Breast Cancer Screening Among Medically Underserved Women in New Mexico: Potential for Lower Recall Rates with Digital Breast Tomosynthesis.

Introduction: Digital breast tomosynthesis (DBT) may decrease recall rates (RRs) and improve positive predictive values (PPVs) and cancer detection rates (CDRs) versus full-field digital mammography (FFDM). The value of DBT has not been assessed in New Mexico's rural and minority population. Objectives of this study were to compare RRs, CDRs, and PPVs using FFDM+DBT versus FFDM in screening mammograms at the University of New Mexico between 2013 and 2016 and to qualitatively evaluate patient decision-making regarding DBT. Materials and Methods: RRs, CDRs, and PPVs with 95% confidence intervals and relative risk were calculated from 35,147 mammograms. The association between relative risk and mammography approach was tested using Pearson's chi-square test. Twenty women undergoing screening were interviewed for qualitative evaluation of decision-making. Results: From 2013 to 2016, RRs were 8.4% and 11.1% for FFDM+DBT and FFDM, respectively. The difference in RRs became more pronounced with time. No significant difference was observed in PPVs or CDRs. Qualitative interviews revealed that the majority had limited prior knowledge of DBT and relied on provider recommendations. Conclusion: In New Mexico women undergoing screening mammography, a 30% relative risk reduction in RRs was observed with FFDM+DBT. Qualitative interviews suggest that women are aware of and receptive to DBT, assuming adequate educational support. Clinical Trials.gov ID: NCT03979729.

Predicting breast cancer response to neoadjuvant chemotherapy based on tumor vascular features in needle biopsies.

In clinical breast cancer intervention, selection of the optimal treatment protocol based on predictive biomarkers remains an elusive goal. Here, we present a modeling tool to predict the likelihood of breast cancer response to neoadjuvant chemotherapy using patient specific tumor vasculature biomarkers. A semi-automated analysis was implemented and performed on 3990 histological images from 48 patients, with 10-208 images analyzed for each patient. We applied a histology-based model to resected primary breast cancer tumors (n = 30), and then evaluated a cohort of patients (n = 18) undergoing neoadjuvant chemotherapy, collecting pre- and post-treatment pathology specimens and MRI data. We found that core biopsy samples can be used with acceptable accuracy (r = 0.76) to determine histological parameters representative of the whole tissue region. Analysis of model histology parameters obtained from tumor vasculature measurements, specifically diffusion distance divided by radius of drug source (L/rb) and blood volume fraction (BVF), provides a statistically significant separation of patients obtaining a pathologic complete response (pCR) from those that do not (Student's t-test; P < 0.05). With this model, it is feasible to evaluate primary breast tumor vasculature biomarkers in a patient specific manner, thereby allowing a precision approach to breast cancer treatment.