Patel, Bhavika

Abstract Purpose Accurately predicting the clinical breast cancer subtypes could be extremely helpful for radiologists, pathologists, surgeons, and clinicians and inform future treatment prediction algorithms. Therefore, we evaluate and compare the accuracy of radiomic features extracted from contrast enhanced mammography (CEM) and magnetic resonance imaging (MRI) scans to make predictions to subtypes of breast cancer. Methods This HIPAA-compliant prospective single institution study was approved by the local institutional review board with written informed consent. Women with breast tumors 2 cm or larger underwent dynamic contrast-enhanced MRI and/or CEM for surgical staging. Semi-manual regions of interest were drawn by radiologist using Cancer Imaging Phenomics Toolkit (CaPTk). Radiomic features were obtained using PyRadiomics and MR- and CEM- based classification models were built on a low-dimensional representation of the features obtained via kernel principal component analysis. We subscribed to an ensemble tree-based learning approach called extremely randomized trees (ERT) to predict cancer subtypes captured via immunohistochemistry markers. Results For MRI analysis, 124 women with newly diagnosed breast cancer were included in the analysis comprising 49 HR+HER2-, 37 HR+HER2+, 11 HR-HER2+, and 27 HR-HER2- cases. For CEM analysis, models were built using data from 170 female patients including 74 HR+HER2-, 41 HR+HER2+, 14 HR-HER2+, and 43 HR-HER2-. CEM based model resulted in accuracies of 55%, 72%, 88%, and 71% respectively for HR+HER2-, HR+HER2+, HR-HER2+, and HR-HER2- whereas MRI based model alone led to accuracies of 54%, 62%, 89%, and 76% respectively for HR+HER2-, HR+HER2+, HR-HER2+, and HR-HER2-. Conclusions Radiomic features extracted from CEM and MR were strong predictors of breast cancer subtypes with CEM-based radiomic features performing slightly better, though not statistically significantly better (p = 0.82), than its MRI counterpart.

Abstract Objective Establish a radiologist-run consultation clinic to review breast density and supplemental screening exams (SSEs) directly with patients in response to breast density reporting laws. Methods Breast radiologists opened and staffed a clinic for formal patient consultations regarding breast density and SSEs. An IRB-approved questionnaire assessed patient knowledge of breast density, SSEs, and encounter satisfaction. Comparative statistical analyses were performed on knowledge-based questions. Results From February 2019 to February 2021, 294 reimbursable consultations were performed with 215 patients completing pre- and post-consultation questionnaires (survey response rate, 73%). Median patient age was 58 years (range, 34–86 years) and 9% (19/210) had a personal history of breast cancer. An increase in patient knowledge of breast density and SSEs was observed as follows: breast density categories (9% correct pre-consultation (20/215), 86% correct post-consultation (185/215), P < 0.001), dense breast effects on cancer risk (39% correct pre-consultation (83/215), 84% post-consultation (180/215)), mammogram sensitivity (90% correct pre-consultation (193/215), 94% post-consultation (201/215)), and increased cancer detection with SSEs (82% correct pre-consultation (177/215), 95% post-consultation (205/215)) (P < 0.001). Post-consultation, 96% (200/209) were satisfied with the usefulness of information, 89% (186/209) strongly agreed they had sufficient knowledge of SSEs, and 81% (167/205) agreed they would like future opportunities to meet with a breast radiologist. Conclusion A consultation clinic staffed by breast radiologists focused on breast density and supplemental breast cancer screening can provide personalized patient counseling, engage patients in shared decision making, assist referring clinicians, and support high quality patient-centered care.

91 Background: Most screening clinical trials are not representative of the populations susceptible to disparities in breast cancer outcomes. Strategies are needed to address known structural barriers to clinical trial participation. We partnered with a federally qualified health center to provide no-cost ride-share services as a strategy to facilitate enrollment of uninsured, Hispanic women in Maricopa County, Arizona into a national trial offering no-cost mammography screening. Objective: This pilot survey study explored the feasibility and experiences of uninsured, Hispanic women with a no-cost ride share program as part of the National Cancer Institute funded ECOG-ACRIN clinical trial, TMIST, studying mammography screening at the Mayo Clinic Arizona in Phoenix. Methods: Participants completed a validated survey by phone and interview-style. Survey items assessed women’s use of and experiences with the ride share service including barriers, benefits, and if the ride share service facilitated their participation in the screening trial. Descriptive statistics were calculated using SAS. Results: A total of 30 women completed the survey (response rate = 30/33). All women first-time Mayo Clinic trial participants and 90% lived at least 30 minutes away from trial facility. The mean age of women was X years, 90% had a high school degree or less, and 97% were Spanish speaking. Most women (73.7%) said that travel distance was a barrier to participation in prior clinical trials. A large proportion of women (73%) reported using ride share services for this trial and all agreed that the ride service made it easier for them to attend their trial visit. Nearly 1/3 (64%) said that they were uncertain or would not have been able to participate without the ride-share as an option. Among women using the ride share service, the main benefits of the program included at-home pick up and the service being of no-cost. Other benefits included flexible timing (86%) and one-on-one service (86%). The primary barrier of the program was the driver not speaking the dominant native language (27.3%). Conclusions: Despite lengthy travel distances, women reported overwhelming positive experiences with the no-cost ride share program and agreed that the service helped facilitate their participation in the clinical trial. Thus, no-cost ride share programs are a promising and feasible strategy to reduce barriers to clinical trial participation among historically underserved populations. Future studies should continue to explore the benefits and sustainability of ride-share programs, including benefits to clinical trial retention.

Background: Understanding the accuracy of a woman's perceived breast cancer risk can enhance shared decision-making about breast cancer screening through provider and patient discussion. We aim to report and compare women's perceived lifetime breast cancer risk to calculated lifetime breast cancer risk. Methods: Women presenting to Mayo Clinic in Arizona and Minnesota in July 2016 completed a survey assessing their perceived breast cancer risk. Lifetime Gail risk scores were calculated from questions pertaining to health history and were then compared with perceived breast cancer risk. Results: A total of 550 predominantly white, married, and well-educated (≥college) women completed surveys. Using lifetime Gail risk scores, 5.6% were classified as high risk (>20% lifetime risk), 7.7% were classified as intermediate risk (15%–20%), and 86.6% were classified as average risk (<15%). Of the 27 women who were classified as high risk, 18 (66.7%) underestimated their risk and of the 37 women who were intermediate risk, 12 (32.4%) underestimated risk. Women more likely to underestimate their risk had a reported history of an abnormal mammogram and at least one or more relative with a history of breast cancer. Surveyed women tended to overestimate risk 4.3 (130/30) times as often as they underestimated risk. Conclusion: In a group of predominantly white, educated, and married cohort of women, there was a large portion of women in the elevated risk groups who underestimated risk. Specific aspects of medical history were associated with underestimation including a history of abnormal mammogram and family history of breast cancer. Overall, in our sample, more women overestimated than underestimated risk.

BackgroundThe objective of this randomized trial was to evaluate the short‐term effect of bilingual written and interpersonal education regarding mammographic breast density (MBD).MethodsLatinas aged 40 to 74 years who were presenting for screening mammography were recruited and randomized 1:1:1 to receive a letter with their mammogram and MBD results (usual care [UC]), a letter plus a brochure (enhanced care [ENH]), or a letter plus a brochure and telephonic promotora education (interpersonal care [INT]). Surveys were administered at enrollment (T0) and 2 weeks to 6 months after intervention delivery (T1). Differences were assessed with χ2, Kruskal‐Wallis, and McNemar tests and pairwise comparisons as appropriate. INT metrics and audio recordings were analyzed with descriptive statistics and qualitative content analysis.ResultsBetween October 2016 and October 2019, 943 of 1108 Latina participants (85%) completed both surveys. At T1, INT participants were more likely (P < .001) to report seeing their MBD results in the letter (70.2%) than UC (53.1%) or ENH participants (55.1%). The percentage of INT women who reported speaking with a provider about MBD (29.0%) was significantly greater (P < .001) than the percentage of UC (14.7%) or ENH participants (15.6%). All groups saw significant (P < .001) but nondifferential improvements in their knowledge of MBD as a masking and risk factor. In the INT group, the promotora delivered education to 77.1% of the 446 participants randomized to INT and answered questions at 28.3% of the encounters for an average of $4.70 per participant.ConclusionsAmong Latinas in a low‐resource setting, MBD knowledge may increase with written or interpersonal education, but with modest investment, interpersonal education may better improve MBD awareness and prompt patient‐provider discussions.

Abstract Purpose:Quantify in vivo biomechanical tissue properties in various breast densities and in normal risk and high risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association between breast biomechanical properties and cancer risk.Methods: Patients with normal risk or high risk of breast cancer underent 3.0 T breast MR imaging and elastography. Breast parenchymal enhancement (BPE), density (from most recent mammogram), stiffness, elasticity, and viscosity were recorded. Within each breast density group (non-dense versus dense), stiffness, elasticity, and viscosity were compared across risk groups (normal versus high). A multivariable logistic regression model was used to evaluate whether the MRE parameters (separately for stiffness, elasticity, and viscosity) predicted risk status after controlling for clinical factors.Results: 50 normal risk and 86 high risk patients were included. Risk groups were similar on age, density, and menopausal status. Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high risk patients (N = 55) compared to normal risk patients (N = 34; all p < 0.001). Stiffness remained a significant predictor of risk status (OR=4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, BPE, age, and menopausal status. Similar results were seen for elasticity and viscosity.Conclusion: A structurally-based, quantitative biomarker of tissue stiffness obtained from MRE is associated with differences in breast cancer risk in dense breasts. Tissue stiffness could provide a novel prognostic marker to help identify high risk women with dense breasts who would benefit from increased surveillance and/or risk reduction measures.

10541 Background: Biomechanical tissue properties may vary in the breasts of patients at elevated risk for breast cancer. We aim to quantify in vivo biomechanical tissue properties in various breast densities and in both normal risk and high risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association of biomechanical properties of the breast with cancer risk. Methods: In this IRB–approved prospective single-institution study, we recruited two groups of women differing by breast cancer risk to undergo a 3.0 T dynamic contrast enhanced MRI/MRE of the breast. Low-average risk women were defined as having no personal or significant family history of breast cancer, no prior high risk breast biopsies and a negative mammography within 12 months. High-risk breast cancer patients were recruited from those patients who underwent standard of care breast MR. Within each breast density group (non-dense versus dense), two-sample t-tests were used to compare breast stiffness, elasticity, and viscosity across risk groups (low-average vs high). Results: There were 50 low-average risk and 86 high-risk patients recruited to the study. The risk groups were similar on age (mean age = 55.6 and 53.6 years), density (68% vs. 64% dense breasts) and menopausal status (66.0% vs. 69.8%). Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high risk patients ( N = 55) compared to low-average risk patients ( N = 34; all p < 0.001). In the multivariate logistic regression model, breast stiffness remained a significant predictor of risk status (OR=4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, MRI BPE, age, and menopausal status. Similar results were seen for breast elasticity (OR=4.88, 95% CI [2.08, 11.43]) and viscosity (OR=11.49, 95% CI [1.15, 114.89]). Conclusions: Structurally-based, quantitative biomarker of tissue stiffness obtained from global 3D breast MRE is associated with differences in breast cancer risk in dense breasts. As such, tissue stiffness could provide a novel prognostic marker to help identify the subset of high-risk women with dense breasts who would benefit from increased surveillance.[Table: see text]