Patel, Bhavika

Abstract INTRODUCTION Disparities in breast cancer (BC) screening are observed between Hispanic and non-Hispanic White women and stronger amongst Hispanic women with under- and uninsured statuses.1-2 Risk-based screening has been recommended by professional societies, including American Cancer Society, starting at age 30 and may be an important strategy for reducing disparities.3 The purpose of the study was to characterize whether women had been told their personal risk of BC and to identify differences in characteristics of patients informed of their personal risk. METHODS This is a cross-sectional analysis of data from a supplemental self-reported survey to an NIH-funded randomized trial conducted at a large Federally Qualified Health Center (FQHC) in Arizona (R01 MD09682-05). The survey was sent to a randomized 600 of the 1,356 participants. Between November 2022 and April 2023, 177 women completed the survey (response rate 29.5%). Participants were asked if a doctor or other healthcare provider had ever talked to them about their personal risk for BC. Differences between those who recalled being told their personal risk for BC and those who did not were assessed using Fisher exact test. RESULTS Overall, 66.3% were 50 years or older, 81.8% who speak primarily Spanish, and 64.8% of respondents had less than a high school education. Also, 22.4% reported a family history of BC, 27.4% have been previously recalled for additional imaging, 10.8% have had a history of prior breast biopsy, 59.9% stated having a regular provider, and 53.1% were found to have dense breast category C or D at screening visit. 40% did not report having a regular doctor although all participants received care at an FQHC. 30.5% of respondents stated that a healthcare professional had ever talked to them about their personal risk for BC. There was a trend toward women with a main doctor being more likely to recall being told their personal risk for BC as compared to those without a main doctor (38.5% vs 22.5%, p=0.06). CONCLUSIONS In this cohort of primarily Spanish-speaking Hispanic women who have had mammography screening and written report of their breast density, about a third of respondents recall being informed of their personal BC risk. These observations need to be confirmed in a larger population with greater response rate, but these results suggest that there is a cohort of patients who need different approaches for conveying risk. REFERENCES 1. Jadav, S., Rajan, S. S., Abughosh, S., & Sansgiry, S. S. (2015). The Role of Socioeconomic Status and Health Care Access in Breast Cancer Screening Compliance Among Hispanics. Journal of Public Health Management and Practice, 21(5), 467–476. https://www.jstor.org/stable/48517175 2. American Cancer Society. (2017). Breast cancer facts & figures 2017-2018. Atlanta, GA: American Cancer Society, Inc. 3. ACS Breast Cancer Screening Guidelines. (2022, January 14). https://www.cancer.org/cancer/types/breast-cancer/screening-tests-and-early-detection/american-cancer-society-recommendations-for-the-early-detection-of-breast-cancer.htm Citation Format: Jhenitza P. Raygoza Tapia, Sarah M. Jenkins, Jennifer L. Ridgeway, Aaron D. Norman, Crystal R. Gonzalez, Valentina Hernandez, Bhavika K. Patel, Celine M. Vachon, Jessica D. Austin. Communicating breast cancer risk to Spanish-speaking Hispanic women [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C126.

The NCCN Guidelines for Breast Cancer Screening and Diagnosis provide health care providers with a practical, consistent framework for screening and evaluating a spectrum of clinical presentations and breast lesions. The NCCN Breast Cancer Screening and Diagnosis Panel is composed of a multidisciplinary team of experts in the field, including representation from medical oncology, gynecologic oncology, surgical oncology, internal medicine, family practice, preventive medicine, pathology, diagnostic and interventional radiology, as well as patient advocacy. The NCCN Breast Cancer Screening and Diagnosis Panel meets at least annually to review emerging data and comments from reviewers within their institutions to guide updates to existing recommendations. These NCCN Guidelines Insights summarize the panel’s decision-making and discussion surrounding the most recent updates to the guideline’s screening recommendations.

PURPOSE Selection of appropriate adjuvant therapy to ultimately reduce the risk of breast cancer (BC) recurrence is a challenge for medical oncologists. Several automated risk prediction models have been developed using retrospective clinical data and have evolved significantly over the years in terms of predictors of recurrence, data usage, and predictive techniques (statistical/machine learning [ML]). METHODS Following PRISMA guidelines, we performed a systematic literature review of the aforementioned statistical and ML models published between January 2008 and December 2022 through searching five digital databases—PubMed, ScienceDirect, Scopus, Cochrane, and Web of Science. The comprehensive search yielded a total of 163 papers and after a screening process focusing on papers that dealt exclusively with statistical/ML methods, only 23 papers were deemed appropriate for further analysis. We benchmarked the studies on the basis of development, evaluation metrics, and validation strategy with an added emphasis on racial diversity of patients included in the studies. RESULTS In total, 30.4% of the included studies use statistical techniques, while 69.6% are ML-based. Among these, traditional ML models (support vector machines, decision tree, logistic regression, and naïve Bayes) are the most frequently used (26.1%) along with deep learning (26.1%). Deep learning and ensemble learning provide the most accurate predictions (AUC = 0.94 each). CONCLUSION ML-based prediction models exhibit outstanding performance, yet their practical applicability might be hindered by limited interpretability and reduced generalization. Moreover, predictive models for BC recurrence often focus on limited variables related to tumor, treatment, molecular, and clinical features. Imbalanced classes and the lack of open-source data sets impede model development and validation. Furthermore, existing models predominantly overlook African and Middle Eastern populations, as they are trained and validated mainly on Caucasian and Asian patients.

Abstract Contrast-enhanced mammography (CEM) has emerged as an important new technology in breast imaging. It can demonstrate a number of imaging artifacts that have the potential to limit interpretation by either obscuring or potentially mimicking disease. Commonly encountered artifacts on CEM include patient motion artifacts (ripple and misregistration), pectoral highlighting artifact, breast implant artifact, halo artifact, corrugation artifact, cloudy fat artifact, contrast artifacts (retention and contamination), skin artifacts (skin line enhancement and skin overexposure), and skin lesions. Skin lesions may demonstrate a variety of imaging appearances and have both benign and malignant etiologies. It is important that the technologist, radiologist, and physicist be aware of potential artifacts and skin enhancement on CEM that may affect interpretation and understand their causes and potential solutions.

Abstract 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.

Abstract Mammographic breast density (MBD) has been shown to be a strong, independent risk factor for breast cancer (BC) irrespective of race/ethnicity. Given the risk association of MBD and its potential to mask tumors on a mammogram, state and federal laws have mandated that women receive information regarding their personal MBD in their mammography reports. However, concerns have been raised regarding the impact of MBD notification on patient anxiety, especially written information for women who experience health disparities such as racial/ethnic minorities, lower health literacy, limited English proficiency and lower socioeconomic status. We performed a randomized controlled clinical trial to examine the impact of three different written and interpersonal approaches to MBD notification on patient anxiety, BC worry, and self-perceived BC risk, among Latinas receiving routine mammography screening at a federally qualified medical center (FQHC). We hypothesized that interpersonal education would reduce anxiety and worry, relative to the written notifications alone. The study was performed at the Baseline Clinic of Mountain Park Health Center, a FQHC in Phoenix, AZ. Women between ages 40 and 74 years presenting for screening mammogram were eligible. After providing signed informed consent, participants were randomized equally to usual care (UC- mailed notification letter); enhanced care (notification letter and MBD educational brochure designed for this study): interpersonal care (notification letter, brochure, promotora education via telephone). A stratified block randomization procedure was used with age > 50 years (yes vs no), ethnicity (Hispanic vs non-Hispanic), and language preference (Spanish vs. English) as strata. Study participants completed surveys at the time of enrollment/pre-intervention (T0), at two weeks to six months after intervention was delivered (T1), and about one year after randomization (T2). Anxiety state was measured using the state anxiety subscale of the State-Trait Anxiety Inventory scale (STAI-S) (range from 20-80). BC worry was probed using the question: “How frequently do you worry about getting breast cancer someday”. The self-perceived lifetime risk of BC was rated between 0% (no chance of BC) to 100% (definitely will get BC). The proportion of participants with an increase or persistence of higher level for each outcome between time points was compared between the three study groups. The study was approved by the Mayo Clinic Institutional Review Board. 1332 Latina women were enrolled and randomized between October 2016 and October 2019. All completed the baseline (T0) survey, 928 completed T1, 632 completed T2, and 516 completed both T1 and T2 surveys. At study entry, majority of the participants were young (64.1% between age 40-49 years), had no family history of breast cancer (81.0%), had less than high school education (68.8%), and were married or partnered (67.8%). At T0, the mean (SD) anxiety STAI score was 32.5 (11.1); 51.8% with moderate or severe anxiety (score > 30). With regard to BC worry, 41.3% reported worrying “sometimes”, “often”, or “almost all the time”. Further, 25.4% reported a self-perceived lifetime risk of developing BC of >10% while only 6.6% had a Gail model estimated lifetime risk score of >10%. There was no significant difference in changes in anxiety, BC worry or self-perceived risk from T0 to either T1 or T2 surveys between the intervention groups. In summary, this study found high levels of baseline anxiety and BC worry (despite the majority being 40-49 years old and having no family history of BC) which persisted regardless of how notification and education were delivered. Future work is needed to improve the understanding of factors that could lower anxiety and BC worry and improve risk perception in this population. Citation Format: Karthik Ghosh, Sarah Jenkins, Jennifer Ridgeway, Jessica D. Austin, Bijan Borah, Bhavika K. Patel, Deborah Rhodes, Aaron Norman, Edna P. Ramos, Matt Jewett, Crystal Gonzalez, Valentina Hernandez, Davinder Singh, Vera Suman, Celine Vachon. Breast Density Notification: Impact on anxiety, breast cancer worry, and self-perceived risk among Latinas at a federally qualified medical center. [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 P6-05-38.