Journal of Breast Imaging最新文献

As molecular imaging use expands for patients with breast cancer, it is important for breast radiologists to have a basic understanding of molecular imaging, including PET. Although breast radiologists may not directly interpret such studies, basic knowledge of molecular imaging will enable the radiologist to better direct diagnostic workup of patients as well as discuss diagnostic imaging with the patient and other treating physicians. Several new tracers are now available to complement imaging glucose metabolism with FDG. Because it provides a noninvasive assessment of disease status across the whole body, PET offers specific advantages over tissue-based assays. Paired with targeted therapy, molecular imaging has the potential to guide personalized treatment of breast cancer, including guiding dosing during drug trials as well as predicting and assessing clinical response. This review discusses the current established applications of FDG, which remains the most widely used PET radiotracer for malignancy, including breast cancer, and highlights potential areas for expanded use based on recent research. It also summarizes research to date on the U.S. Food and Drug Administration (FDA)-approved PET tracer 16α-18F-fluoro-17β-estradiol (FES), which targets ER, including the current guidelines from the Society of Nuclear Medicine and Molecular Imaging on the appropriate use of FES-PET/CT for breast cancer as well as areas of active investigation for other potential applications. Finally, the review highlights several of the most promising novel PET tracers that are poised for clinical translation in the near future.

Objective: Changes in a patient's reported breast density status (dense vs nondense) trigger modifications in their cancer risk profile and supplemental screening recommendations. This study tracked the frequency and longitudinal sequence of breast density status changes among patients who received serial mammograms.

Methods: This IRB-approved, HIPAA-compliant retrospective cohort study tracked breast density changes among patients who received at least 2 mammograms over an 8-year study period. BI-RADS density assessment categories A through D, visually determined at the time of screening, were abstracted from electronic medical records and dichotomized into either nondense (categories A or B) or dense (categories C or D) status. A sequence analysis of longitudinal changes in density status was performed using Microsoft SQL.

Results: A total of 58 895 patients underwent 231 997 screening mammograms. Most patients maintained the same BI-RADS density category A through D (87.35% [51 444/58 895]) and density status (93.35% [54 978/58 859]) throughout the study period. Among patients whose density status changed, the majority (97% [3800/3917]) had either scattered or heterogeneously dense tissue, and over half (57% [2235/3917]) alternated between dense and nondense status multiple times.

Conclusion: Our results suggest that many cases of density status change may be attributable to intra- and interradiologist variability rather than to true underlying changes in density. These results lend support to consideration of automated density assessment because breast density status changes can significantly impact cancer risk assessment and supplemental screening recommendations.

The economics of health care and payment policy are complex and continually evolving. Breast imaging radiologists may not feel equipped to understand the financial aspect of their practice, but this is a critical competency from residency to senior leadership, especially for breast imaging radiologists. From conducting effective negotiations for new equipment as technology evolves to understanding how insurance benefit design affects patient access to care, breast imaging radiologists need to grasp the financial structures that underpins their practice. Fortunately, resources exist that are appropriate for each career stage, and this article directs the breast imaging radiologist to those resources.

Image-guided biopsy is an integral step in the diagnosis and management of suspicious image-detected breast or axillary lesions, allowing for accurate diagnosis and, if indicated, treatment planning. Tissue sampling can be performed under guidance of a full spectrum of breast imaging modalities, including stereotactic, tomosynthesis, sonographic, and MRI, each with its own set of advantages and limitations. Procedural planning, which includes consideration of technical, patient, and lesion factors, is vital for diagnostic accuracy and limitation of complications. The purpose of this paper is to review and provide guidance for breast imaging radiologists in selecting the best procedural approach for the individual patient to ensure accurate diagnosis and optimal patient outcomes. Common patient and lesion factors that may affect successful sampling and contribute to postbiopsy complications are reviewed and include obesity, limited patient mobility, patient motion, patients prone to vasovagal reactions, history of anticoagulation, and lesion location, such as proximity to vital structures or breast implant.

Objective: Inaccurate breast biopsy marker placement and marker migration during stereotactic biopsy procedures compromise their reliability for lesion localization and precise surgical excision. This trial evaluated the impact of 5-mm predeployment retraction of the marker introducer on marker migration, investigating other potential factors that influence the outcome.

Methods: This parallel, randomized controlled trial enrolled women aged ≥18 years undergoing stereotactic breast biopsy at a single institution from May 2020 through August 2022. The study was approved by the institutional review board at the University of Alabama at Birmingham (UAB). Patients were randomized to intervention (5-mm introducer retraction before marker deployment) or control (standard marker placement) by drawing a labeled paper. The primary outcome was the distance of marker migration on immediate postprocedure mammogram.

Results: Of 251 patients enrolled, 223 were analyzed; 104 received the intervention, and 119 received control. Mean (SD) marker migration was 12.1 (14.9) mm in the intervention group vs 9.8 (14.9) mm, with differences between groups estimated at 2.3 mm (SE = 1.9, P = .2312) (d = 0.16; 95% CI, 1.5-6.0). Effects of age, breast density, thickness, and biopsy approach showed no statistical significance. In exploratory models, central lesions exhibited 5.7 mm less migration than proximal lesions (95% CI, 0.7-10.6; P = .025), and each body mass index (BMI) unit increase was associated with 0.3 mm greater migration (95% CI, 0-0.6; P = .044).

Conclusion: Retracting the marker introducer 5 mm before deployment did not reduce migration. Higher BMI and certain lesion locations were all associated with marker migration, highlighting the need to investigate biomechanical factors and techniques to optimize breast marker placement.Clinical Trials Registration: NCT04398537.

Objective: The performance of a commercially available artificial intelligence (AI)-based software that detects breast arterial calcifications (BACs) on mammograms is presented.

Methods: This retrospective study was exempt from IRB approval and adhered to the HIPAA regulations. Breast arterial calcification detection using AI was assessed in 253 patients who underwent 314 digital mammography (DM) examinations and 143 patients who underwent 277 digital breast tomosynthesis (DBT) examinations between October 2004 and September 2022. Artificial intelligence performance for binary BAC detection was compared with ground truth (GT) determined by the majority consensus of breast imaging radiologists. Area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value (NPV), accuracy, and BAC prevalence rates of the AI algorithm were compared.

Results: The case-level AUCs of AI were 0.96 (0.93-0.98) for DM and 0.95 (0.92-0.98) for DBT. Sensitivity, specificity, and accuracy were 87% (79%-93%), 92% (88%-96%), and 91% (87%-94%) for DM and 88% (80%-94%), 90% (84%-94%), and 89% (85%-92%) for DBT. Positive predictive value and NPV were 82% (72%-89%) and 95% (92%-97%) for DM and 84% (76%-90%) and 92% (88%-96%) for DBT, respectively. Results are 95% confidence intervals. Breast arterial calcification prevalence was similar for both AI and GT assessments.

Conclusion: Breast AI software for detection of BAC presence on mammograms showed promising performance for both DM and DBT examinations. Artificial intelligence has potential to aid radiologists in detection and reporting of BAC on mammograms, which is a known cardiovascular risk marker specific to women.