Women’s imaging encompasses many radiology procedures related to women and the diseases that are most prevalent to women such as breast cancer or gynecological issues. Mammogram, breast ultrasound, breast MRI and breast biopsy are the most commonly used procedures.
The photoacoustic CT, or PACT, imaging technique is said to perform comparably to mammography for cancer detection, but without the discomfort of standard mammograms.
The agent “exhibits powerful tumor delineation” in challenging cases of determining cancer subtypes, and could potentially lead to more personalized, effective treatment strategies.
New research adds to the “strong evidence” supporting screening guidelines and highlights the importance of women adhering to clinical recommendations.
Researchers have demonstrated a deep learning model that can correct course for breast radiologists who otherwise may have erroneously deemed tissue dense in screening exams.
Post-treatment changes may mask some of the subtle, early signs of recurring breast cancer on traditional mammography, an Academic Radiology editorial explains.
Computer-aided detection boosted by AI has often proven superior to traditional CAD over the past decade, yet the “new way” has been slow to win broad adoption.
Assessing more than 11,000 patients with lesions designated as BI-RADS 4, radiologists using digital breast tomosynthesis found no significant diagnostic advantages over standard 2D mammography.
Providers screening women with dense breasts and benign breast disease should consider individualized mammogram protocols for these patients, authors of the study suggested.
The FDA has OK’d two subsidiaries of Los Angeles-based RadNet to sell medical AI software—one product for diagnosing breast cancer, the other for streamlining MRI prostate reporting workflows.
Combining ensemble AI models with reads from breast radiologists of mixed experience levels can help health systems consistently diagnose malignant architectural distortion on mammography.