Mammography

In a study of over 30,000 screening digital breast tomosynthesis (DBT) exams for over 8,000 women, researchers found a specificity rate of 95.1 percent, a false negative rate of 2.1 (per 1,000 exams) and an abnormal interpretation rate (AIR) of 5.7 percent.

In a study examining the potential of the large language models ChatGPT-4 and Bard to follow ACR Appropriateness Criteria for breast cancer, lung cancer, ovarian cancer and colorectal cancer screening, researchers noted “impressive accuracy in making radiologic clinical decisions.”

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Contrast-enhanced mammography had a 98 percent sensitivity rate for diagnosing invasive lobular carcinoma and provided high conspicuity for 82 percent of detected lesions, according to research presented at the recent Radiological Society of North America (RSNA) conference.

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In a new study examining population attributable risk proportions (PARPs) based on data from over three million screening mammography exams, researchers found that postmenopausal Black women had the highest BMI-related PARP and premenopausal Asian and Pacific Islander women had the highest breast density-related PARP for advanced breast cancer.

Contrast-enhanced mammography detected additional lesions in 43 percent of patients and led to additional biopsies in 18.2 percent of patients, over half of whom had malignant lesions, according to a study of over 500 women presented at the recent Radiological Society of North America (RSNA) conference.

The combination of digital breast tomosynthesis (DBT) and digital mammography had a 21.6 higher invasive breast cancer detection rate for stage 1 tumors than digital mammography alone, according to a new study involving nearly 100,000 women.

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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.

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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.

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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.

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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.

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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.