Patel, Bhavika K

Abstract Objective To compare the pathology outcomes of image-guided breast biopsies across a multistate health care enterprise by the method of detection (MOD), including the positive predictive value (PPV3) and histological types. Methods Biopsies from October 31, 2017, to July 6, 2023, across 18 imaging sites in 4 states were extracted. Radiologists prospectively assigned MODs, including symptomatic presentations (self-examination, clinical examination) and imaging modalities (mammogram, tomosynthesis, US, MRI, contrast-enhanced mammogram, molecular breast imaging, other imaging). For each MOD, the rates of malignancy and cancer subtypes were calculated. Univariate and multivariate logistic regression analyses were performed. Results In 26 932 biopsies on 22 582 patients (mean age 57.6 years ± 14.2 [SD]; 22 416 women), 6068 (81.1%) cancers were detected by screening, and 1144 (15.3%) were detected by symptoms. Symptomatic presentation MODs had a higher PPV3 (43.0% vs 25.6%; P < .001), larger cancer lesion sizes (x̄ = 27.5 mm vs x̄ = 17.8 mm; P < 001), and a higher association with invasive carcinoma (96.7% vs 76.8%; P <.001) compared with screening imaging-based MODs. Methods of detection with higher PPV3s also tended to have higher rates of invasive carcinoma (Spearman’s ρ = 0.83). Conclusion Compared with breast cancers detected after symptomatic presentations, those identified via image-based screening were smaller and less often invasive, confirming the value of screening in early detection. Positive predictive value and the rate of invasive carcinoma varied in parallel across MODs.

Abstract Objective To assess the performance and cost implications of contrast-enhanced mammography-guided biopsy (CEM Bx). Methods This is a prospective study from May 5, 2021, to April 18, 2022, across 2 U.S. academic centers, evaluating technical success, patient/radiologist feedback, and operational factors of CEM Bx. Inclusion criteria included that patients be 40 years or older and recommended for biopsy with suspicious findings on contrast-enhanced mammography (CEM). Descriptive statistics are reported for clinical outcomes, length of procedure, and user and participant feedback. An estimate of cost was made by comparing general Medicare costs that are being billed for CEM Bx and post-clip mammogram ($475.48) to the Medicare costs of MRI-guided biopsy (MRI Bx) and post-clip placement mammogram ($845.94). Results Eighty-two participants enrolled. Twenty-two were excluded at the time of CEM Bx (22/82, 27%). Sixty participants were included (mean age 57 years, range 33 to 81 years) with 63 suspicious CEM lesions. The malignancy rate was 22% (14/63). A technical success rate of 100% was achieved. Average CEM Bx time was 29% to 33% the average reported MRI-guided breast biopsy times (CEM Bx = 11 minutes; MRI Bx = 33 to 38 minutes), indicating operational efficiencies. A positive opinion of CEM Bx was reported by 78% of radiologists, 93% of technologists, and 98% of patients. The estimated cost for each CEM Bx was ~ 45% lower than MRI Bx. Conclusion CEM Bx demonstrates high success rates and satisfaction scores. CEM Bx resulted in shorter procedure times, enhanced operational efficiency, and ~45% reduction in costs compared with MRI Bx. As advancements continue, we anticipate CEM Bx will offer a cost-efficient and timely option for breast biopsy.

PURPOSE Contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) have shown similar diagnostic performance in detection of breast cancer. Limited CEM data are available for high-risk breast cancer screening. The purpose of the study was to prospectively investigate the efficacy of supplemental screening CEM in elevated risk patients. MATERIALS AND METHODS A prospective, single-institution, institutional review board–approved observational study was conducted in asymptomatic elevated risk women age 35 years or older who had a negative conventional two-dimensional digital breast tomosynthesis screening mammography (MG) and no additional supplemental screening within the prior 12 months. RESULTS Four hundred sixty women were enrolled from February 2019 to April 2021. The median age was 56.8 (range, 35.0-79.2) years; 408 of 460 (88.7%) were mammographically dense. Biopsy revealed benign changes in 22 women (22/37, 59%), high-risk lesions in four women (4/37, 11%), and breast cancer in 11 women (11/37, 30%). Fourteen cancers (10 invasive, tumor size range 4-15 mm, median 9 mm) were diagnosed in 11 women. The overall supplemental cancer detection rate was 23.9 per 1,000 patients, 95% CI (12.0 to 42.4). All cancers were grade 1 or 2, ER+ ERBB2–, and node negative. CEM imaging screening offered high specificity (0.875 [95% CI, 0.844 to 0.906]), high NPV (0.998 [95% CI, 0.993 to 1.000), moderate PPV1 (0.164 [95% CI, 0.076 to 0.253), moderate PPV3 (0.275 [95% CI, 0.137 to 0.413]), and high sensitivity (0.917 [95% CI, 0.760 to 1.000]). At least 1 year of imaging follow-up was available on all patients, and one interval cancer was detected on breast MRI 4 months after negative screening CEM. CONCLUSION A pilot trial demonstrates a supplemental cancer detection rate of 23.9 per 1,000 in women at an elevated risk for breast cancer. Larger, multi-institutional, multiyear CEM trials in patients at elevated risk are needed for validation.

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 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.

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.