Journal of Breast Imaging最新文献

Objective: Assess current practices and plans regarding home workstations and remote diagnostic breast imaging in the United States.

Methods: A 43-question survey relating to remote breast imaging was distributed to Society of Breast Imaging members from July 6, 2023, through August 2, 2023. A descriptive summary of responses was performed. Pearson's chi-squared test was used to compare demographic variables of respondents and questions of interest.

Results: In total, 424 surveys were completed (response rate 13%, 424/3244). One-third of breast imaging radiologists (31%, 132/424) reported reading examinations from home or a personal remote site for a median of 25% of their clinical time. The most common types of examinations read from home were screening mammography (90%, 119/132), screening US (58%, 77/132), diagnostic mammography and MRI (both 53%, 70/132), and diagnostic US (49%, 65/132). Respondents from private practices were more likely than those from academic practices to read diagnostic imaging from home (67%, 35/52 vs 29%, 15/52; P <.001). Respondents practicing in the West were less likely to read breast imaging examinations from home compared with those in other geographic regions (18%, 12/67 vs 28%-43% for other regions; P = .023). No differences were found among respondents' overall use of home workstations based on age, gender, or having dependents. Most respondents (75%, 318/424) felt that remote breast reading would be a significant practice pattern in the future.

Conclusion: Home workstations for mammography and remote diagnostic breast imaging are a considerable U.S. practice pattern. Further research should explore radiologist preferences regarding remote breast imaging and its impact on clinical care and radiologist well-being.

Objective: To evaluate potential screening mammography performance and workload impact using a commercial artificial intelligence (AI)-based triage device in a population-based screening sample.

Methods: In this retrospective study, a sample of 2129 women who underwent screening mammograms were evaluated. The performance of a commercial AI-based triage device was compared with radiologists' reports, actual outcomes, and national benchmarks using commonly used mammography metrics. Up to 5 years of follow-up examination results were evaluated in cases to establish benignity. The algorithm sorted cases into groups of "suspicious" and "low suspicion." A theoretical workload reduction was calculated by subtracting cases triaged as "low suspicion" from the sample.

Results: At the default 93% sensitivity setting, there was significant improvement (P <.05) in the following triage simulation mean performance measures compared with actual outcome: 45.5% improvement in recall rate (13.4% to 7.3%; 95% CI, 6.2-8.3), 119% improvement in positive predictive value (PPV) 1 (5.3% to 11.6%; 95% CI, 9.96-13.4), 28.5% improvement in PPV2 (24.6% to 31.6%; 95% CI, 24.8-39.1), 20% improvement in sensitivity (83.3% to 100%; 95% CI, 100-100), and 7.2% improvement in specificity (87.2% to 93.5%; 95% CI, 92.4-94.5). A theoretical 62.5% workload reduction was possible. At the ultrahigh 99% sensitivity setting, a theoretical 27% workload reduction was possible. No cancers were missed by the algorithm at either sensitivity.

Conclusion: Artificial intelligence-based triage in this simulation demonstrated potential for significant improvement in mammography performance and predicted substantial theoretical workload reduction without any missed cancers.

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.

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.

Invasive lobular carcinoma (ILC) is the second-most common histologic subtype of breast cancer, constituting 5% to 15% of all breast cancers. It is characterized by an infiltrating growth pattern that may decrease detectability on mammography and US. The use of digital breast tomosynthesis (DBT) improves conspicuity of ILC, and sensitivity is 80% to 88% for ILC. Sensitivity of mammography is lower in dense breasts, and breast tomosynthesis has better sensitivity for ILC in dense breasts compared with digital mammography (DM). Screening US identifies additional ILCs even after DBT, with a supplemental cancer detection rate of 0 to 1.2 ILC per 1000 examinations. Thirteen percent of incremental cancers found by screening US are ILCs. Breast MRI has a sensitivity of 93% for ILC. Abbreviated breast MRI also has high sensitivity but may be limited due to delayed enhancement in ILC. Contrast-enhanced mammography has improved sensitivity for ILC compared with DM, with higher specificity than breast MRI. In summary, supplemental screening modalities increase detection of ILC, with MRI demonstrating the highest sensitivity.

Objective: We assess whether mammographic patient-assisted compression (PAC) has an impact on breast compression thickness and patient discomfort compared with technologist-assisted compression (TAC).

Methods: A total of 382 female patients between ages 40 and 90 years undergoing screening mammography from February 2020 to June 2021 were recruited via informational pamphlet to participate in this IRB-approved study. Patients without prior baseline mammograms were excluded. The participating patients were randomly assigned to the PAC or TAC study group. Pre- and postmammogram surveys assessed expected pain and experienced pain, respectively, using a 100-mm visual analogue scale and the State-Trait Anxiety Inventory. Breast compression thickness values from the most recent mammogram were compared with the patient's recent prior mammogram.

Results: Between the 2 groups, there was no significant difference between the expected level of pain prior to the mammogram (P = .97). While both study groups reported a lower level of experienced pain than was expected, the difference was greater for the PAC group (P <.0001). Additionally, the PAC group reported significantly lower experienced pain during mammography compared with the TAC group (P = .014). The correlation of trait/state anxiety scores with pre- and postmammogram pain scores was weak among the groups. Lastly, the mean breast compression thickness values for standard screening mammographic views showed no significant difference in the PAC group when compared with the patient's prior mammogram.

Conclusion: Involving patients in compression reduces their pain independent of the patient's state anxiety during mammography while having no effect on breast compression thickness. Implementing PAC could improve the mammography experience.

Molecular breast imaging (MBI) is a functional imaging modality that utilizes technetium 99m sestamibi radiotracer uptake to evaluate the biology of breast tumors. Molecular breast imaging can be a useful tool for supplemental screening of women with dense breasts, for breast cancer diagnosis and staging, and for evaluation of treatment response in patients with breast cancer undergoing neoadjuvant systemic therapy. In addition, MBI is useful in problem-solving when mammography and US imaging are insufficient to arrive at a definite diagnosis and for patients who cannot undergo breast MRI. Based on the BI-RADS lexicon, a standardized lexicon has been developed to aid radiologists in MBI reporting. In this article, we review MBI equipment, procedures, and lexicon; clinical indications for MBI; and the radiation dose associated with MBI.

Objective: We describe the demographics, clinical presentation, imaging findings, and treatment response among 235 cases of biopsy-proven idiopathic granulomatous mastitis (IGM) at a single institution.

Methods: An institutional review board-approved retrospective search of the breast imaging database was performed to select patients with biopsy-proven IGM between 2017 and 2022. Retrospective review evaluated clinical presentation, imaging findings with US and mammography, and treatment recommendations (antibiotics, nonsteroidal anti-inflammatory drugs [NSAIDs], warm compresses, or observation only). Response to treatment was evaluated on follow-up US. A favorable treatment response was a decrease in size or resolution of disease on follow-up imaging. Statistical analysis using Poisson regression was performed to evaluate the clinical outcomes associated with each treatment.

Results: A total of 235 patients met the selection criteria with a mean age of 38 years (18 to 68). The majority of patients were Hispanic (95%, 223/235). Of all patients, 75.3% (177/235) received treatment (consisting of 1 or any combination of antibiotics, NSAIDs, warm compresses), 24.7% (58/235) were treated with observation, 78.7% (185/235) returned for follow-up imaging, and 21.3% (50/235) were lost to follow-up. Of those with follow-up imaging, disease improvement was seen in 70.3% (102/145) of patients who received treatment compared with 72.5% (29/40) of patients treated by observation alone. Multivariate analysis further showed no difference in clinical outcomes among the treatment of unifocal, multifocal, or recurrent IGM.

Conclusion: Nonsteroidal treatment of IGM showed no significant improvement on follow-up imaging compared to treatment with observation alone in a predominantly Hispanic patient population.