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Accurate characterization of microcalcifications (MCs) in 2D digital mammography is a necessary step toward reducing the diagnostic uncertainty associated with the callback of indeterminate MCs. Quantitative analysis of MCs can better identify MCs with a higher likelihood of ductal carcinoma in situ or invasive cancer. However, automated identification and segmentation of MCs remain challenging with high false positive rates. We present a two-stage multiscale approach to MC segmentation in 2D full-field digital mammograms (FFDMs) and diagnostic magnification views. Candidate objects are first delineated using blob detection and Hessian analysis. A regression convolutional network, trained to output a function with a higher response near MCs, chooses the objects which constitute actual MCs. The method was trained and validated on 435 screening and diagnostic FFDMs from two separate datasets. We then used our approach to segment MCs on magnification views of 248 cases with amorphous MCs. We modeled the extracted features using gradient tree boosting to classify each case as benign or malignant. Compared to state-of-the-art comparison methods, our approach achieved superior mean intersection over the union (0.670 ± 0.121 per image versus 0.524 ± 0.034 per image), intersection over the union per MC object (0.607 ± 0.250 versus 0.363 ± 0.278) and true positive rate of 0.744 versus 0.581 at 0.4 false positive detections per square centimeter. Features generated using our approach outperformed the comparison method (0.763 versus 0.710 AUC) in distinguishing amorphous calcifications as benign or malignant.

Background It is important to establish screening mammography performance benchmarks for quality improvement efforts. Purpose To establish performance benchmarks for digital breast tomosynthesis (DBT) screening and evaluate performance trends over time in U.S. community practice. Materials and Methods In this retrospective study, DBT screening examinations were collected from five Breast Cancer Surveillance Consortium (BCSC) registries between 2011 and 2018. Performance measures included abnormal interpretation rate (AIR), cancer detection rate (CDR), sensitivity, specificity, and false-negative rate (FNR) and were calculated based on the American College of Radiology Breast Imaging Reporting and Data System, fifth edition, and compared with concurrent BCSC DM screening examinations, previously published BCSC and National Mammography Database benchmarks, and expert opinion acceptable performance ranges. Benchmarks were derived from the distribution of performance measures across radiologists (n = 84 or n = 73 depending on metric) and were presented as percentiles. Results A total of 896 101 women undergoing 2 301 766 screening examinations (458 175 DBT examinations [median age, 58 years; age range, 18-111 years] and 1 843 591 DM examinations [median age, 58 years; age range, 18-109 years]) were included in this study. DBT screening performance measures were as follows: AIR, 8.3% (95% CI: 7.5, 9.3); CDR per 1000 screens, 5.8 (95% CI: 5.4, 6.1); sensitivity, 87.4% (95% CI: 85.2, 89.4); specificity, 92.2% (95% CI: 91.3, 93.0); and FNR per 1000 screens, 0.8 (95% CI: 0.7, 1.0). When compared with BCSC DM screening examinations from the same time period and previously published BCSC and National Mammography Database performance benchmarks, all performance measures were higher for DBT except sensitivity and FNR, which were similar to concurrent and prior DM performance measures. The following proportions of radiologists achieved acceptable performance ranges with DBT: 97.6% for CDR, 91.8% for sensitivity, 75.0% for AIR, and 74.0% for specificity. Conclusion In U.S. community practice, large proportions of radiologists met acceptable performance ranges for screening performance metrics with DBT. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Lee and Moy in this issue.

ObjectiveWomen with serious mental illness are more likely to be diagnosed as having late-stage breast cancer than women without serious mental illness, suggesting a disparity in screening mammography. This study aimed to compare screening mammography rates in a nationally representative sample of Medicaid beneficiaries with and without schizophrenia.MethodsMedicaid Analytic eXtract files, 2007-2012, were used to identify a cohort of women ages 40-64 with schizophrenia who were eligible for Medicaid but not Medicare (N=87,572 in 2007 and N=114,341 in 2012) and a cohort without schizophrenia, frequency-matched by age, race-ethnicity, and state (N=97,003 in 2007 and N=126,461 in 2012). Annual screening mammography rates were calculated and adjusted for demographic characteristics and comorbid conditions. Multivariable logistic regression was used to estimate the association between beneficiary characteristics and screening mammography rates.ResultsIn 2012, 27.2% of women with schizophrenia completed screening mammography, compared with 26.8% of the control cohort. In the schizophrenia cohort, American Indian/Alaskan Native women had significantly lower odds of receiving mammography (OR=0.82, p=0.02) than White women, whereas Hispanic/Latina women had higher odds (OR=1.16, p

BACKGROUND: Machine learning (ML) approaches facilitate risk prediction model development using high-dimensional predictors and higher-order interactions at the cost of model interpretability and transparency. We compared the relative predictive performance of statistical and ML models to guide modeling strategy selection for surveillance mammography outcomes in women with a personal history of breast cancer (PHBC). METHODS: We cross-validated seven risk prediction models for two surveillance outcomes, failure (breast cancer within 12 months of a negative surveillance mammogram) and benefit (surveillance-detected breast cancer). We included 9,447 mammograms (495 failures, 1,414 benefits, and 7,538 nonevents) from years 1996 to 2017 using a 1:4 matched case-control samples of women with PHBC in the Breast Cancer Surveillance Consortium. We assessed model performance of conventional regression, regularized regressions (LASSO and elastic-net), and ML methods (random forests and gradient boosting machines) by evaluating their calibration and, among well-calibrated models, comparing the area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI). RESULTS: LASSO and elastic-net consistently provided well-calibrated predicted risks for surveillance failure and benefit. The AUCs of LASSO and elastic-net were both 0.63 (95% CI, 0.60-0.66) for surveillance failure and 0.66 (95% CI, 0.64-0.68) for surveillance benefit, the highest among well-calibrated models. CONCLUSIONS: For predicting breast cancer surveillance mammography outcomes, regularized regression outperformed other modeling approaches and balanced the trade-off between model flexibility and interpretability. IMPACT: Regularized regression may be preferred for developing risk prediction models in other contexts with rare outcomes, similar training sample sizes, and low-dimensional features.

IntroductionThe development and optimization of novel diagnostic imaging prototypes heavily rely on experimental work. In radiology, this experimental work involves the use of phantoms. When testing novel techniques to demonstrate their advantages, anthropomorphic phantoms are utilized. The aim of this study was to investigate seven materials for 3D printing to replicate the radiological properties of breast lesions.MethodsTo achieve this objective, we utilized three fused filament fabrication materials, namely, polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and polyethylene terephthalate glycol (PET-G), along with resins such as White v4 Resin, Flexible 80A v1 Resin, Model v2 Resin, and Wax40 v1 Resin, to 3D print seven irregularly shaped lesions. These lesions were used to prepare a set of seven physical phantoms, each filled with either water or liquid paraffin, and one of the printed lesions. The phantoms were then scanned using a mammography unit at 28 kVp. Additionally, six computational breast phantoms, replicating the shape of the physical phantoms, were generated. These computational models were assigned the attenuating properties of various breast tissues, including glandular tissue, adipose tissue, skin, and lesions. Mammography images were generated under the same experimental conditions as the physical scans. Both the simulated and experimental images were evaluated for their contrast-to-noise ratio (CNR) and contrast (C).DiscussionThe results indicated that the studied resins and filament-based materials are all suitable for replicating breast lesions. Among these, PLA and White v4 Resin exhibited the densest formations and can effectively approximate breast lesions that are slightly less attenuating than glandular tissue, while ABS and Flexible 80A v1 Resin were the least dense and can represent fat-containing breast lesions. The remaining materials provided good approximations for malignant lesions. These materials can be utilized for constructing phantoms for experimental work, rendering the model a valuable tool for optimizing mammography protocols, ensuring quality control of mammography X-ray equipment, and aiding in the diagnosis and assessment of breast cancer.

Mammography is a radiation medical exam, which makes detection of mammary microcalcifications possible at an early stage. The dose received by the patient’s breast is known as the average glandular dose, which is considered a quality control indicator. Estimation of this parameter implies knowing the effective energy of the x-ray beam delivered. This is the case when thermoluminescent dosimetry is the method of choice. The algorithm developed to discriminate the x-ray energy the mammography patient has been exposed to while undergoing routine procedures, applies two thermoluminescent dosimeters, one of them filtered by a 1 mm thick aluminum layer. The effective energy of the x-ray beam and the correction factor are obtained by knowing the relation between the filtered and non-filtered dosemeters readout. This algorithm was then used to estimate the average glandular dose following the IAEA TRS 457 protocol. The dose values computed were compared with the international diagnostic reference levels suggested by the technical literature.

This data article presents a comprehensive dataset comprising breast cancer images collected from patients, encompassing two distinct sets: one from individuals diagnosed with breast cancer and another from those without the condition. Expert physicians carefully select, verify, and categorize the dataset to guarantee its quality and dependability for use in research and teaching. The dataset, which originates from Sulaymaniyah, Iraq, provides a distinctive viewpoint on the frequency and features of breast cancer in the area. This dataset offers a wealth of information for developing and testing deep learning algorithms for identifying breast cancer, with 745 original images and 9,685 augmented images. The addition of augmented X-rays to the dataset increases its adaptability for algorithm development and instructional projects. This dataset holds immense potential for advancing medical research, aiding in the development of innovative diagnostic tools, and fostering educational opportunities for medical students interested in breast cancer detection and diagnosis.

Background and purpose: We have recently demonstrated that screen-detected invasive breast cancers had more favourable tumour characteristics than non-screen-detected. The objective of the study was to analyse differences in breast cancer treatment between screen-detected and non-screen-detected cases by age at diagnosis, with and without adjustment for tumour (T) and nodal (N) status, within a nationwide, population-based mammography screening programme utilising register data. Material and methods: Data spanning 2008–2017 were collected from the National Quality Register for Breast Cancer. Multivariable logistic regression analysis was used to estimate odds ratios and 95% confidence intervals for treatment disparities between screen-detected and non-screen-detected breast cancer. Results: Among 46,481 women diagnosed with invasive breast cancer aged 40–74 and invited for mammography screening, significant differences in treatment were observed. Screen-detected cases showed higher likelihoods of partial mastectomy compared to mastectomy, endocrine therapy, and radiotherapy, whereas chemotherapy and antibody therapy were less likely compared to non-screen-detected cases. However, when adjusting for surgery type, screen-detected cases showed lower likelihoods of radiotherapy. Age at diagnosis significantly influenced treatment odds ratios, with interactions observed for all treatments except radiotherapy adjusted for surgery. Differences increased with age, except for endocrine therapy. Radiotherapy adjusted for surgery type showed no age-related interaction. Adjusting for T and N did not alter these patterns. Interpretation: In general, screen-detected cases received less aggressive treatment, such as mastectomy, chemotherapy, and antibody therapy, compared to non-screen-detected cases. Disparities increased with age, except for endocrine therapy and radiotherapy adjusted for surgery. Differences persisted after adjusting for T and N, suggesting that these factors cannot solely explain the results.

IntroductionPatients with clinically node-negative breast cancer have a negative sentinel lymph node status (pN0) in approximately 75% of cases and the necessity of routine surgical nodal staging by sentinel lymph node biopsy (SLNB) has been questioned. Previous prediction models for pN0 have included postoperative variables, thus defeating their purpose to spare patients non-beneficial axillary surgery. We aimed to develop a preoperative prediction model for pN0 and to evaluate the contribution of mammographic breast density and mammogram features derived by artificial intelligence for de-escalation of SLNB.Materials and methodsThis retrospective cohort study included 755 women with primary breast cancer. Mammograms were analyzed by commercially available artificial intelligence and automated systems. The additional predictive value of features was evaluated using logistic regression models including preoperative clinical variables and radiological tumor size. The final model was internally validated using bootstrap and externally validated in a separate cohort. A nomogram for prediction of pN0 was developed. The correlation between pathological tumor size and the preoperative radiological tumor size was calculated.ResultsRadiological tumor size was the strongest predictor of pN0 and included in a preoperative prediction model displaying an area under the curve of 0.68 (95% confidence interval: 0.63–0.72) in internal validation and 0.64 (95% confidence interval: 0.59–0.69) in external validation. Although the addition of mammographic features did not improve discrimination, the prediction model provided a 21% SLNB reduction rate when a false negative rate of 10% was accepted, reflecting the accepted false negative rate of SLNB.ConclusionThis study shows that the preoperatively available radiological tumor size might replace pathological tumor size as a key predictor in a preoperative prediction model for pN0. While the overall performance was not improved by mammographic features, one in five patients could be omitted from axillary surgery by applying the preoperative prediction model for nodal status. The nomogram visualizing the model could support preoperative patient-centered decision-making on the management of the axilla.

BACKGROUND: By the end of 2022, nearly 20 million workers in the United States have gained paid-sick-leave coverage from mandates that require employers to provide benefits to qualified workers, including paid time off for the use of preventive services. Although the lack of paid-sick-leave coverage may hinder access to preventive care, current evidence is insufficient to draw meaningful conclusions about its relationship to cancer screening. METHODS: We examined the association between paid-sick-leave mandates and screening for breast and colorectal cancers by comparing changes in 12- and 24-month rates of colorectal-cancer screening and mammography between workers residing in metropolitan statistical areas (MSAs) that have been affected by paid-sick-leave mandates (exposed MSAs) and workers residing in unexposed MSAs. The comparisons were conducted with the use of administrative medical-claims data for approximately 2 million private-sector employees from 2012 through 2019. RESULTS: Paid-sick-leave mandates were present in 61 MSAs in our sample. Screening rates were similar in the exposed and unexposed MSAs before mandate adoption. In the adjusted analysis, cancer-screening rates were higher among workers residing in exposed MSAs than among those in unexposed MSAs by 1.31 percentage points (95% confidence interval [CI], 0.28 to 2.34) for 12-month colorectal cancer screening, 1.56 percentage points (95% CI, 0.33 to 2.79) for 24-month colorectal cancer screening, 1.22 percentage points (95% CI, -0.20 to 2.64) for 12-month mammography, and 2.07 percentage points (95% CI, 0.15 to 3.99) for 24-month mammography. CONCLUSIONS: In a sample of private-sector workers in the United States, cancer-screening rates were higher among those residing in MSAs exposed to paid-sick-leave mandates than among those residing in unexposed MSAs. Our results suggest that a lack of paid-sick-leave coverage presents a barrier to cancer screening. (Funded by the National Cancer Institute.).

PurposeThe aim of this study was to develop a prioritization strategy for scheduling immediate screening mammographic interpretation and possible diagnostic evaluation.MethodsA population-based cohort with screening mammograms performed from 2012 to 2020 at 126 radiology facilities from 7 Breast Cancer Surveillance Consortium registries was identified. Classification trees identified combinations of clinical history (age, BI-RADS® density, time since prior mammogram, history of false-positive recall or biopsy result), screening modality (digital mammography, digital breast tomosynthesis), and facility characteristics (profit status, location, screening volume, practice type, academic affiliation) that grouped screening mammograms by recall rate, with ≥12/100 considered high and ≥16/100 very high. An efficiency ratio was estimated as the percentage of recalls divided by the percentage of mammograms.ResultsThe study cohort included 2,674,051 screening mammograms in 925,777 women, with 235,569 recalls. The most important predictor of recall was time since prior mammogram, followed by age, history of false-positive recall, breast density, history of benign biopsy, and screening modality. Recall rates were very high for baseline mammograms (21.3/100; 95% confidence interval, 19.7-23.0) and high for women with ≥5 years since prior mammogram (15.1/100; 95% confidence interval, 14.3-16.1). The 9.2% of mammograms in subgroups with very high and high recall rates accounted for 19.2% of recalls, an efficiency ratio of 2.1 compared with a random approach. Adding women

Contrast-enhanced mammography (CEM) may provide an alternative to magnetic resonance imaging as a diagnostic exam in women with known or suspected breast cancer or as a screening exam in women at increased risk of breast cancer. Women with breast augmentation, either for oncologic or cosmetic reasons, may fall into this increased risk population and need safe and effective screening and diagnostic imaging tools. Here, we present our clinical practice data in order to demonstrate the feasibility of CEM in women with breast implants. An institutional review board-approved, Health Insurance Portability and Accountability Act-compliant, retrospective review of our tertiary cancer centers database yielded 104 women with breast implants who underwent 198 CEM exams from November 2014 to March 2020. All 198/198 (100%) exams were successfully completed in 104 women. Exam indications included: 174/198 (88%) screening due to increased risk, 10/198 (5%) to evaluate a palpable abnormality, 9/198 (

Breast hemangiomas are benign vascular tumors that are infrequently identified in male patients. Most of the reported cases of hemangiomas in male breasts have been identified in symptomatic patients who presented with a palpable lump in the breast. We present a case of an incidentally discovered hemangioma in an asymptomatic male patient, raising the possibility that male breast hemangiomas may be more prevalent than originally thought.

Noniatrogenically acquired foreign bodies in the nipple-areola complex or breast skin are rare and can have variable imaging features, depending on the nature of the foreign material. We present the case of a 41-year-old female who had numerous apparent round and punctate calcifications in the right periareolar breast, predominantly within the skin. The biopsy showed multiple glass shards on a background of scar tissue. Further discussion with the patient confirmed that the glass shards were acquired during a motor vehicle accident several years earlier. We also review the types of foreign body material observed in the breast, the imaging appearance of glass foreign bodies in soft tissue, and methods of removal.

Idiopathic Granulomatous Mastitis (IGM) is a benign inflammatory disease of the breast that is difficult to treat, as there is no definitive protocol for management and it has a higher recurrence rate. The present case series presents five patients who exhibited clinical symptoms such as breast swelling, pain and nipple discharge, and were diagnosed with IGM. They were treated with a multimodality approach (surgical and/or conservative management) based on their symptoms. All patients were successfully treated for IGM using the multimodality approach. While a recurrence was observed in one out of five patients, it was further successfully treated with additional conservative management. The proposed multimodality treatment is effective in treating IGM. It eliminates the risk associated with surgical intervention among patients clinically diagnosed with IGM. It also eliminates the risk of corticosteroid side-effects among patients whose initial clinical diagnosis and postoperative histopathological examination is negative for IGM.

Introduction: Breast cancer is the most common cancer among women worldwide. Early detection of breast cancer reduces mortality and morbidity. Acquiring or identifying valuable information in the form of rules is the key to an accurate diagnosis and differentiation between benign and malignant breast cancers. Our goal is to find the hidden but beneficial knowledge in the form of rules from datasets. In this paper, we use association rule mining algorithms to obtain information in the form of data rules for differential diagnosis between benign and malignant breast masses based on radiomic features, extracted from mammography images. Material and Methods: In this study, 703 patients with both benign and malignant tumors were selected from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) database. The embedded method was employed to select the radiomic features of the image and uncover the hidden patterns of data through the Apriori algorithm. Results: The association rules were generated from separated rules for benign and malignancy classes. The important features of the benign class include mass margins, horizontal long-run emphasis, 135drfraction, WavEnHLs3, vertical short-run emphasis, Teta1, 45dgr run-length with no uniformity, Teta2, and differential entropy2. However, the important features of the malignant class include assessment, correlation4, Teta1, WavEnLHs3, contrast5, vertical short-run emphasis, and differential entropy2. Conclusion: It can be concluded that the proposed method has been successful in determining specific features for tumor prediction.

Abstract In the mid-1990s, the identification of BRCA1/2 genes for breast cancer susceptibility led to testing breast MRI accuracy in screening women at increased risk. From 2000 onwards, ten intraindividual comparative studies showed the marked superiority of MRI: the sensitivity ranged 25−58% for mammography, 33−52% for ultrasound, 48−67% for mammography plus ultrasound, and 71−100% for MRI; specificity 93–100%, 91–98%, 89–98%, and 81–98%, respectively. Based on the available evidence, in 2006–2007, the UK National Institute for Clinical Excellence and the American Cancer Society recommended MRI screening of high-risk women, followed by other international guidelines. Despite evidence-based medicine ideally requiring randomised controlled trials (RCTs) for policy changes regarding screening procedures, breast MRI for high-risk screening was adopted in many countries worldwide. In 2019, the results of the “DENSE” RCT were published in favour of breast MRI screening of women with extremely dense breasts compared to mammography alone, showing a reduction of more than 80% of the interval cancer rate in women who attended MRI screening. Even though international recommendations in favour of this practice were issued, substantial obstacles still prevent health systems from adopting breast MRI for screening women with extremely dense breasts. A paradox is evident: we adopted a screening procedure without evidence from RCTs, and now that we have this level-1 evidence for the same procedure, we fail to do so. This critical review tries to explain the differences between the two cases, as examples of the complex pathways of translating radiological research into everyday practice. Critical relevance statement The high-level evidence in favour of breast MRI screening of women with extremely dense breasts is failing to persuade policy makers to translate this into clinical practice. Key points • Breast MRI screening of high-risk women was adopted on basis of the evidence provided by test accuracy comparative studies showing an MRI performance greatly superior to that of mammography. • Breast MRI screening of women with extremely dense breasts has not been adopted although the evidence of a large reduction in interval cancer rate from a RCT. • We illustrate the differences between the two cases, as an example of the complex ways of translation of radiological research in clinical practice according to the EBM theory. Graphical Abstract

Ectopic breast tissue can develop along the mammary ridge from the axilla to the groin, and the most common site is the axillae. Primary carcinoma of ectopic breast tissue is extremely rare. We report a rare case of a 61-year-old woman with a palpable mass in her left axilla who had a history of surgical excision of accessory breast tissue in the same area. Mammography (MMG), including axillary tail view, ultrasound (US), and breast MRI were performed. We evaluated the extent and characteristics of the microcalcifications in the axillary tail view. A US-guided biopsy was done, and histopathology revealed an invasive ductal carcinoma. Enhanced abdominal CT revealed multiple hepatic masses consistent with metastases, and the patient received palliative chemotherapy. Herein, we present a rare case of breast cancer arising from accessory breast tissue in the axilla, best appreciated on the axillary tail view of the patient’s MMG.

Abstract Background Residual microcalcifications after neoadjuvant chemotherapy (NAC) are challenging for deciding extent of surgery and questionable for impact on prognosis. We investigated changes in the extent and patterns of microcalcifications before and after NAC and correlated them with pathologic response. We also compared prognosis of patients depending on presence of residual microcalcifications after NAC. Methods A total of 323 patients with invasive breast carcinoma treated with neoadjuvant chemotherapy at Kangbuk Samsung Hospital and Samsung Medical center from March 2015 to September 2018 were included. Patients were divided into four groups according to pathologic response and residual microcalcifications. Non-pCRw/mic group was defined as breast non-pCR with residual microcalcifications. Non-pCRw/o mic group was breast non-pCR without residual microcalcifications. pCRw/mic group was breast pCR with residual microcalcifications. pCRw/o mic group was breast pCR without residual microcalcifications. The first aim of this study is to investigate changes in the extent and patterns of microcalcifications before and after NAC and to correlate them with pathologic response. The second aim is to evaluate oncologic outcomes of residual microcalcifications according to pathologic response after NAC. Results There were no statistical differences in the extent, morphology, and distribution of microcalcifications according to pathologic response and subtype after NAC (all p > 0.05). With a median follow-up time of 71 months, compared to pCRw/o mic group, the hazard ratios (95% confidence intervals) for regional recurrence were 5.190 (1.160–23.190) in non-pCRw/mic group and 5.970 (1.840–19.380) in non-pCRw/o mic group. Compared to pCRw/o mic group, the hazard ratios (95% CI) for distant metastasis were 8.520 (2.130–34.090) in non-pCRw/mic group, 9.120 (2.850–29.200) in non-pCRw/o mic group. Compared to pCRw/o mic, the hazard ratio (95% CI) for distant metastasis in pCRw/mic group was 2.240 (0.230–21.500) without statistical significance (p = 0.486). Conclusions Regardless of residual microcalcifications, patients who achieved pCR showed favorable long term outcome compared to non-pCR group.

Introduction: Mammographic density is a significant risk factor for breast cancer. Classification of mammographic density based on Breast Imaging Reporting and Data System (BI-RADS) is usually used to describe breast density categories but the visual assessment can have some restrictions in a routine check in the screening mammography centers. The object of this study was to investigate the effectiveness of artificial neural networks in predicting breast density, based on the clinical patient dataset in a University hospital.Material and Methods: In this study, mammographic breast density was assessed for 219 women who underwent digital mammography screening using Volpara software. A model based on the Multi-Layer Perceptron Neural Network was trained to predict patient density by identifying the (dense vs. non-dense) breast density categories. The predictive model applied to the classification was examined by the Receiver operating characteristic (ROC) curve.Results: The results show that the model predicted the breast density of patients with a classification rate of 98.2%. In addition, the area under the curve (AUC) was 0.998, signifying a high level of classification accuracy.Conclusion: The use of artificial neural networks is useful for predicting patients breast density based on clinical mammograms.

Ductal carcinoma in situ (DCIS), a pathological type of breast cancer that is limited to the terminal ducts of the breast without breaking through the basement membrane, is considered as the precursor of invasive ductal carcinoma (IDC). When DCIS breaks through the basement membrane and invades surrounding tissues, it can form infiltrating lesions. If the maximum diameter of a single infiltrating lesion is less than 1mm or the maximum diameter of multiple infiltrating lesions is less than 1mm, it is defined as ductal carcinoma in situ with microinvasion (DCIS-Mi). About 12%-40% of untreated and intervened DCIS will progress to IDC, and DCIS and IDC can also coexist. However, there is a considerable portion of DCIS that never progresses with good prognosis. Recently, overdiagnosis and overtreatment of DCIS have become the research hotspots. The histological grade of DCIS is mainly based on the morphology of the nucleus, which is divided into three nuclear levels: low, medium, and high. There are also significant differences in receptor expression and molecular type distribution between DCIS, DCIS-Mi, and IDC. For DCIS with or without microinvasion as well as different histological grades, there are many controversies about the treatment regimen, clinical prognosis and risk. The development of modern imaging technology has achieved preliminary evaluation of histological grading, infiltration status, and prognosis prediction of DCIS. The most commonly used breast imaging techniques in clinical practice currently include mammography (MG), ultrasound (US), and magnetic resonance imaging (MRI). The imaging principles of these three techniques are different, and each has its own advantages and disadvantages in breast disease imaging diagnosis. However, they can complement each other and play an important role in disease diagnosis, treatment, and prognosis evaluation. Mammography has the advantages of safety, reliability and good repeatability. It is the preferred screening method for breast cancer recommended by international guidelines. The main manifestations of DCIS on MG can be divided into non calcified lesions and calcified lesions. On US, the main manifestations are lesions and non-lesion type, which can be further divided into hypoechoic changes, calcification, ductal changes, and structural disorders and distortions. MRI has higher sensitivity in detecting DCIS without calcification and multifocal DCIS compared with MG, and has higher accuracy in evaluating the lesion range. However, there are also shortcomings such as low diagnostic specificity and insensitivity to microcalcification display. In addition, radiomics has great potential in the histopathological evaluation, prediction, and guidance of individualized precision treatment of DCIS. In the current era of precision medicine, image features, histopathology, molecular genes, etc. are increasingly significant in predicting the prognosis of breast cancer. The early accurate diagnosis and molecular type of DCIS are also extremely important in clinical work. It has become a consensus in clinical treatment to predict the potential benefits of different treatments through molecular typing, histological grade, and imaging findings, in order to develop the most suitable personalized treatment plan. This article reviewed the correlation between imaging features and the molecular subtype, histopathology and prognosis of DCIS.

Abstract Background To explore the capability and clinical significance of chest thin-section computed tomography (CT) for localization of mammographically detected clustered microcalcifications. Methods A total of 69 patients with 71 mammographically detected clustered microcalcifications received surgical biopsy under the guidance of mammography (MG), CT was used to localize calcifications combined with MG if calcifications can be seen on CT. Intraoperative mammography of the specimens were performed in all cases for identification of the resected microcalcifications. The clinical, imaging and pathological information of these patients were analyzed. Results A total of 42 (59.15%) cases of calcifications were localized by CT + MG, 29 (40.85%) cases were guided only by the mammography. All suspicious calcifications on the mammography were successfully removed. Pathological results showed 42 cases were cancer, 23 cases were benign, and 6 cases were atypical hyperplasia. The mean age in the CT + MG group was older than that of the MG group (54.12 vs. 49.27 years; P = 0.014). The maximum diameter of clusters of microcalcifications on mammography in the CT + MG group was larger than that of the MG group [(cranio-caudal view, 1.52 vs. 0.61 mm, P = 0.000; mediolateral oblique (MLO) view, 1.53 vs. 0.62 mm, P = 0.000)]. The gray value ratio (calcified area / paraglandular; MLO, P = 0.004) and the gray value difference (calcified area - paraglandular; MLO, P = 0.005) in the CT + MG group was higher than that of the MG group. Multivariate analysis showed that the max diameter of clusters of microcalcifications (MLO view) was a significant predictive factor of localization by CT in total patients (P = 0.001). Conclusions About half of the mammographically detected clustered microcalcifications could be localized by thin-section CT. Maximum diameter of clusters of microcalcifications (MLO view) was a predictor of visibility of calcifications by CT. Chest thin-section CT may be useful for localization of calcifications in some patients, especially for calcifications that are only visible in one view on the mammography.

Abstract Contrast-enhanced mammography (CEM) is a relatively recent diagnostic technique increasingly being utilized in clinical practice. Until recently, there was a lack of standardized reporting for CEM findings. However, this has changed with the publication of a supplement in the Breast Imaging Reporting and Data System (BI-RADS). A comprehensive understanding of CEM is essential for further enhancing its role in both screening and managing patients with breast malignancies. CEM can also be beneficial for problem-solving, improving the management of uncertain breast findings. Practitioners in this field should become more cognizant of how and when to employ this technique and interpret the various CEM findings. This paper aims to outline the key findings in the updated version of the BI-RADS specifically dedicated to CEM. Additionally, it will present some clinical cases commonly encountered in clinical practice. Critical relevance statement Standardized reporting and a thorough understanding of CEM findings are pivotal for advancing the role of CEM in screening and managing breast cancer patients. This standardization contributes significantly to integrating CEM as an essential component of daily clinical practice. Key points • A complete knowledge and understanding of the findings outlined in the new BI-RADS CEM are necessary for accurate reporting. • BI-RADS CEM supplement is intuitive and practical to use. • Standardization of the CEM findings enables more accurate patient management. Graphical Abstract

Abstract Among women, breast cancer remains one of the most dominant cancer types. In the year 2022, around 2,87,800 new cases were diagnosed, and 43,200 women faced mortality due to this disease. Analysis and processing of mammogram images is vital for its earlier identification and thus helps in reducing mortality rates and facilitating effective treatment for women. Accordingly, several deep-learning techniques have emerged for mammogram classification. However, it is still challenging and requires promising solutions. This study proposed a newer automated computer-aided implementation for breast cancer classification. The work starts with enhancing the mammogram contrast using a haze-reduced adaptive technique followed by augmentation. Afterward, EfficientNet-B4 pre-trained architecture is trained for both original and enhanced sets of mammograms individually using static hyperparameters’ initialization. This provides an output of 1792 feature vectors for each set and then fused using a serial mid-value-based approach. The final feature vectors are then optimized using a chaotic-crow-search optimization algorithm. Finally, the obtained significant feature vectors were classified with the aid of machine learning algorithms. The evaluation is made using INbreast and CBIS-DDSM databases. The proposed framework attained a balanced computation time with a maximum classification performance of 98.459 and 96.175% accuracies on INbreast and CBIS-DDSM databases, respectively.

Abstract Purpose One major risk factor for breast cancer is high mammographic density. It has been estimated that dense breast tissue contributes to ~ 30% of all breast cancer. Prevention targeting dense breast tissue has the potential to improve breast cancer mortality and morbidity. Anti-estrogens, which may be associated with severe side-effects, can be used for prevention of breast cancer in women with high risk of the disease per se. However, no preventive therapy targeting dense breasts is currently available. Inflammation is a hallmark of cancer. Although the biological mechanisms involved in the increased risk of cancer in dense breasts is not yet fully understood, high mammographic density has been associated with increased inflammation. We investigated whether low-dose acetylsalicylic acid (ASA) affects local breast tissue inflammation and/or structural and dynamic changes in dense breasts. Methods Postmenopausal women with mammographic dense breasts on their regular mammography screen were identified. A total of 53 women were randomized to receive ASA 160 mg/day or no treatment for 6 months. Magnetic resonance imaging (MRI) was performed before and after 6 months for a sophisticated and continuous measure breast density by calculating lean tissue fraction (LTF). Additionally, dynamic quantifications including tissue perfusion were performed. Microdialysis for sampling of proteins in vivo from breasts and abdominal subcutaneous fat, as a measure of systemic effects, before and after 6 months were performed. A panel of 92 inflammatory proteins were quantified in the microdialysates using proximity extension assay. Results After correction for false discovery rate, 20 of the 92 inflammatory proteins were significantly decreased in breast tissue after ASA treatment, whereas no systemic effects were detected. In the no-treatment group, protein levels were unaffected. Breast density, measured by LTF on MRI, were unaffected in both groups. ASA significantly decreased the perfusion rate. The perfusion rate correlated positively with local breast tissue concentration of VEGF. Conclusions ASA may shape the local breast tissue microenvironment into an anti-tumorigenic state. Trials investigating the effects of low-dose ASA and risk of primary breast cancer among postmenopausal women with maintained high mammographic density are warranted. Trial registration EudraCT: 2017-000317-22.

Atypical ductal hyperplasia (ADH) is a benign lesion of the breast that is associated with an increased risk of invasive breast cancer. This review explores the pathophysiology, risk factors for progression to breast cancer, and lifetime management for patients diagnosed with ADH on core needle biopsy (CNB). The management plan for patients diagnosed with ADH includes regular clinical surveillance, diagnostic mammography, along with risk-reduction strategies such as lifestyle modifications or the use of adjuvant endocrine therapies. This review aims to delve into the complexities of ADH from diagnosis to management to aid clinicians in finding the best way to approach this high-risk breast lesion.

Background: Studies have shown lower rates of cancer screening and high mortality rates among all Asian Americans than among non-Hispanic White populations. However, most of these studies often confound diverse Asian American subgroups with limited data on cancer screening for Indian Americans, with this group being particularly interesting because of their counterintuitive socioeconomic status. For this reason, the objective of this study is to evaluate knowledge of the United States Preventive Services Task Force (USPSTF) cancer screening guidelines and compliance among South Indian Americans residing in Southern California. Methods: This was a cross-sectional study gathering community responses through an electronic survey. The survey reports knowledge of USPSTF screening guidelines and participant compliance rates. Rates were further compared to non-Hispanic White populations from official sources. Results: South Indian Americans residing in California had lower rates of compliance for colorectal, lung, and breast cancer screening when compared to that of non-Hispanic White populations in the same region, with the exception of cervical cancer screening rates. Conclusion: Understanding the cultural characteristics of special populations, such as Indian Americans, can help communities adhere to more effective screening practices that can improve outcomes.

Abstract Introduction Breast cancer disease is known as the most common cancer among women. Lack of knowledge and awareness is a leading cause of breast cancer, and since nearly all women are increasingly susceptible to this disease, training screening behaviors for early detection is proven essential in order to reduce breast cancer mortality. Therefore, the present study was designed to determine the effect of educational intervention based on the Health Action Model in improving breast cancer screening behaviors in women aged 30 to 69 in Kashan, Iran. Methods This quasi-experimental study was conducted on 162 women aged 30–69 years old among the clients of Comprehensive health service centers in Kashan, Iran and they were assigned to intervention and control groups. The research instrument included a questionnaire assessed within three phases: baseline, 3-months, and 6-months, containing Health Action Model (HAM) structures and also three screening behaviors. The intervention consisted of a model-based education package and was carried out over 2 months. To evaluate the effect of the intervention, the mean of model structures and proportion screening behaviors in the third and sixth months were compared with the baseline phase. All analyses were carried out using SPSS, version 22. Results The intervention and control groups were homogeneous regarding the structures of the HAM and the proportion of screening behaviors in the baseline phase (p > 0.05). In the 3-month (p

Abstract Background Breast cancer (BC) represents an important cause of cancer death, its incidence rate has been rising gradually in the Arab world, and in Syria, BC is the most common cancer and the leading cause of cancer death; its prognosis gets better as we detect it early in its first stages. So, it is very important to implement one or more early detection methods such as Breast Self-Examination (BSE), Clinical Breast Examination (CBE), and mammography. BSE represents an effective method to find out changes in breast structure when they happen. This study investigates the knowledge of BSE and its practice in BC patients. Methods A quantitative cross-sectional study in Al-Bairouni hospital in Damascus-Syria was carried out using face-to-face interviews based on a structured questionnaire, which consisted of 4 sections. The data were subjected to statistical analysis using various analytical tests, including the independent t-test, one-way analysis of variance (ANOVA), and Chi-square test. Results Five hundred patients were interviewed. Only 27.4% of patients had a good knowledge of BSE, 17.4% had average knowledge, and 55.2% had low knowledge of BSE. The factors that have an impact on the knowledge of BSE were: family breast cancer history (first and second-degree relatives), education, and the region of living (between governorates). The effect of knowledge of BSE on its practice was positive. However, only 24.8% of patients have been practicing BSE; the reasons for not practicing BSE were: no one has told the patient about it (64.8% of cases), and the patient does not have any symptoms relating to the breast (21.4%).Breast cancer was identified through breast self-examination (BSE) in 15.6% of cases. Conclusion There is a low degree of knowledge and little practice of BSE among Syrian breast cancer patients. Family breast cancer history, governate, occupation, and level of education had a statistically significant effect on knowledge scores of BSE, unlike age and social status. So, some steps should be taken to increase awareness about BSE among Syrian females.

Abstract Breast cancer stands as a paramount public health concern worldwide, underscoring an imperative necessity within the research sphere for precision-driven and efficacious methodologies facilitating accurate detection. The existing diagnostic approaches in breast cancer often suffer from limitations in accuracy and efficiency, leading to delayed detection and subsequent challenges in personalized treatment planning. The primary focus of this research is to overcome these shortcomings by harnessing the power of advanced deep learning techniques, thereby revolutionizing the precision and reliability of breast cancer classification. This research addresses the critical need for improved breast cancer diagnostics by introducing a novel Convolutional Neural Network (CNN) model integrated with an Early Stopping callback and ReduceLROnPlateau callback. By enhancing the precision and reliability of breast cancer classification, the study aims to overcome the limitations of existing diagnostic methods, ultimately leading to better patient outcomes and reduced mortality rates. The comprehensive methodology includes diverse datasets, meticulous image preprocessing, robust model training, and validation strategies, emphasizing the model's adaptability and reliability in varied clinical contexts. The findings showcase the CNN model's exceptional performance, achieving a 95.2% accuracy rate in distinguishing cancerous and non-cancerous breast tissue in the integrated dataset, thereby demonstrating its potential for enhancing clinical decision-making and fostering the development of AI-driven diagnostic solutions.

Copolyamide fillers, such as Aquafilling, have been used off-label for breast augmentation in many countries, including Korea. However, safety concerns have arisen due to reported complications, including induration, masses, mastalgia, firmness, asymmetry, migration, infection, and dimpling. We report the case of a 33-year-old woman who presented with distant migration of copolyamide breast filler to the left lower abdomen and inguinal area following mammography for breast cancer screening. This case highlights the potential risks associated with the migration of copolyamide breast fillers, particularly in the context of cancer screening procedures, and emphasizes the importance of awareness and vigilance among clinicians and patients.

Periodic mammography and/or sonography examinations are conducted across numerous hospitals nationalwidely, especially for antedees with a positive mammography screening. Despite the regular practice, clinical efficacy of hospital-based breast cancer surveillance remains unclear. Specifically, the impact of surveillance interval upon survival and prognostic surrogates stratified by menopausal status, as well as malignant transition rate should be deciphered. We retrieved cancer registry to ascertain 841 breast cancers with surveillance history through administration data. Healthy controls underwent breast surveillance and were concurrently free of cancer. More benign diseases rather than cancers were identified from premenopausal women (age ≤50 years) with sonography alone within one year, as well as older women (age >50) with both mammography and sonography one to two years before a cancer or benign diagnosis. Among breast cancers, mammography alone during the antecedent one to two years had a protective effect for diagnosing carcinoma in situ rather than invasive cancer (age-adjusted odds ratio: 0.048, P = 0.016). Three-state time homogeneous Markov model showed that hospital-based breast surveillance within 2 years of disease onset reduced the malignant transition rate by 65.16% (59.79–76.74%). The clinical efficacy of breast cancer surveillance was evidenced.

Abstract Background Interval cancers might be divided into true negative interval cancer where a new lesion is detected that no sign of disease could be detected on previous screening mammogram. For false-negative interval cancers, those missed for overt symptoms and those missed for mild or undetectable ones, this includes interpretive error as benign interval cancer (benign mimics), subtle changes, masked carcinoma or slowly growing or patient factors, such as the dense breast parenchyma. Technical failure interval cancer hampered the reader to discover the abnormality. The aim of this study was to relate the risk factors for the development of the interval breast cancer such as breast density, positive family history of breast cancer in Egyptian population. Highlight the causes of missed breast cancer in order to overcome it in the future radiological interpretation. Methods A total of 74,546 screening mammographic examinations were performed in the radiology department at specialized breast cancer center in the period between 2015 and 2021 with about 844 pathologically proved malignant cases. Out of the 844 pathologically proven breast cancer cases, 185 breast cancer patients were interval breast cancer having reported normal examination in the previous year, 88 were true interval breast cancer and 97 were missed on mammography and detected later on. The cases were subjected to full-field digital mammogram (FFDM), complementary ultrasound, contrast-enhanced digital mammography (CEDM) and magnetic resonance imaging (MRI) in some cases, and all cases were histopathologically proven by either fine needle aspiration, core needle biopsy (CNB) or open biopsy. Results The mean age of the patients was 53.5 years (range 36–83 years). The overall breast cancer detection rate was 11 per 1000 women. The 185 interval cancers were present at a rate of 3.8 per 1000 women. True negative interval breast cancers where 88 cancers were not present in retrospect on screening mammograms, 17 cases present with benign findings (benign mimics mass or calcifications) and 80 cancers were missed cancers. Analysis of the cause of missed carcinoma revealed patient-related, tumor-related, or provider-related factors. Tumor factors were the most commonly encountered, accounting for 49.5% (48 cases), then provider factors in 25.8% (25 cases) while the patient factors were the least commonly encountered in 24.7% (24 cases). Recorded positive family history found in about 35% (31cases) of the true interval breast cancers. Conclusions Although mammography is the standard for detecting early breast cancer, some cancers can be missed due to various causes. Mammographic interpretation must meet high standards to reduce missed cancers. Radiologists should carefully assess screening views and order additional imaging if needed. Palpable lesions and clinical data should be further examined with ultrasound and contrast imaging if necessary. Always compare current images with previous examinations to check for any changes in lesion size. When one pathology is found, search for additional lesions.

Background: Detecting breast cancer in its early stages remains a significant challenge in the present context and is a leading cause of death among women, primarily due to delayed identification. This paper presents a practical and accurate approach based on deep learning to identify breast cancer in cytology images.Method: The analytical approach leverages knowledge from a related problem through a technique known as transfer learning. Convolutional neural networks (CNNs) are employed due to their remarkable performance on large datasets. Image classification architectures such as Google network (GoogleNet), Visual geographical group network (VGGNet), residual network (ResNet), and dense convolution network (DenseNet) are utilized in this approach. By applying transfer learning, the images are classified into two categories: those containing cancer cells and those without them. The performance of the proposed ensemble method is evaluated using a breast cytology image dataset.Results: The results of our proposed ensemble framework outperform conventional CNN models in terms of precision, recall, and F1 measures, achieving an impressive 86% prediction accuracy. Visual representations of validation graphs for each classifier demonstrate that the ensemble framework surpasses the performance of pre-trained CNN architectures.Conclusion: Combining the outcomes of conventional CNN architectures into an ensemble framework enhances early breast cancer detection, leading to a reduction in mortality through timely medical interventions.

Neuroendocrine breast carcinoma (NEBC) is an uncommon and malignant breast lesion. The absence of proper testing for this type of breast cancer carcinoma exacerbates this paucity. Additionally, only scant evidence of these tumors is present because of the repeated revisions in their diagnostic criteria throughout time. This case report highlights the importance of the correlation between histological and radiological findings in the diagnosis and treatment of neuroendocrine breast cancer. We describe the clinical, radiological, and histological characteristics of a patient diagnosed with neuroendocrine carcinoma. The breast parenchyma was penetrated by a carcinomatous tumor growth that contained trabeculae and lobules, exhibiting mild cytonuclear atypia, and having a low mitotic index. This report emphasizes the value of a multidisciplinary strategy for precise diagnosis and treatment planning.