast experts Wendie Berg, MD, and JoAnn Pushkin, executive director of DenseBreast-info Inc., explain the current status of breast density patient inform laws, reimbursement and new technologies to aid cancer detection. 

Female Medical Research Scientist Working with Brain Scans

FDA approves AI analysis of high-grade gliomas

An AI startup in the neuro-oncology space has received the government’s go-ahead to market software for analyzing certain fast-growing brain tumors on MRI.

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Current LDCT eligibility criteria leave women and minorities behind

Compared to USPSTF 2013,  modified eligibility criteria could increase cancer detection by 37%.

Illustration of the four types of breast tissue densities. The more dense, the harder it is for radiologists to detect cancers, which had led to about 40 states to now require notiofication of patients if they have dense breasts and the impact on their care, with possible miss-reads and that they may need supplemental imaging.

VIDEO: What is the impact of breast density notification laws?

Society of Breast Imaging (SBI) President John Lewin, MD, discusses how legislation concerning breast density notifications has impacted mammography.

Radiomics-based models can detect pancreatic cancer well before clinical diagnosis

Recently a radiomics-based machine learning model proved highly accurate at predicting which patients would develop pancreatic cancer three to 36 months after abdominal CT imaging.

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Chest X-ray guidelines disappoint in the ED

At one institution, the guidelines are not as effective as many had hoped, according to new post-implementation data.

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How breast cancer screening could increase lung cancer screening compliance in eligible women

Breast cancer screenings present an additional opportunity to identify more women who would also qualify for lung cancer screening, authors of a new paper in JAMA said.

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What factors impact reader variability the most? New research offers insight

A new paper in Radiology explores factors that can lead to reader variability in CT imaging, from the radiologist’s experience level and subspecialty to navigation patterns and time spent interpreting. 

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