Glucocorticoid-induced TNF receptor family related protein (GITR) is present on many different cell types. Previous studies have shown that in vivo administration of an anti-GITR agonist mAb (DTA-1) inhibits regulatory T cells (Treg)-dependent suppression and enhances T cell responses. In this study, we show that administration of DTA-1 induces >85% tumor rejection in mice challenged with B16 melanoma. Rejection requires CD4+, CD8+, and NK1.1+ cells and is dependent on IFN-γ and Fas ligand and independent of perforin. Depletion of Treg via anti-CD25 treatment does not induce B16 rejection, whereas 100% of the mice depleted of CD25+ cells and treated with DTA-1 reject tumors, indicating a predominant role of GITR on effector T cell costimulation rather than on Treg modulation. T cells isolated from DTA-1-treated mice challenged with B16 are specific against B16 and several melanoma differentiation Ags. These mice develop memory against B16, and a small proportion of them develop mild hypopigmentation. Consistent with previous studies showing that GITR stimulation increases Treg proliferation in vitro, we found in our model that GITR stimulation expanded the absolute number of FoxP3+ cells in vivo. Thus, we conclude that overall, GITR stimulation overcomes self-tolerance/ignorance and enhances T cell-mediated antitumor activity with minimal autoimmunity.

• BREAST IMAGING M ammography is the only imaging modality shown to reduce breast cancer mortality in randomized trials (1-8). Despite its benefits, challenges include variation in interpretive performance and the scarcity of specialized radiologists (9,10). A recent report of mammography screening performance in U.S. community practice demonstrated that radiologists' diagnostic performance ranged from 66.7% to 98.6% for sensitivity and from 71.2% to 96.9% for specificity (11). False-negative examinations can result in delayed diagnosis, and false-positive examinations can lead to unnecessary procedures, impacting both patient experience and overall costs. Moreover, the ability of specialized radiologists to serve the global population of women eligible for breast cancer screening is limited by workflow inefficiencies (12,13). Both technologic and workflow solutions have been proposed to improve radiologist interpretive performance and efficiency. Computer-aided detection (CAD), which detects and marks suspicious findings on mammograms, aims to improve radiologist sensitivity. Although traditional approaches have not demonstrated improved radiologist performance in sensitivity or specificity in clinical practice (14-16), a more recent deep learning approach to CAD has shown promise in improving sensitivity in a reader study (17). However, this does not address limitations in radiologist specificity or efficiency. Double reading (ie, having two radiologists interpret the same mammogram) has also been implemented to improve radiologist performance. Although some studies demonstrate slight improvements in sensitivity, double reading worsens workflow efficiency and increases false-positive examination results (18,19). We hypothesized that a deep learning model trained to triage mammograms as cancer free can improve radiologist efficiency and specificity without harming sensitivity. Specifically, we trained a model to predict cancer

Risk factors for LR after BCS include age<40 years, node positivity, ER negativity, and absence of adjuvant radiation therapy. Patients younger than age 40 years are at increased risk of LR after BCS.

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While nearly half of women at high lifetime breast cancer risk undergo routine screening mammography at a facility with on-site breast MRI availability, supplemental breast MRI remains widely underutilized among those who may benefit from earlier cancer detection. Future studies should evaluate whether other enabling factors such as formal risk assessment and patient awareness of high lifetime breast cancer risk can mitigate the underutilization of supplemental screening breast MRI.

With the advent of new screening technologies, including digital breast tomosynthesis, screening ultrasound, and breast magnetic resonance imaging, there is growing concern that existing disparities among traditionally underserved populations will worsen. These newer screening modalities purport improved cancer detection over mammography alone but are not offered at all screening facilities and often require a larger co-pay or out-of-pocket expense. Thus, the potential for worsening disparities with regard to access and appropriate utilization of supplemental screening technologies exists. Currently, there is a dearth of literature on the topic of health disparities related to access and the use of supplemental breast cancer screening and their impact on outcomes. Identifying and addressing explanatory factors for persistent and potentially worsening disparities remain a central focus of efforts to improve equity in breast cancer care. Therefore, this paper provides an overview of factors that may contribute to present and future disparities in breast cancer screening and outcomes, and explores specific relevant topics requiring greater research efforts as more personalized, multimodality breast cancer screening approaches are adopted into clinical practice.