A novel machine learning model for breast cancer detection using mammogram images.

The most fatal disease affecting women worldwide now is breast cancer. Early detection of breast cancer enhances the likelihood of a full recovery and lowers mortality. Based on medical imaging, researchers from all around the world are developing breast cancer screening technologies. Due to their rapid progress, deep learning algorithms have caught the interest of many in the field of medical imaging. This research proposes a novel method in mammogram image feature extraction with classification and optimization using machine learning in breast cancer detection. The input image has been processed for noise removal, smoothening, and normalization. The input image features were extracted using probabilistic principal component analysis for detecting the presence of tumors in mammogram images. The extracted tumor region is classified using the Naïve Bayes classifier and transfer integrated convolution neural networks. The classified output has been optimized using firefly binary grey optimization and metaheuristic moth flame lion optimization. The experimental analysis has been carried out in terms of different parameters based on datasets. The proposed framework used an ensemble model for breast cancer that made use of the proposed Bayes + FBGO and TCNN + MMFLO classifier and optimizer for diverse mammography image datasets. The INbreast dataset was evaluated using the proposed Bayes + FBGO and TCNN + MMFLO classifiers, which achieved 95% and 98% accuracy, respectively.

Effectiveness of a Health Educational Program in Enhancing Breast Cancer Knowledge Among Women in Rural Karnataka, South India.

INTRODUCTION AND AIM: Breast cancer is one of the significant causes of mortality in India, ranking second only to cervical cancer among women. Annually, the country has witnessed the detection of 200,000 new cases, with 60% identified in the early stages. This study aimed to assess the effectiveness of a health education intervention program designed to enhance knowledge about breast cancer among women in rural Karnataka. MATERIALS AND METHODS: A descriptive study design was employed and a total of 320 women were selected through multi-stage sampling. The educational intervention involved a PowerPoint presentation by the investigator, which was followed by group discussions that culminated with plenary sessions for clarifying the doubts of respondents. At the end of every educational session, pre-designed, pre-tested, and validated questionnaires, comprising a mix of structured and semi-structured questions, were completed by the respondents as part of the post-test.  Results: Among the participants, 44.7% were educated up to the primary level, a majority (64.1%) were employed, and most (90.3%) were married. Additionally, 56.9% reported a monthly income below 3000 Indian rupees (₹), with the majority (86.3%) falling below the poverty line (BPL) category. A statistically significant improvement (p = 0.0001) in knowledge related to breast health, breast self-examination, clinical breast examination, and mammography was observed in the post-intervention phase when compared to the pre-test. 86.2% of the respondents showed an increase in knowledge level about breast health (either from poor to moderate or from moderate to good) and the practice of breast self-examination increased from 4.7% (pre-test) to 60.3% (post-test).  Conclusion: The study demonstrated a significant enhancement in women's knowledge levels after implementing the health education intervention program. These findings underscore the importance of health education strategies in raising awareness of lifestyle diseases, particularly breast cancer, among women.

Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence.

Background: Mammography is the modality of choice for breast cancer screening. However, some cases of breast cancer have been diagnosed through ultrasonography alone with no or benign findings on mammography (hereby referred to as non-visibles). Therefore, this study aimed to identify factors that indicate the possibility of non-visibles based on the mammary gland content ratio estimated using artificial intelligence (AI) by patient age and compressed breast thickness (CBT). Methods: We used AI previously developed by us to estimate the mammary gland content ratio and quantitatively analyze 26,232 controls and 150 non-visibles. First, we evaluated divergence trends between controls and non-visibles based on the average estimated mammary gland content ratio to ensure the importance of analysis by age and CBT. Next, we evaluated the possibility that mammary gland content ratio ≥50% groups affect the divergence between controls and non-visibles to specifically identify factors that indicate the possibility of non-visibles. The images were classified into two groups for the estimated mammary gland content ratios with a threshold of 50%, and logistic regression analysis was performed between controls and non-visibles. Results: The average estimated mammary gland content ratio was significantly higher in non-visibles than in controls when the overall sample, the patient age was ≥40 years and the CBT was ≥40 mm (p < 0.05). The differences in the average estimated mammary gland content ratios in the controls and non-visibles for the overall sample was 7.54%, the differences in patients aged 40-49, 50-59, and ≥60 years were 6.20%, 7.48%, and 4.78%, respectively, and the differences in those with a CBT of 40-49, 50-59, and ≥60 mm were 6.67%, 9.71%, and 16.13%, respectively. In evaluating mammary gland content ratio ≥50% groups, we also found positive correlations for non-visibles when controls were used as the baseline for the overall sample, in patients aged 40-59 years, and in those with a CBT ≥40 mm (p < 0.05). The corresponding odds ratios were ≥2.20, with a maximum value of 4.36. Conclusion: The study findings highlight an estimated mammary gland content ratio of ≥50% in patients aged 40-59 years or in those with ≥40 mm CBT could be indicative factors for non-visibles.

Clinical outcomes of screening and diagnostic mammography in a limited resource healthcare system.

INTRODUCTION: Breast cancer is a significant public health concern in Jordan. It is the most common cancer among Jordanian women. Despite its high incidence and advanced stage at time of diagnosis, the uptake of breast cancer screening in Jordan is low. This study aims to compare clinical outcomes of both screening and diagnostic mammogram among women in Jordan. METHODS: A retrospective cohort of 1005 women who underwent mammography in breast imaging unit in a tertiary hospital in Jordan. It aimed to investigate outcomes of screening and diagnostic mammography. recall rates, clinical manifestations and cancer rates were investigated. RESULTS: A total of 1005 participants were involved and divided into screening group (n = 634) and diagnostic group (n = 371). Women in the diagnostic group were more likely to be younger, premenopausal, smokers with higher BMI. Among the screening group, 22.3% were labeled with abnormal mammogram, 26% recalled for ultrasound, 46 patients underwent tissue biopsy and a total of 12 patients had a diagnosis of breast carcinoma. Among the diagnostic group, the most commonly reported symptoms were a feeling of breast mass, mastalgia and nipple discharge. Abnormal mammogram was reported in 50.4% of women, a complementary ultrasound was performed for 205 patients. A diagnostic Tru-cut biopsy for 144 patients and diagnostic excisional biopsy for 17 patients were performed. A total of 131 had a diagnosis of carcinoma. CONCLUSION: With the high possibility of identifying a carcinoma in mammography among symptomatic women and low uptake of screening mammogram, efforts to increase awareness and improve access to screening services are crucial in reducing the burden of breast cancer in Jordan.

Primary MALT lymphoma of the breast: pathological and radiological characteristics.

PURPOSE: Primary Mucosa-associated lymphoid tissue (MALT) lymphoma is a rare diagnosis in the breast, and clinical diagnosis based on radiological features is often challenging. This study aimed to evaluate the clinicopathological, and radiological characteristics of the patients diagnosed with primary breast MALT lymphoma. METHODS: This study examined 18 cases of primary MALT lymphoma of the breast diagnosed at a single tertiary center between January 2002 to December 2020. Medical charts, radiological imaging and original pathology slides were reviewed for each case. RESULTS: All cases were female (gender assigned at birth) and presented with a palpable mass or an incidental imaging finding. Imaging presentation ranged from mammographic asymmetries, circumscribed masses, and ultrasound masses lacking suspicious features. Seventeen cases were biopsied under ultrasound; one received a diagnostic excision biopsy. Microscopic examination of the breast specimens demonstrated atypical small lymphocyte infiltration with plasmacytoid differentiation and rare lymphoepithelial lesions. Immunohistochemistry was performed in all cases and established the diagnosis. Most patients were treated with radiotherapy, and only three were treated with chemotherapy. The median follow-up period was 4 years and 7.5 months, and all patients were alive at the last follow-up. CONCLUSION: Primary MALT breast lymphomas are usually indolent and non-systemic, and local radiotherapy may effectively alleviate local symptoms. Radiological findings show overlap with benign morphological features, which can delay the diagnosis of this unusual etiology. Although further studies involving a larger cohort could help establish the clinical and radiological characteristics of primary breast MALT lymphomas, pathology remains the primary method of diagnosis. TRIAL REGISTRATION NUMBER: University Health Network Ethics Committee (CAPCR/UHN REB number 19-5844), retrospectively registered.

[Segmentation of Mass in Mammogram Using Gaze Search Patterns].

PURPOSE: It is very difficult for a radiologist to correctly detect small lesions and lesions hidden on dense breast tissue on a mammogram. Therefore, recently, computer-aided detection (CAD) systems have been widely used to assist radiologists in interpreting images. Thus, in this study, we aimed to segment mass on the mammogram with high accuracy by using focus images obtained from a eye-tracking device. METHODS: We obtained focus images for two mammography expert radiologists and 19 mammography technologists on 8 abnormal and 8a normal mammograms published by the DDSM. Next, the auto-encoder, Pix2Pix, and UNIT learned the relationship between the actual mammogram and the focus image, and generated the focus image for the unknown mammogram. Finally, we segmented regions of mass on mammogram using the U-Net for each focus image generated by the auto-encoder, Pix2Pix, and UNIT. RESULTS: The dice coefficient in the UNIT was 0.64±0.14. The dice coefficient in the UNIT was higher than that in the auto-encoder and Pix2Pix, and there was a statistically significant difference (p<0.05). The dice coefficient of the proposed method, which combines the focus images generated by the UNIT and the original mammogram, was 0.66±0.15, which is equivalent to the method using the original mammogram. CONCLUSION: In the future, it will be necessary to increase the number of cases and further improve the segmentation.

Deep learning for computer-aided abnormalities classification in digital mammogram: A data-centric perspective.

Breast cancer is the most common type of cancer in women, and early abnormality detection using mammography can significantly improve breast cancer survival rates. Diverse datasets are required to improve the training and validation of deep learning (DL) systems for autonomous breast cancer diagnosis. However, only a small number of mammography datasets are publicly available. This constraint has created challenges when comparing different DL models using the same dataset. The primary contribution of this study is the comprehensive description of a selection of currently available public mammography datasets. The information available on publicly accessible datasets is summarized and their usability reviewed to enable more effective models to be developed for breast cancer detection and to improve understanding of existing models trained using these datasets. This study aims to bridge the existing knowledge gap by offering researchers and practitioners a valuable resource to develop and assess DL models in breast cancer diagnosis.

Mammographic assessment of breast density as a tool for predicting the response to neoadjuvant therapy in breast cancer patients.

Background and aims: Breast cancer (BC) is the most frequently diagnosed cancer and the leading cause of cancer-related death among women worldwide. For locally advanced diseases and high-risk tumors, neoadjuvant therapy (NAT) is the treatment of choice. Some studies show that mammographic density (MD) tumor margins and the presence of microcalcifications play a prognostic role in BC patients. Hence, the objective of this retrospective study was to assess if MD could predict the response to NAT among different molecular subtypes of BC patients undergoing NAT at The "Prof. Dr I. Chiricuta" Oncology Institute of Cluj-Napoca, Romania (IOCN). Furthermore, the association between MD, tumor margins and the presence of microcalcifications with clinico-pathological data was analyzed. Methods: Eighty-four breast cancer patients diagnosed and treated at IOCN were included in this study. The morphological characteristics of the tumors were framed according to the BIRADS lexicon. The presence or absence of microcalcifications was also assessed. First, the significance of associations between breast density, margins and microcalcifications and clinico-pathological parameters of the patients were tested with Fisher or Fisher-Freeman-Halton Exact Test. Next, using multinomial logistic regression, we modelled the associations between the pathological response measured by Miller Payne and Residual cancer burden (RCB) systems and the BI-RADS. Variables having significant univariate tests were selected as candidates for the multivariable analysis (adjusted model). Results: Breast densities were significantly associated with the age of the patients (p=0.01), number of positive lymph nodes (p=0.037), margins (p=0.002) and combined categories of Miller-Payne (p=0.034) and RCB pathological response (p=0.021). Margins was significantly associated with ki67 proliferation index (p=0.029), estrogen receptor (ER) (p=0.007), progesterone receptor (PR) (p=0.019), molecular subtype (p<0.001) and the number of clinically observed positive lymph nodes at diagnosis (p=0.019). Conclusions: In our cohort, BC patients with lower MD had higher odds of achieving pCR following NAT, suggesting the role of MD as a clinical prognostic marker. Larger multicenter studies are warranted to validate the prognostic value of MD, which could aid in patients stratification based on their likelihood to respond to NAT.

Quantitative evaluation of Saliency-Based Explainable artificial intelligence (XAI) methods in Deep Learning-Based mammogram analysis.

BACKGROUND: Explainable Artificial Intelligence (XAI) is prominent in the diagnostics of opaque deep learning (DL) models, especially in medical imaging. Saliency methods are commonly used, yet there's a lack of quantitative evidence regarding their performance. OBJECTIVES: To quantitatively evaluate the performance of widely utilized saliency XAI methods in the task of breast cancer detection on mammograms. METHODS: Three radiologists drew ground-truth boxes on a balanced mammogram dataset of women (n = 1496 cancer-positive and negative scans) from three centers. A modified, pre-trained DL model was employed for breast cancer detection, using MLO and CC images. Saliency XAI methods, including Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++, and Eigen-CAM, were evaluated. We utilized the Pointing Game to assess these methods, determining if the maximum value of a saliency map aligned with the bounding boxes, representing the ratio of correctly identified lesions among all cancer patients, with a value ranging from 0 to 1. RESULTS: The development sample included 2,244 women (75%), with the remaining 748 women (25%) in the testing set for unbiased XAI evaluation. The model's recall, precision, accuracy, and F1-Score in identifying cancer in the testing set were 69%, 88%, 80%, and 0.77, respectively. The Pointing Game Scores for Grad-CAM, Grad-CAM++, and Eigen-CAM were 0.41, 0.30, and 0.35 in women with cancer and marginally increased to 0.41, 0.31, and 0.36 when considering only true-positive samples. CONCLUSIONS: While saliency-based methods provide some degree of explainability, they frequently fall short in delineating how DL models arrive at decisions in a considerable number of instances.

Digital mammography dataset for breast cancer diagnosis research (DMID) with breast mass segmentation analysis.

Purpose:In the last two decades, computer-aided detection and diagnosis (CAD) systems have been created to help radiologists discover and diagnose lesions observed on breast imaging tests. These systems can serve as a second opinion tool for the radiologist. However, developing algorithms for identifying and diagnosing breast lesions relies heavily on mammographic datasets. Many existing databases do not consider all the needs necessary for research and study, such as mammographic masks, radiology reports, breast composition, etc. This paper aims to introduce and describe a new mammographic database. Methods:The proposed dataset comprises mammograms with several lesions, such as masses, calcifications, architectural distortions, and asymmetries. In addition, a radiologist report is provided, describing the details of the breast, such as breast density, description of abnormality present, condition of the skin, nipple and pectoral muscles, etc., for each mammogram. Results:We present results of commonly used segmentation framework trained on our proposed dataset. We used information regarding the class of abnormalities (benign or malignant) and breast tissue density provided with each mammogram to analyze the segmentation model's performance concerning these parameters. Conclusion:The presented dataset provides diverse mammogram images to develop and train models for breast cancer diagnosis applications.

Continuity of Radiologists Between Diagnostic Breast Imaging and Image-Guided Breast Biopsy: Impact on Patient-Reported Biopsy Morbidity Experiences.

OBJECTIVE: To determine whether continuity of care between diagnostic breast imaging and subsequent image-guided breast biopsy could reduce patient-reported biopsy-related morbidity. METHODS: This was a prospective, pragmatically randomized, 2-arm health utilities analysis of 200 women undergoing diagnostic breast imaging followed by US- or stereotactic-guided breast biopsy at a single quaternary care center from September 3, 2019, to April 10, 2023. Breast biopsy-naive women with a BI-RADS 4 or 5 finding at diagnostic imaging were randomly scheduled for the typically first available biopsy appointment. One day after biopsy, enrolled patients were administered the Testing Morbidities Index (TMI). The primary outcome was the difference in TMI summary utility scores in patients who did vs did not have the same radiologist perform diagnostic imaging and biopsy. RESULTS: Response rates were 63% (100/159) for the different radiologist cohort and 71% (100/140) for the same radiologist cohort; all respondents answered all questions in both arms. Mean time to biopsy was 7 ± 6 days and 10 ± 9 days, and the number of participating radiologists was 11 and 18, respectively. There was no difference in individual measured domains (pain, fear, or anxiety before procedure; pain, embarrassment, fear, or anxiety during procedure; mental or physical impact after procedure; all P >.00625) or in overall patient morbidity (0.83 [95% CI, 0.81-0.85] vs 0.82 [95% CI: 0.80-0.84], P = .66). CONCLUSION: Continuity of care between diagnostic breast imaging and image-guided breast biopsy did not affect morbidity associated with breast biopsy, suggesting that patients should be scheduled for the soonest available biopsy appointment rather than waiting for the same radiologist.

Unsupervised feature correlation model to predict breast abnormal variation maps in longitudinal mammograms.

Breast cancer continues to be a significant cause of mortality among women globally. Timely identification and precise diagnosis of breast abnormalities are critical for enhancing patient prognosis. In this study, we focus on improving the early detection and accurate diagnosis of breast abnormalities, which is crucial for improving patient outcomes and reducing the mortality rate of breast cancer. To address the limitations of traditional screening methods, a novel unsupervised feature correlation network was developed to predict maps indicating breast abnormal variations using longitudinal 2D mammograms. The proposed model utilizes the reconstruction process of current year and prior year mammograms to extract tissue from different areas and analyze the differences between them to identify abnormal variations that may indicate the presence of cancer. The model incorporates a feature correlation module, an attention suppression gate, and a breast abnormality detection module, all working together to improve prediction accuracy. The proposed model not only provides breast abnormal variation maps but also distinguishes between normal and cancer mammograms, making it more advanced compared to the state-of-the-art baseline models. The results of the study show that the proposed model outperforms the baseline models in terms of Accuracy, Sensitivity, Specificity, Dice score, and cancer detection rate.

Male breast abscess: A rare entity.

Breast abscess in males is a rare condition, which accounts for 1%-3% of all documented breast diseases. Males with certain risk factors may develop a breast abscess. The ultrasonographic, mammographic, and pathological characteristics of this case will be highlighted in the report. A 51-year-old morbidly obese Saudi male who is a 160-pack-years smoker presented to our surgical clinic complaining of a right breast mass that presented a long time ago and was changing in size. The mass was painless until 5 days prior to presentation. On physical examination, a firm nonmobile 3 × 4 cm mass was felt at 10-12-o'clock, 1 cm away from the nipple. A bilateral X-ray mammogram and ultrasound were performed with fine needle aspiration and culture. The mammogram of the right breast showed a well-circumscribed subareolar mass with equal density, and it was also associated with overlying skin thickening and relative breast parenchymal edema. The fine needle aspiration grossly showed yellowish-green turbid content followed by turbid blood. The anaerobic culture results showed the gram-positive cocci, Finegoldia Magna. The patient was then instructed to take an antibiotic accordingly and return after 1 week. Fine needle aspiration and culture were performed again after antibiotics and grossly showed 2-3 cc of pus without any growth in culture. Male breast disorders are typically benign, with gynecomastia being the most prevalent, and malignancy being the most serious despite its rarity. Breast abscesses are a challenging clinical condition, and radiologists have a pivotal role in evaluation and follow-up of these lesions.

Preventive service utilization among adults with cystic fibrosis covered by private insurance is comparable to the general population.

BACKGROUND: As the life expectancy of the cystic fibrosis (CF) population is lengthening with modulator therapies, diligent age-appropriate screening and preventive care are increasingly vital for long-term health and wellbeing. METHODS: We performed a retrospective analysis comparing rates of receiving age- and sex-appropriate preventive services by commercially insured adult people with CF (PwCF) and adults without CF from the general population (GP) via the Truven Health MarketScan database (2012-2018). RESULTS: We captured 25,369 adults with CF and 488,534 adults from the GP in the United States. Comparing these groups, we found that 43% versus 39% received an annual preventive visit, 28% versus 28% were screened for chlamydia, 38% versus 37% received pap smears every 3 years (21-29-year-old females), 33% versus 31% received pap smears every 5 years (30-64-year-old females), 55% versus 44% received mammograms, 23% versus 21% received colonoscopies, and 21% versus 20% received dyslipidemia screening (all screening rates expressed per 100 person-years). In age-stratified analysis, 18-27-year-old PwCF had a lower rate of annual preventive visits compared to adults in the same age group of the GP (27% versus 42%). CONCLUSIONS: We discovered a comparable-to-superior rate of preventive service utilization in adults with CF relative to the GP, except in young adulthood from 18-27 years. Our findings establish the importance of meeting the primary care needs of adults with CF and call for development of strategies to improve preventive service delivery to young adults.

Breast Cancer Screening and BI-RADS Scoring Trends Before and During the COVID-19 Pandemic in an Academic Safety-Net Hospital.

BACKGROUND: Little is known about how the COVID-19 pandemic affected screening mammography rates and Breast Imaging Reporting and Data Systems (BI-RADS) categorizations within populations facing social and economic inequities. Our study seeks to compare trends in breast cancer screening and BI-RADS assessments in an academic safety-net patient population before and during the COVID-19 pandemic. PATIENTS AND METHODS: Our single-center retrospective study evaluated women ≥ 18 years old with no known breast cancer diagnosis who received breast cancer screening from March 2019-September 2020. The screening BI-RADS score, completion of recommended diagnostic imaging, and diagnostic BI-RADS scores were compared between the pre-COVID-19 era (from 1 March 2019 to 19 March 2020) and COVID-19 era (from 20 March 2020 to 30 September 2020). RESULTS: Among the 11,798 patients identified, screened patients were younger (median age 57 versus 59 years, p < 0.001) and more likely covered by private insurance (35.9% versus 32.3%, p < 0.001) during the COVID-19 era compared with the pre-COVID-19 era. During the pandemic, there was an increase in screening mammograms categorized as BI-RADS 0 compared with the pre-COVID-19 era (20% versus 14.5%, p < 0.0001). There was no statistically significant difference in rates of completion of diagnostic imaging (81.6% versus 85.4%, p = 0.764) or assignment of suspicious BI-RADS scores (BI-RADS 4-5; 79.9% versus 80.8%, p = 0.762) between the two eras. CONCLUSIONS: Although more patients were recommended to undergo diagnostic imaging during the pandemic, there were no significant differences in race, completion of diagnostic imaging, or proportions of mammograms categorized as suspicious between the two time periods. These findings likely reflect efforts to maintain equitable care among diverse racial groups served by our safety-net hospital.

Efficient adversarial debiasing with concept activation vector - Medical image case-studies.

BACKGROUND: A major hurdle for the real time deployment of the AI models is ensuring trustworthiness of these models for the unseen population. More often than not, these complex models are black boxes in which promising results are generated. However, when scrutinized, these models begin to reveal implicit biases during the decision making, particularly for the minority subgroups. METHOD: We develop an efficient adversarial de-biasing approach with partial learning by incorporating the existing concept activation vectors (CAV) methodology, to reduce racial disparities while preserving the performance of the targeted task. CAV is originally a model interpretability technique which we adopted to identify convolution layers responsible for learning race and only fine-tune up to that layer instead of fine-tuning the complete network, limiting the drop in performance RESULTS:: The methodology has been evaluated on two independent medical image case-studies - chest X-ray and mammograms, and we also performed external validation on a different racial population. On the external datasets for the chest X-ray use-case, debiased models (averaged AUC 0.87 ) outperformed the baseline convolution models (averaged AUC 0.57 ) as well as the models trained with the popular fine-tuning strategy (averaged AUC 0.81). Moreover, the mammogram models is debiased using a single dataset (white, black and Asian) and improved the performance on an external datasets (averaged AUC 0.8 to 0.86 ) with completely different population (primarily Hispanic patients). CONCLUSION: In this study, we demonstrated that the adversarial models trained only with internal data performed equally or often outperformed the standard fine-tuning strategy with data from an external setting. The adversarial training approach described can be applied regardless of predictor's model architecture, as long as the convolution model is trained using a gradient-based method. We release the training code with academic open-source license - https://github.com/ramon349/JBI2023_TCAV_debiasing.

Retained Biopsy Site Markers After Breast Lesion Surgical Resection: Associations With Residual Malignancy.

BACKGROUND. Biopsy site markers (BSMs) placed during image-guided core needle biopsy (CNB) are typically targeted for surgical excision, along with the breast imaging abnormality. Retained BSMs raise concern of incomplete resection of the breast abnormality. OBJECTIVE. The purpose of our study was to assess the frequency of residual malignancy in patients with retained BSMs identified on the initial mammography performed after breast lesion surgical excision. METHODS. This retrospective study included 30 patients (median age, 59 years) who underwent surgical resection between August 2015 and April 2022 of a borderline, high-risk, or malignant breast lesion after CNB and technically adequate preoperative image-guided localization, in whom the initial postoperative mammography report described a retained nonmigrated BSM. EMR data were extracted. The index pathology from CNB and initial surgical resection was classified as malignant or nonmalignant. The presence of residual malignancy after initial surgical resection required pathologic confirmation from subsequent tissue sampling; the absence of residual malignancy required 2 years of benign imaging follow-up. RESULTS. Thirteen specimen radiographs were interpreted intraoperatively by a surgeon with later radiologist interpretation, and 17 underwent real-time radiologist interpretation. Eighteen patients had malignant index pathology from the initially resected lesion. The frequency of residual malignancy on subsequent follow-up after initial surgical resection was higher in patients with malignant than nonmalignant index pathology (39% [7/18] vs 0% [0/12], respectively; p = .02). Among patients with malignant index pathology, the frequency of residual malignancy was higher in those without, than with, malignancy in the initial surgical specimen (80% [4/5] vs 23% [3/13]; p = .047). Also in these patients, the frequency of a positive interpretation of the initial postoperative mammography (BI-RADS category 4 or 6) was not significantly different between those with and without residual malignancy (57% [4/7] vs 55% [6/11]; p > .99). CONCLUSION. Patients with retained BSMs associated with malignant index lesions are at substantial risk of having residual malignancy. Initial postoperative mammography is not sufficient for excluding residual malignancy. CLINICAL IMPACT. Retained BSMs associated with index malignancy should be considered suspicious for residual malignancy. In this scenario, timely additional tissue sampling targeting the retained BSM is warranted, given the greater-than-2% chance of malignancy. Active surveillance is a reasonable management strategy in patients with retained BSMs from nonmalignant index lesions.

Rate and Timeliness of Diagnostic Evaluation and Biopsy After Recall From Screening Mammography in the National Mammography Database.

OBJECTIVE: To describe the rate and timeliness of diagnostic resolution after an abnormal screening mammogram in the ACR's National Mammography Database. METHODS: Abnormal screening mammograms (BI-RADS 0 assessment) in the National Mammography Database from January 1, 2008, to December 31, 2021, were retrospectively identified. The rates and timeliness of follow-up with diagnostic evaluation and biopsy were assessed and compared across patient and facility demographics. RESULTS: Among the 2,874,310 screening mammograms reported as abnormal, follow-up was documented in 66.4% (n = 1,909,326). Lower follow-up rates were observed in younger women (59.4% in women < 30 years, 63.2% in women 30-39 years), Black (57.4%) and American Indian (59.5%) women, and women with no breast cancer family history (63.0%). The overall median time to diagnostic evaluation was 9 days. Longer median diagnostic evaluation time was noted in Black (14 days), other or mixed race (14 days), and Hispanic women (13 days). Of the 318,977 recalled screening mammograms recommended for biopsy, 238,556 (74.8%) biopsies were documented. Lower biopsy rates were noted in older women (71.5% in women aged ≥80) and Black (71.5%) and American Indian (52.2%) women. The overall median time from diagnostic evaluation to biopsy was 21 days. Longer median biopsy time was noted in older (23 days aged ≥80), Black (25 days), mixed or other race (26 days), and Hispanic women (23 days), and rural (24 days) or community hospital affiliated facilities (22 days). DISCUSSION: There is variability in the rates and timeliness of diagnostic evaluation and biopsy in women with abnormal screening mammogram. Subsets of women and facilities could benefit from targeted interventions to promote timely diagnostic resolution and biopsy after an abnormal screening mammogram.