Mammography

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.

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

Catch up on the top AI-related news and research from the past month.

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

Primary diagnostic delays in mammography screening led to a greater than 10 percent higher incidence of lymph node metastasis with invasive breast cancer in comparison to women without a primary diagnostic delay, according to new research out of the Netherlands.

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

In a new study comparing standard breast MRI, abbreviated breast MRI and contrast-enhanced mammography in supplemental breast cancer screening, researchers found that MRI offered a greater than 14 percent higher cancer detection rate and a nearly 39 percent higher sensitivity rate than CEM.

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

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

Reportedly the first randomized trial to examine the impact of artificial intelligence (AI) on screening mammography, researchers found AI-aided screening led to a 20 percent increase in breast cancer detection and a 44.3 percent decrease in mammography screening workload.

Catch up on the top AI-related news and research from the past month.

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

In a study of over 1.300 women with dense breasts, the combination of mammography and ultrasound had a recall rate of 11.7 percent, a specificity rate of 89.1 percent and an accuracy rate of 89.2 percent in comparison to a 21.4 percent recall rate, 79.4 percent specificity and 79.5 percent accuracy for the combination of mammography, ultrasound, and artificial intelligence (AI).

In a dataset enriched for African American women, BRCA mutation carriers and those with benign breast disease, a mammography-based deep learning model demonstrated a five-year AUC of 63 percent for predicting breast cancer in comparison to 54 percent for BI-RADS assessment.

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