Journal of Breast Imaging最新文献

Beyond the technical aspects, success and long-term patient outcomes of image-guided breast biopsies depend on the overall patient experience. Patient experience in turn is influenced by intangible factors, such as environmental features during the procedure; patient-centered communication prior to, during, and subsequent to the procedure; and management of expectations and biopsy complications. Here, we review evidence-based literature and results of a national Society of Breast Imaging survey on approaches to both mitigate and manage common image-guided core biopsy complications as well as nontechnical strategies to improve the patient biopsy experience.

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.

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: 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.

Objective: The purpose of this study was to evaluate the utility of US for identifying and characterizing lesions detected on molecular breast imaging (MBI).

Methods: A retrospective single-institution review was performed of patients with MBI studies with subsequent US for abnormal MBI findings between January 1, 2015, and September 30, 2021. Medical records, imaging, and histopathology were reviewed. The reference standard was histopathology and/or imaging follow-up. Associations among MBI findings, the presence of an US correlate, and histopathology were evaluated by Fisher exact tests.

Results: The 32 lesions detected on MBI in 25 patients were evaluated by US, and 19 lesions had an US correlate (19/32, 59%). Mass uptake was more likely to have an US correlate (11/13, 85%; P = .02) than nonmass uptake (7/19, 37%), and mass uptake was more likely to be malignant (5/13, 38%; P = .01). Of the 13 lesions without an US correlate, 5 were evaluated and subsequently biopsied by MRI (2 high-risk lesions and 3 benign lesions). Follow-up MBIs demonstrated stability/resolution for 5 lesions in 4 patients at 6 months or longer. Three patients had no further imaging.

Conclusion: Mass lesions identified on MBI were more likely to have an US correlate and were more likely to be malignant than nonmass lesions.

Objective: There are limited data on the application of artificial intelligence (AI) on nonenriched, real-world screening mammograms. This work aims to evaluate the ability of AI to detect false negative cancers not detected at the time of screening when reviewed by the radiologist alone.

Methods: A commercially available AI algorithm was retrospectively applied to patients undergoing screening full-field digital mammography (FFDM) or digital breast tomosynthesis (DBT) at a single institution from 2010 to 2019. Ground truth was established based on 1-year follow-up data. Descriptive statistics were performed with attention focused on AI detection of false negative cancers within these subsets.

Results: A total of 26 694 FFDM and 3183 DBT examinations were analyzed. Artificial intelligence was able to detect 7/13 false negative cancers (54%) in the FFDM cohort and 4/10 (40%) in the DBT cohort on the preceding screening mammogram that was interpreted as negative by the radiologist. Of these, 4 in the FFDM cohort and 4 in the DBT cohort were identified in breast densities of C or greater. False negative cancers detected by AI were predominantly luminal A invasive malignancies (9/11, 82%). Artificial intelligence was able to detect these false negative cancers a median time of 272 days sooner in the FFDM cohort and 248 days sooner in the DBT cohort compared to the radiologist.

Conclusion: Artificial intelligence was able to detect cancers at the time of screening that were missed by the radiologist. Prospective studies are needed to evaluate the synergy of AI and the radiologist in real-world settings, especially on DBT examinations.