A Tale of Two Hospitals: Effect of Access to Care Through a Safety Net Hospital on Adjuvant Therapy, Imaging Compliance and 5-Year Survival Rates Compared to the University Hospital Served by the Same Breast Cancer Clinical Teams.

RATIONALE AND OBJECTIVES: To compare rates of guideline-concordant care, imaging surveillance, recurrence and survival outcomes between a safety-net (SNH) and tertiary-care University Hospital (UH) served by the same breast cancer clinical teams. MATERIALS AND METHODS: 647 women with newly diagnosed breast cancer treated in affiliated SNH and UH between 11.1.2014 and 3.31.2017 were reviewed. Patient demographics, completion of guideline-concordant adjuvant chemotherapy, radiation and hormonal therapy were recorded. Two multivariable logistic regression models were performed to investigate the effect of hospital and race on cancer stage. Kaplan-Meier log-rank and Cox-regression were used to analyze five-year recurrence-free (RFS) and overall survival (OS) between hospitals and races, (p < 0.05 significant). RESULTS: Patients in SNH were younger (mean SNH 53.2 vs UH 57.9, p < 0.001) and had higher rates of cT3/T4 disease (SNH 19% vs UH 5.5%, p < 0.001). Patients in the UH had higher rates of bilateral mastectomy (SNH 17.6% vs UH 40.1% p < 0.001) while there was no difference in the positive surgical margin rate (SNH 5.0% vs UH 7.6%, p = 0.20), completion of adjuvant radiation (SNH 96.9% vs UH 98.7%, p = 0.2) and endocrine therapy (SNH 60.8% vs UH 66.2%, p = 0.20). SNH patients were less compliant with mammography surveillance (SNH 64.1% vs UH 75.1%, p = 0.02) and adjuvant chemotherapy (SNH 79.1% vs UH 96.3%, p < 0.01). RFS was lower in the SNH (SNH 54 months vs UH 57 months, HR 1.90, 95% CI: 1.18-3.94, p = 0.01) while OS was not significantly different (SNH 90.5% vs UH 94.2%, HR 1.78, 95% CI: 0.97-3.26, p = 0.06). CONCLUSION: In patients experiencing health care disparities, having access to guideline-concordant care through SNH resulted in non-inferior OS to those in tertiary-care UH.

Current status of optoacoustic breast imaging and future trends in clinical application: is it ready for prime time?

Optoacoustic imaging (OAI) is an emerging field with increasing applications in patients and exploratory clinical trials for breast cancer. Optoacoustic imaging (or photoacoustic imaging) employs non-ionizing, laser light to create thermoelastic expansion in tissues and detect the resulting ultrasonic emission. By combining high optical contrast capabilities with the high spatial resolution and anatomic detail of grayscale ultrasound, OAI offers unique opportunities for visualizing biological function of tissues in vivo. Over the past decade, human breast applications of OAI, including benign/malignant mass differentiation, distinguishing cancer molecular subtype, and predicting metastatic potential, have significantly increased. We discuss the current state of optoacoustic breast imaging, as well as future opportunities and clinical application trends. CLINICAL RELEVANCE STATEMENT: Optoacoustic imaging is a novel breast imaging technique that enables the assessment of breast cancer lesions and tumor biology without the risk of ionizing radiation exposure, intravenous contrast, or radionuclide injection. KEY POINTS: • Optoacoustic imaging (OAI) is a safe, non-invasive imaging technique with thriving research and high potential clinical impact. • OAI has been considered a complementary tool to current standard breast imaging techniques. • OAI combines parametric maps of molecules that absorb light and scatter acoustic waves (like hemoglobin, melanin, lipids, and water) with anatomical images, facilitating scalable and real-time molecular evaluation of tissues.

Supplemental Optoacoustic Imaging of Breast Masses: A Cost-Effectiveness Analysis.

RATIONALE AND OBJECTIVES: To evaluate the cost-effectiveness of utilizing supplemental optoacoustic ultrasound (OA/US) versus gray-scale ultrasound (US) alone to differentiate benign and malignant breast masses in a diagnostic setting. MATERIALS AND METHODS: We created a decision-tree model to compare the cost-effectiveness of OA/US and US from the perspective of the US healthcare system. We utilized diagnostic test performance parameters from the PIONEER-01(NCT01943916) clinical trial and cost parameters (USD) from the Truven Health MarketScan Databases. Utility (quality adjusted life year, QALY) values were determined following published patient-reported outcomes. Cost-effectiveness was calculated through incremental cost-effectiveness ratio (USD/QALY, ICER) and net monetary benefit (NMB) in a Markov chain model. Deterministic and probabilistic sensitivity analyses were performed to determine the significance of variation in input parameters. A willingness-to-pay (WTP) threshold of $100,000/QALY was used for the study. RESULTS: OA/US had an estimated cumulative cost of $16,617.36 and the outcome of 16.85 QALYs in the 25-year period. The incremental NMB for OA/US was $1495.36, and the ICER was -$31,715.82/QALY, indicating that supplemental use of OA/US was more cost-effective than US alone. In the deterministic sensitivity analysis, when the cost of OA/US exceeded $1030.61 or the sensitivity of OA/US fell below 79.7%, or the specificity fell below 30.5%, the US alone strategy yielded higher NMB values compared to supplemental OA/US. According to probabilistic sensitivity analysis, OA/US was the better strategy in 98.69% of 10,000 iterations. CONCLUSION: OA/US is more cost-effective than US to differentiate benign or malignant breast masses in the diagnostic setting. It can reduce costs while improving patients' quality of life, primarily by reducing false-positive results with consequent benign biopsies.

Breast Cancer Disparity and Outcomes in Underserved Women.

Women in the United States who continue to face obstacles accessing health care are frequently termed an underserved population. Safety-net health care systems play a crucial role in mitigating health disparities and reducing burdens of disease, such as breast cancer, for underserved women. Disparities in health care are driven by various factors, including race and ethnicity, as well as socioeconomic factors that affect education, employment, housing, insurance status, and access to health care. Underserved women are more likely to be uninsured or underinsured throughout their lifetimes. Hence they have greater difficulty gaining access to breast cancer screening and are less likely to undergo supplemental imaging when needed. Therefore, underserved women often experience significant delays in the diagnosis and treatment of breast cancer, leading to higher mortality rates. Addressing disparities requires a multifaceted approach, with formal care coordination to help at-risk women navigate through screening, diagnosis, and treatment. Mobile mammography units and community outreach programs can be leveraged to increase community access and engagement, as well as improve health literacy with educational initiatives. Radiology-community partnerships, comprised of imaging practices partnered with local businesses, faith-based organizations, homeless shelters, and public service departments, are essential to establish culturally competent breast imaging care, with the goal of equitable access to early diagnosis and contemporary treatment. Published under a CC BY 4.0 license. Test Your Knowledge questions are available in the Online Learning Center. See the invited commentary by Leung in this issue.

Surviving the COVID-19 pandemic: navigating the recovery of breast imaging services in a safety-net hospital.

PURPOSE: The purpose of this study was to determine the impact of COVID-19 on county safety-net breast imaging services and describe the steps taken to actively manage and mitigate delays. METHODS: This was an IRB exempt retrospective review of our county safety-net breast imaging practice analyzed for 4 distinct time periods: (1) "Shut-down period": March 17, 2020 to May 17, 2020; (2) "Phased re-opening": May 18, 2020 to June 30, 2020; (3) "Ramp-up": July 1, 2020 to September 30, 2020; and (4) "Current state": October 1, 2020 to September 30, 2021. These time periods were compared to identical time periods 1 year prior. For "Current state," given that the 1-year prior comparison encompassed the first 3 periods of the pandemic, the identical time period 2 years prior was also compared. RESULTS: Our safety-net practice sustained significant volume losses during the first 3 time periods with a 99% reduction in screening mammography in the shut-down period. Cancers diagnosed decreased by 17% in 2020 (n = 229) compared to 2019 (n = 276). By implementing multiple initiatives that targeted improved access to care, including building community-hospital partnerships and engagement through outreach events and a community education roadshow, we were able to recover and significantly exceed our pandemic screening volumes by 48.1% (27,279 vs 18,419) from October 1, 2020 to September 30, 2021 compared to the identical time period 1 year prior, and exceed our pre-pandemic screening volume by 17.4% (27,279 vs 23,234) compared to the identical time period 2 years prior. CONCLUSION: Through specific community outreach programs and optimized navigation, our safety-net breast imaging practice was able to mitigate the impact of COVID-19 on our patient population by increasing patient engagement and breast imaging services.

Artificial Intelligence in Breast Imaging: Challenges of Integration Into Clinical Practice.

Artificial intelligence (AI) in breast imaging is a rapidly developing field with promising results. Despite the large number of recent publications in this field, unanswered questions have led to limited implementation of AI into daily clinical practice for breast radiologists. This paper provides an overview of the key limitations of AI in breast imaging including, but not limited to, limited numbers of FDA-approved algorithms and annotated data sets with histologic ground truth; concerns surrounding data privacy, security, algorithm transparency, and bias; and ethical issues. Ultimately, the successful implementation of AI into clinical care will require thoughtful action to address these challenges, transparency, and sharing of AI implementation workflows, limitations, and performance metrics within the breast imaging community and other end-users.