Journal of Breast Imaging最新文献

Objective: To analyze recent trends in U.S. breast cancer mortality rates by age group and race and ethnicity.

Methods: This retrospective analysis of female breast cancer mortality rates used National Center for Health Statistics data from 1990 to 2022 for all women, by age group, and by race or ethnicity. Joinpoint analysis assessed trends in breast cancer mortality rates.

Results: Breast cancer mortality rates for women 20 to 39 years old decreased 2.8% per year from 1999 to 2010 but showed no decline from 2010 to 2022 (annual percentage change [APC], -0.01; P = .98). For women of ages 40 to 74 years, breast cancer mortality rates decreased 1.7% to 3.9% per year from 1990 to 2022 (P <.001); a decline was found for all cohorts in this age group except Asian women. For women ≥75 years of age, breast cancer mortality rates declined significantly from 1993 to 2013 (APC, -1.26; P = .01) but showed no evidence of decline from 2013 to 2022 (APC, -0.2; P = .24). Across all ages, breast cancer mortality rates declined for White and Black women but not for Asian, Hispanic, and Native American women. Asian women ≥75 years of age had significantly increasing mortality rates (APC, 0.73; P <.001). For 2004 to 2022, breast cancer mortality rates were 39% higher in Black women than White women and varied strongly by age group: 104% for ages 20 to 39 years, 51% for ages 40 to 74 years, and 13% for ages ≥75 years.

Conclusion: Female breast cancer mortality rates have stopped declining in women <40 years of age and >74 years of age. The higher mortality rates in Black women compared with White women are age dependent and substantially higher in younger women.

The National Cancer Institute-funded Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer mathematical models have been increasingly utilized by policymakers to address breast cancer screening policy decisions and influence clinical practice. These well-established and validated models have a successful track record of use in collaborations spanning over 2 decades. While mathematical modeling is a valuable approach to translate short-term screening performance data into long-term breast cancer outcomes, it is inherently complex and requires numerous inputs to approximate the impacts of breast cancer screening. This review article describes the 6 independently developed CISNET breast cancer models, with a particular focus on how they represent breast cancer screening and estimate the contribution of screening to breast cancer mortality reduction and improvements in life expectancy. We also describe differences in structures and assumptions across the models and how variation in model results can highlight areas of uncertainty. Finally, we offer insight into how the results generated by the models can be used to aid decision-making regarding breast cancer screening policy.

Receiving feedback can sometimes be difficult and uncomfortable but is an essential component of professional development in breast imaging. Trainees have an opportunity to leverage feedback in breast imaging by incorporating self-assessments, real-world patient outcomes, procedural feedback, patient interactions, and available audit data to build confidence and competency in residency and fellowship. We present strategies for seeking and receiving feedback with a growth mindset, including specific scenarios in breast imaging where trainees can incorporate feedback and maximize learning potential.

Radiologists face a range of challenges to maximize the life-saving benefits of screening mammography, including pressure to maintain accuracy, manage heavy workloads, and minimize the risk of fatigue and burnout. This review provides targeted strategies to address these challenges and, ultimately, to improve interpretive performance of screening mammography. Workflow optimizations, including offline vs online and batched vs nonbatched interpretation, interrupted vs uninterrupted reading, and the importance of comparing current mammograms with prior examinations will be explored. Each strategy has strengths, weaknesses, and logistical challenges that must be tailored to the individual practice environment. Moreover, as breast radiologists contend with increasingly busy and hectic working conditions, practical solutions to protect reading environments and minimize distractions, such as the "sterile cockpit" approach, will be described. Additionally, breast radiologists are at greater risk for fatigue and burnout due to rising clinic volumes and an inadequate workforce. Optimizing the approach to reading screens is critical to helping breast imaging radiologists maintain and maximize the benefits of screening mammography, ensure the best outcomes for our patients, and maintain radiologist job satisfaction.

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.