Moreira, Matheus José Barbosa

160 Background: In the United States, racial/ethnic disparities in adoption of breast cancer evidence-based practices (EBPs) persist along the cancer care continuum. This scoping review applied an implementation science lens to describe the current evidence base for interventions to reduce disparities, synthesizing evidence on effective interventions while highlighting gaps in the literature. Methods: A formalized search strategy identified USA-based interventional studies aiming to improve adoption of an EBP along the breast cancer care continuum among racially/ethnically minoritized patients. Relevant articles from 1/2010 - 8/2022 (Medline, Embase, Cochrane, and Scopus) were included. For each article, two reviewers screened and extracted data, with a third resolving discrepancies. The PRISMA-ScR checklist and JBO Manual for Evidence Synthesis framework were followed. Results: Our search strategy yielded 45,215 articles. After applying inclusion/exclusion criteria, 59 articles were retained, targeting screening (85%, 50 of 59), treatment (12%, 7 of 59), and survivorship (5%, 3 of 59) (2 targeted multiple areas). No articles targeted end-of-life care. Of these, 49% (29 of 59) were randomized controlled trials (RCTs). Among 24 screening RCTs, the most common interventions—and fraction with statistically significant improved screening uptake—were patient navigation (100%, 11 of 11) and patient education (80%, 4 of 5). Among 4 treatment RCTs, 3 targeted adjuvant chemotherapy uptake. Of these, 1 had significant improvements and used patient navigation. The 2 studies without significant improvements used a patient tracking tool/alert system or patient education. Among 2 survivorship RCTs, one used email interventions, resulting in significantly improved physical activity and dietary health. A second used a nurse-facilitated internet support group, resulting in significantly improved physical/psychological symptoms. Early to mid-stage implementation outcomes (acceptability, adoption, appropriateness, feasibility, or fidelity) were commonly measured (81%, 48 of 59). No study measured later stage implementation outcomes (sustainability or costs). Conclusions: In multiple studies, patient navigation and education improved breast cancer screening among racially/ethnically minoritized populations. In one study, patient navigation improved completion of adjuvant treatment. Despite these studies, patient navigation is not reimbursed for cancer screening, and adoption has not been widespread. Future studies should focus on evaluating and improving later stage implementation outcomes (sustainability and costs). Based on these results, insurance reimbursement mechanisms for screening-related navigation may be warranted. Future breast cancer care delivery disparities research should also be expanded to include treatment, survivorship, and end-of-life care.

e24049 Background: Vulvo-Vaginal Atrophy (VVA) or Genitourinary Syndrome of Menopause (GSM) is a common condition among breast cancer (BC) patients, especially those undergoing antiestrogen therapy. Despite being an option in refractory cases, the safety of hormonal treatment remains uncertain in this population. The aim of this study was to review the safety and serum estrogen levels of hormonal therapy in patients with BC history presenting with VVA symptoms. Methods: Pubmed, Embase, and Cochrane were searched for studies comparing different hormonal treatment options for VVA in breast cancer survivors. Statistical analysis was performed using a random effects model and heterogeneity using Cochran's Q-statistic and the I2 index. Results: We included 17 studies, of which 5 were randomized controlled trials (RCTs). Treatment modalities included in this study were topical vaginal estradiol and estriol preparations, vaginally applied testosterone, DHEA, and ospemifene. We found that, among patients treated with the estriol and estradiol preparations, there was an average increase of 7.67pg/ml (SMD 7.67 pg/mL; 95% CI -1.00, 16.35; p < 0.001). Analysis of the testosterone group found temporary peaks of serum estradiol levels, but one study showed persistent elevation above normal postmenopausal levels. One study with prasterone revealed no elevation of serum estradiol concentration. One study with ospemifene demonstrated no increase in the risk of BC recurrence. Conclusions: In conclusion, among treatments available for BC survivors, low-dose vaginal estrogen showed the smallest changes in serum estradiol levels and had the most evidence, but safety remains unclear, especially for patients on aromatase inhibitors. Alternative treatments such as ospemifene need more data supporting safety and efficacy. These results suggest that concerns related to cancer recurrence should keep aiming for the lowest possible concentration.

Abstract Introduction: Deep learning (DL) has shown promising results for mammographic breast cancer diagnosis. However, the impact of artificial intelligence (AI) in the screening process have not yet been fully addressed in terms of workload reduction, which has potential to decrease healthcare disparities. Radiologists are tasked with overwhelming volumes of screening mammograms, particularly in medically underserved areas. Therefore, when applied to workflow improvement, AI might be a tool to reduce healthcare disparities. The purpose of this systematic review and meta-analysis was to assess if AI-based triaging of breast cancer screening mammograms could reduce the radiologist’s workload with non-inferior sensitivity. Methods: PubMed, EMBASE, Cochrane Central and Web of Science databases were systematically searched for studies that evaluated AI algorithms on computer-aided triage of breast cancer screening mammograms. We extracted data from homogenous studies and performed a proportion meta-analysis with random-effects model to verify the radiologist’s workload reduction and the software’s sensitivity. Results: A total of 14 studies were systematically selected. Three studies using the same commercially available DL algorithm were included in the meta-analysis, with 156852 examinations evaluated at the threshold of 8. The radiologist’s workload decreased by 68.3% (95%CI 0.655-0.711, I² = 98.76%, p &lt; 0.001), with a sensitivity of 93.1% (95%CI 0.882-0.979, I² = 83.86%, p = 0.002). Conclusion: Our findings suggest that DL computer-aided triage of breast cancer screening mammograms significantly reduces the radiologist’s workload with high sensitivity. Although AI’s implementation remains complex and heterogeneous, it is a promising tool to optimize healthcare resources with a potential large impact in low resource settings that struggle with workforce shortage. Citation Format: Debora Xavier, Isabele A. Miyawaki, Carlos Alberto Campello Jorge, Matheus Jose Barbosa Moreira, Bruno M. Carvalho, Felipe Batalini. Artificial intelligence-based triaging of breast cancer screening mammograms and radiologist workload reduction: a systematic review and meta-analysis [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-04-06.