Women's health related to breast imaging, including mammography, breast MRI, ABUS, automated breast ultrasound, breast ultrasound, breast biopsy, PEM and positron emission mammography.
Images in a 57-year-old woman noted to have "good prognosis" invasive cancer detected at digital breast tomosynthesis (DBT) screening. (a) Craniocaudal view of the left breast obtained with the two-dimensional digital mammography (DM) portion of the DM/DBT screening study demonstrates a subtle area of distortion in the medial left breast. (b) Single-slice image from the left craniocaudal DBT portion of the screening study shows an area of bridging distortion (circle). (c) Electronically enlarged image of the area of concern seen on the left craniocaudal view in a single DBT slice as shown in b. (d) Targeted US scan demonstrates two small adjacent irregular solid masses. US-guided core biopsy yielded an invasive carcinoma of the tubular subtype that was estrogen receptor positive, progesterone receptor positive, and human epidermal growth factor receptor 2 negative. The results of the sentinel node biopsy were negative. Image courtesy of the Radiological Society of North America
Example: SoftVue image stacks of sound speed, as shown for cases ranging across the four Breast Imaging Reporting and Data System (BI-RADS) breast density categories ((a), fatty; (b), scattered; (c), heterogeneously dense; (d), extremely dense). Note the quantitative scale indicating that absolute measurements are obtained. Image courtesy of MDPI
Christopher Comstock, M.D., (Memorial Sloan Kettering Cancer Center) is the lead author of a paper in JAMA that reports that abbreviated breast MRI detected significantly more (almost 2 and a half times as many) breast cancers than digital breast tomosynthesis (3-D mammography) in average-risk women with dense breasts. Photo courtesy of Memorial Sloan Kettering Cancer Center
Mammograms of a 49-year-old woman with invasive lobular carcinoma on the right-side breast. A small mass with micro-calcifications on the right-side breast was detected correctly by AI with an abnormality score of 96%. This case was recalled by 7 out of 14 radiologists (4 breast radiologists and 3 general radiologists) initially (without AI) and all 14 radiologists recalled this case correctly with the assistance of AI.
A 50-y-old postmenopausal woman with fibroadenoma (arrows) in left breast. (A) Unenhanced fat-saturated T1-weighted MRI shows extreme amount of FGT (ACR d). (B) Moderate BPE is seen on dynamic contrast-enhanced MRI at 90 s. (C) Mean ADC of breast parenchyma of contralateral breast on diffusion-weighted imaging with ADC mapping is 1.5 × 10?3 mm2/s. (D) On 18F-FDG PET/CT, lesion is not 18F-FDG-avid, and BPU of normal breast parenchyma is relatively high, with SUVmax of 3.2. Photo courtesy of K Pinker, et al., Medical University of Vienna, Vienna, Austria
This is a lung X-ray reviewed automatically by artificial intelligence (AI) to identify a collapsed lung (pneumothorax) in the color coded area. This AI app from Lunit is awaiting final FDA review and in planned to be integrated into several vendors' mobile digital radiography (DR) systems. Fujifilm showed this software integrated as a work-in-progress into its mobile X-ray system at RSNA 2019. GE Healthcare has its own version of this software for its mobile r=ray systems that gained FDA in 2019.