Search Results (80)

Background: Mammographic microcalcifications (MCs) are a common early radiological finding in breast cancer, but their significance in relation to molecular subtypes, particularly HER2-positive tumors, remains under investigation. Objectives: To evaluate the association between MCs and HER2 status in invasive breast cancer. Methods: A retrospective study was conducted on 185 patients treated at a breast unit between 2018 and 2023. Clinical, histological, and molecular data were analyzed. Logistic regression was used to identify independent predictors of MCs. Results: MCs were present in 27% of HER2-positive patients and 16.15% of HER2-negative patients (p < 0.001). HER2 positivity was the only significant independent predictor (OR = 5.89; 95% CI: 2.42–14.30; p < 0.001). Age, breast density, and histology were not associated. Conclusions: MCs are significantly associated with HER2 positivity and may serve as an early imaging marker of aggressive disease, supporting the integration of radiologic and molecular diagnostics. Full article

Background/Objectives: Accurately correlating mammographic findings with corresponding histopathologic features is considered one of the essential aspects of mammographic evaluation, guiding the next steps in cancer management and preventing overdiagnosis. The objective of this study was to evaluate patterns of mammographic microcalcifications and their association with histopathological findings related to various breast tumors. Methods: 110 out of 3603 women had microcalcification of BIRADS 3 or higher and were subjected to stereotactic/ultrasound (USG) guided biopsies, and hook-wire localization excision procedures. Ultrasound and mammography images were reviewed by experienced radiologists using the standard American College of Radiology Breast-Imaging Reporting and Data System (ACR BI-RADS). Results: Our study showed that features with a high positive predictive value (PPV) of breast malignancy were heterogeneous (75%), fine linear/branching pleomorphic microcalcifications (66.7%), linear (100%), and segmental distributions (57.1%). Features that showed a higher risk of association with ductal carcinoma in situ (DCIS) were fine linear/branching pleomorphic (odds ratio (OR): 3.952), heterogeneous microcalcifications (OR: 3.818), segmental (OR: 5.533), linear (OR: 3.696), and regional (OR: 2.929) distributions. Furthermore, the features with higher risks associated with invasive carcinoma had heterogeneous (OR: 2.022), fine linear/branching pleomorphic (OR: 1.187) microcalcifications, linear (OR: 6.2), and regional (OR: 2.543) distributions. The features of associated masses in mammograms that showed a high PPV of malignancy had high density (75%), microlobulation (100%), and spiculated margins (75%). Conclusions: We concluded that specific patterns and distributions of microcalcifications were indeed associated with a higher risk of malignancy. Those with fine linear or branching pleomorphic and segmental distribution were at a higher risk of DCIS, whereas those with heterogeneous morphology with a linear distribution were at a higher risk of invasive carcinoma. Full article

Introduction: An inverse association between high mammographic breast density (MBD) and pathologic complete response (pCR) following neoadjuvant chemotherapy (NAC) for early breast cancer (EBC) has been reported. However, the relationship of MBD to relapse-free (RFS) and breast cancer-specific survival (BCSS) is unexplored. This study aims to validate the relationship between MBD and NAC pCR in EBC and to assess correlations with RFS and BCSS. Materials & Methods: MBD was measured on contralateral mammograms in 127 women before NAC using Cumulus software. The percent dense area was correlated with patient and tumour characteristics, pCR, RFS and BCSS. Results: Mean MBD was higher in relapsing patients (p = 0.041) but did not vary by pCR or BC-deaths. As a dichotomous variable, no difference was seen between high and low MBD cohorts for pCR (17.5 vs. 25.0%, p = 0.15), BC relapse (38 vs. 30%, p = 0.15) or BC death (32 vs. 25%, p = 0.20). A planned analysis by body mass index (BMI) demonstrated high MBD associated with lower pCR (0% vs. 28.1%, p = 0.036) and trends for higher relapse (56% vs. 28%, p = 0.063) and BC deaths (56 vs. 28%, (p = 0.071)) in obese patients. No relationship was observed in non-obese patients. Conclusions: Obesity and high MBD may interact to cause chemoresistance. Further research in these patients is warranted. Full article

Background and Objectives: Breast cancer is a leading global health challenge, where early detection is essential for improving survival outcomes. Two-dimensional (2D) mammography is the established standard for breast cancer screening; however, its diagnostic accuracy is limited by factors such as breast density and inter-reader variability. Recent advances in artificial intelligence (AI) have shown promise in enhancing radiological interpretation. This study aimed to assess the utility of AI in improving lesion detection and classification in 2D mammography. Materials and Methods: A retrospective analysis was performed on a dataset of 578 mammographic images obtained from a single radiology center. The dataset consisted of 36% pathologic and 64% normal cases, and was partitioned into training (403 images), validation (87 images), and test (88 images) sets. Image preprocessing involved grayscale conversion, contrast-limited adaptive histogram equalization (CLAHE), noise reduction, and sharpening. A convolutional neural network (CNN) model was developed using transfer learning with ResNet50. Model performance was evaluated using sensitivity, specificity, accuracy, and area under the receiver operating characteristic (AUC-ROC) curve. Results: The AI model achieved an overall classification accuracy of 88.5% and an AUC-ROC of 0.93, demonstrating strong discriminative capability between normal and pathologic cases. Notably, the model exhibited a high specificity of 92.7%, contributing to a reduction in false positives and improved screening efficiency. Conclusions: AI-assisted 2D mammography holds potential to enhance breast cancer detection by improving lesion classification and reducing false-positive findings. Although the model achieved high specificity, further optimization is required to minimize false negatives. Future efforts should aim to improve model sensitivity, incorporate multimodal imaging techniques, and validate results across larger, multicenter prospective cohorts to ensure effective integration into clinical radiology workflows. Full article

Breast cancer is the most common malignancy among women globally, with incidence rates continuing to rise. A comprehensive understanding of its risk factors and the underlying biological mechanisms that drive tumor initiation is essential for developing effective prevention strategies. This review examines key non-modifiable risk factors, such as genetic predisposition, demographic characteristics, family history, mammographic density, and reproductive milestones, as well as modifiable risk factors like exogenous hormone exposure, obesity, diet, and physical inactivity. Importantly, reproductive history plays a dual role, providing long-term protection while temporarily increasing breast cancer risk shortly after pregnancy. Current chemoprevention strategies primarily depend on selective estrogen receptor modulators (SERMs), including tamoxifen and raloxifene, which have demonstrated efficacy in reducing the incidence of estrogen receptor-positive breast cancer but remain underutilized due to adverse effects. Emerging approaches such as aromatase inhibitors, RANKL inhibitors, progesterone antagonists, PI3K inhibitors, and immunoprevention strategies show promise for expanding preventive options. Understanding the interactions between risk factors, hormonal influences, and tumorigenesis is critical for optimizing breast cancer prevention and advancing safer, more targeted chemopreventive interventions Full article

Background: To progress research into the biological mechanisms that link mammographic breast density to breast cancer risk, fibroglandular breast density can be used as a surrogate measure. This study aimed to develop a computational tool to classify fibroglandular breast density in hematoxylin and eosin (H&E)-stained breast tissue sections using deep learning approaches that would assist future mammographic density research. Methods: Four different architectural configurations of transferred MobileNet-v2 convolutional neural networks (CNNs) and four different models of vision transformers were developed and trained on a database of H&E-stained normal human breast tissue sections (965 tissue blocks from 93 patients) that had been manually classified into one of five fibroglandular density classes, with class 1 being very low fibroglandular density and class 5 being very high fibroglandular density. Results: The MobileNet-Arc 1 and ViT model 1 achieved the highest overall F1 scores of 0.93 and 0.94, respectively. Both models exhibited the lowest false positive rate and highest true positive rate in class 5, while the most challenging classification was class 3, where images from classes 2 and 4 were mistakenly classified as class 3. The area under the curves (AUCs) for all classes were higher than 0.98. Conclusions: Both the ViT and MobileNet models showed promising performance in the accurate classification of H&E-stained tissue sections across all five fibroglandular density classes, providing a rapid and easy-to-use computational tool for breast density analysis. Full article

Breast cancer, as of 2022, is the most prevalent type of cancer in women. Breast density—a measure of the non-fatty tissue in the breast—is a strong risk factor for breast cancer that can be estimated from mammograms. The importance of studying breast density is twofold. First, high breast density can be a factor in lowering mammogram sensitivity, as dense tissue can mask tumors. Second, higher breast density is associated with an increased risk of breast cancer, making accurate assessments vital. This paper presents a comprehensive review of the mammographic density estimation literature, with an emphasis on machine-learning-based approaches. The approaches reviewed can be classified as visual, software-, machine learning-, and segmentation-based. Machine learning methods can be further broken down into two categories: traditional machine learning and deep learning approaches. The most commonly utilized models are support vector machines (SVMs) and convolutional neural networks (CNNs), with classification accuracies ranging from 76.70% to 98.75%. Major limitations of the current works include subjectivity and cost-inefficiency. Future work can focus on addressing these limitations, potentially through the use of unsupervised segmentation and state-of-the-art deep learning models such as transformers. By addressing the current limitations, future research can pave the way for more reliable breast density estimation methods, ultimately improving early detection and diagnosis. Full article

Breast cancer remains one of the primary causes of cancer-related deaths among women globally. Early detection via mammography is essential for improving prognosis and survival rates. However, mammogram diagnostic accuracy is severely hindered by dense breast tissue, which can obstruct potential malignancies, complicating early detection. To tackle this pressing issue, this study introduces an innovative approach that leverages Generative Adversarial Networks (GANs), specifically CycleGAN and GANHopper, to transform breast density in mammograms. The aim is to diminish the masking effect of dense tissue, thus enhancing the visibility of underlying malignancies. The method uses unsupervised image-to-image translation to gradually alter breast density (from high (ACR-D) to low (ACR-A)) in mammographic images, detecting obscured lesions while preserving original diagnostic features. We applied this approach to multiple mammographic datasets, demonstrating its effectiveness in diverse contexts. Experimental results exhibit substantial improvements in detecting potential malignancies concealed by dense breast tissue. The method significantly improved precision, recall, and F1-score metrics across all datasets, revealing previously obscured malignancies and image quality assessments confirmed the diagnostic relevance of transformed images. The study introduces a novel mammogram analysis method using advanced machine-learning techniques, enhancing diagnostic accuracy in dense breasts and potentially improving early breast cancer detection and patient outcomes. Full article

Breast cancer is among the most prevalent cancers in the female population globally. Therefore, screening campaigns as well as approaches to identify patients at risk are particularly important for the early detection of suspect lesions. This study aims to propose a workflow for the automatic classification of patients based on one of the most relevant risk factors in breast cancer, which is represented by breast density. The proposed classification methodology takes advantage of the features automatically extracted from mammographic images, as digital mammography represents the major screening tool in women. Textural features were extracted from the breast parenchyma through a radiomics approach, and they were used to train different machine learning algorithms and neural network models to classify the breast density according to the standard Breast Imaging Reporting and Data System (BI-RADS) guidelines. Both binary and multiclass tasks have been carried out and compared in terms of performance metrics. Preliminary results show interesting classification accuracy (93.55% for the binary task and 82.14% for the multiclass task), which are promising compared to the current literature. As the proposed workflow relies on straightforward and computationally efficient algorithms, it could serve as a basis for a fast-track protocol for the screening of mammograms to reduce the radiologists’ workload. Full article

Background: Breast density impacts upon breast cancer risk and recurrence, but its influence on breast cancer-specific survival is unclear. This study examines the influence of mammographic breast density (MBD) at diagnosis on breast cancer-specific survival. Methods: The data of 224 patients diagnosed with breast cancer were analyzed. Two area-based MBD measurement tools—AutoDensity and LIBRA—were used to measure MBD via a mammogram of the contralateral breast acquired at the time of diagnosis. These patients were split into two groups based on their percent breast density (PBD): high (PBD ≥ 20%) versus low (PBD < 20%). Breast cancer-specific survival in each of these PBD groups was assessed at a median follow-up of 34 months using Kaplan–Meier analysis and the Cox proportional hazards model. Results: The proportion of women with low PBD who died from breast cancer was significantly higher than that seen with high PBD (p = 0.01). The 5-year breast cancer-specific survival was poorer among women with low PBD than those with high PBD (0.348; 95% CI: 0.13–0.94) vs. 0.87; 95% CI: (0.8–0.96); p < 0.001)]. Women with higher breast density demonstrated longer survival regardless of the method of PBD measurement: LIBRA [log-rank test (Mantel–Cox): 9.4; p = 0.002)]; AutoDensity [log-rank test (Mantel–Cox) 7.6; p = 0.006]. Multivariate analysis also demonstrated that there was a higher risk of breast cancer-related deaths in women with low PBD (adjusted HR: 5.167; 95% CI: 1.974–13.521; p = 0.001). Conclusion: Women with <20% breast density at breast cancer diagnosis demonstrate poor survival regarding the disease. The impact of breast density on survival is not influenced by the method of measurement. Full article

Background/Objectives: High breast density is a risk factor for breast cancer and can reduce the sensitivity of mammography. Given the influence of breast density on patient risk stratification and screening accuracy, it is crucial to monitor the prevalence of extremely dense breasts within local populations. Moreover, there is a lack of comprehensive understanding regarding breast density prevalence in Switzerland. Therefore, this study aimed to determine the prevalence of breast density in a selected Swiss population. Methods: To overcome the potential variability in breast density classifications by human readers, this study utilized commercially available deep convolutional neural network breast classification software. A retrospective analysis of mammographic images of women aged 40 years and older was performed. Results: A total of 4698 mammograms from women (58 ± 11 years) were included in this study. The highest prevalence of breast density was in category C (heterogeneously dense), which was observed in 41.5% of the cases. This was followed by category B (scattered areas of fibroglandular tissue), which accounted for 22.5%. Conclusions: Notably, extremely dense breasts (category D) were significantly more common in younger women, with a prevalence of 34%. However, this rate dropped sharply to less than 10% in women over 55 years of age. Full article

Mammographic density (MD) assessment is subject to inter- and intra-observer variability. An automated method, such as Quantra software, could be a useful tool for an objective and reproducible MD assessment. Our purpose was to evaluate the performance of Quantra software in assessing MD, according to BI-RADS^® Atlas Fifth Edition recommendations, verifying the degree of agreement with the gold standard, given by the consensus of two breast radiologists. A total of 5009 screening examinations were evaluated by two radiologists and analysed by Quantra software to assess MD. The agreement between the three assigned values was expressed as intraclass correlation coefficients (ICCs). The agreement between the software and the two readers (R1 and R2) was moderate with ICC values of 0.725 and 0.713, respectively. A better agreement was demonstrated between the software’s assessment and the average score of the values assigned by the two radiologists, with an index of 0.793, which reflects a good correlation. Quantra software appears a promising tool in supporting radiologists in the MD assessment and could be part of a personalised screening protocol soon. However, some fine-tuning is needed to improve its accuracy, reduce its tendency to overestimate, and ensure it excludes high-density structures from its assessment. Full article

Assessing a woman’s risk of breast cancer is important for personalized screening. Mammographic density is a strong risk factor for breast cancer, but parenchymal texture patterns offer additional information which cannot be captured by density. We aimed to combine BI-RADS density score 4th Edition and a deep-learning-based texture score to stratify women in screening and compare rates among the combinations. This retrospective study cohort study included 216,564 women from a Danish populations-based screening program. Baseline mammograms were evaluated using BI-RADS density scores (1–4) and a deep-learning texture risk model, with scores categorized into four quartiles (1–4). The incidence rate ratio (IRR) for screen-detected, interval, and long-term cancer were adjusted for age, year of screening and screening clinic. Compared with subgroup B1-T1, the highest IRR for screen-detected cancer were within the T4 category (3.44 (95% CI: 2.43–4.82)−4.57 (95% CI: 3.66–5.76)). IRR for interval cancer was highest in the BI-RADS 4 category (95% CI: 5.36 (1.77–13.45)−16.94 (95% CI: 9.93–30.15)). IRR for long-term cancer increased both with increasing BI-RADS and increasing texture reaching 5.15 (4.31–6.16) for the combination of B4-T4 compared with B1-T1. Deep-learning-based texture analysis combined with BI-RADS density categories can reveal subgroups with increased rates beyond what density alone can ascertain, suggesting the potential of combining texture and density to improve risk stratification in breast cancer screening. Full article

Background: High breast density found using mammographs (MGs) reduces positivity rates and is considered a risk factor for breast cancer. Research on the relationship between Volpara density grade (VDG) and compressed breast thickness (CBT) in the Japanese population is still lacking. Moreover, little attention has been paid to pseudo-dense breasts with CBT < 30 mm among high-density breasts. We investigated VDG, CBT, and apparent high breast density in patients with breast cancer. Methods: Women who underwent MG and breast cancer surgery at our institution were included. VDG and CBT were measured. VDG was divided into a non-dense group (NDG) and a dense group (DG). Results: This study included 419 patients. VDG was negatively correlated with CBT. The DG included younger patients with lower body mass index (BMI) and thinner CBT. In the DG, patients with CBT < 30 mm had lower BMI and higher VDG; however, no significant difference was noted in the positivity rate of the two groups. Conclusions: Younger women tend to have higher breast density, resulting in thinner CBT, which may pose challenges in detecting breast cancer on MGs. However, there was no significant difference in the breast cancer detection rate between CBT < 30 mm and CBT ≥ 30 mm. Full article

The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736–0.903), along with an F1 score of 0.798 (0.594–0.905), precision of 0.806 (0.596–0.896), recall of 0.830 (0.650–0.946), and a Cohen’s Kappa ($\kappa$) of 0.708 (0.562–0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model’s competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes. Full article