Mammography

Catch up on the top AI-related news and research in radiology over the past month.

Offering an all-in-one platform of artificial intelligence (AI) applications, MyBreastAI Suite reportedly facilitates early breast cancer detection and enhances efficiency with breast imaging workflows.

Consistent adherence to the five most recent mammography screenings prior to a breast cancer diagnosis reduced breast cancer death risk by 72 percent in comparison to women who did not have the mammography screening, according to new research findings presented at the annual Radiological Society of North America (RSNA) conference.

Dr. Kottler sat down with Diagnostic Imaging at RSNA 2023 to discuss AI imaging milestones and the potential impact of AI on workflows in radiology.

Catch up on the top radiology content of the past week.

In findings from an enriched cohort of asymptomatic patients with screening-detected abnormalities, researchers found that contrast-enhanced mammography (CEM) was superior to conventional mammography and offered equivalent detection of breast cancer in comparison to breast MRI and abbreviated breast MRI.

Lunit Insight DBT may facilitate improved detection and efficiency for radiologists interpreting digital breast tomosynthesis images.

Catch up on the top radiology content of the past week.

Mammography-based artificial intelligence (AI) models demonstrated an 11 percent higher median AUC for predicting breast cancer than traditional clinical risk factors, according to a new systematic review of 16 retrospective studies.

Incorporating text messaging into your mammography screening program may facilitate improved mammography screening rates in your patient population as well as enhanced practice management efficiencies.

Catch up on the top AI-related news and research in radiology over the past month.

Catch up on the top radiology content of the past week.

Artificial intelligence (AI) software assigned high malignancy risk scores to mammography exams completed up to two years prior to breast cancer diagnosis in over 38 percent of screen-detected cancer cases and over 39 percent of interval cancer cases, according to newly published research.

Utilized in conjunction with screening digital mammography or digital breast tomosynthesis (DBT), the artificial intelligence (AI)-powered software cmAngio may help detect and localize breast arterial calcification (BAC), an incidental finding that has been linked to an elevated risk for heart disease and stroke.

In reportedly the first randomized study to compare hypofractionated radiation treatment versus conventional radiation treatment for women who had breast reconstruction procedures after mastectomy, researchers at the recent ASTRO conference noted similar rates of cancer recurrence, chest wall toxicity and patient well-being.

Catch up on the top AI-related news and research in radiology over the past month.

Catch up on the top radiology news of the past week.

New research suggests the capability of a mammography-based deep learning model to identify women at high risk of breast cancer led to more than triple the cancer detection rate on breast MRI in comparison to traditional risk assessment tools.

Catch up on the top radiology content of the past week.

In a prospective study of over 55,000 women who had screening mammography, researchers found that double-reading by a radiologist and artificial intelligence (AI) was non-inferior to double-reading by two radiologists in detecting breast cancer.

Catch up on the top radiology content of the past week.

In separate test sets that included challenging mammography cases, researchers found that artificial intelligence (AI) demonstrated similar sensitivity and specificity for detecting breast cancer in comparison to assessments from over 500 clinicians.

Catch up on the top radiology content of the past week.

In the third episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss the challenges of expanded breast cancer screening amid a backdrop of radiologist shortages and ever-increasing volume on radiology worklists.

The study demonstrated that the combined model yielded improved risk assessment for both interval and long-term breast cancers.