Search Results (348)

Background: Breast cancer is the most prevalent malignancy among women globally. In Romania, it is the most frequent form of cancer affecting women, with approximately 12,000 new cases diagnosed annually, and the second most common cause of cancer-related mortality, second only to lung cancer. Methods: This study looked at 79 breast cancer patients from Oltenia, concentrating on epidemiology, histology, diagnostic features, and treatments. Patients were chosen based on inclusion criteria such as histopathologically verified diagnosis, availability of clinical and treatment data, and follow-up information. The analyzed biological material consisted of tissue samples taken from the breast parenchyma and axillary lymph nodes. Even though not the primary subject of this paper, all patients underwent immunohistochemical (IHC) evaluation both preoperatively and postoperatively. Results: We found invasive ductal carcinoma to be the predominant type, while ductal carcinoma in situ (DCIS) and mixed types were rare. We performed cross-tabulations of metastasis versus nodal status and age versus therapy type; none reached significance (all p > 0.05), suggesting observed differences were likely due to chance. A chi-square test comparing surgical interventions (breast-conserving vs. mastectomy) in patients who did or did not receive chemotherapy showed, χ² = 3.17, p = 0.367, indicating that chemotherapy did not significantly influence surgical choice. Importantly, adjuvant chemotherapy and radiotherapy were used at similar rates across age groups, whereas neoadjuvant hormonal (endocrine) therapy was more common in older patients (but without statistical significance). Conclusions: Finally, we discussed the consequences of individualized care and early detection. Romania’s shockingly low screening rate, which contributes to delayed diagnosis, emphasizes the importance of improved population medical examination and tailored treatment options. Also, the country has one of the lowest rates of mammography uptake in Europe and no systematic population screening program. Full article

Breast cancer is the leading cause of cancer-related mortality among women world-wide, and early screening is critical for improving patient survival. Medical imaging plays a central role in breast cancer screening, diagnosis, and treatment monitoring. However, conventional imaging modalities—including mammography, ultrasound, and magnetic resonance imaging—face limitations such as low diagnostic specificity, relatively slow imaging speed, ionizing radiation exposure, and dependence on exogenous contrast agents. Photoacoustic imaging (PAI), a novel hybrid imaging technique that combines optical contrast with ultrasonic spatial resolution, has shown great promise in addressing these challenges. By revealing anatomical, functional, and molecular features of the breast tumor microenvironment, PAI offers high spatial resolution, rapid imaging, and minimal operator dependence. This review outlines the fundamental principles of PAI and systematically examines recent advances in its application to breast cancer screening, diagnosis, and therapeutic evaluation. Furthermore, we discuss the translational potential of PAI as an emerging breast imaging modality, complementing existing clinical techniques. Full article

Mammography is the main method for early detection of breast cancer, which is still a major global health concern. However, inter-reader variability and the inherent difficulty of interpreting subtle radiographic features frequently limit the accuracy of diagnosis. A thorough assessment of deep convolutional neural networks (CNNs) for automated mammogram classification is presented in this work, along with the introduction of two innovative multi-view integration techniques: Dual-Branch Ensemble (DBE) and Merged Dual-View (MDV). By setting aside two datasets for out-of-sample testing, we evaluate the generalizability of the model using six different mammography datasets that represent various populations and imaging systems. We compare a number of cutting-edge architectures on both individual and combined datasets, including ResNet, DenseNet, EfficientNet, MobileNet, Vision Transformers, and VGG19. Both MDV and DBE strategies improve classification performance, according to experimental results. VGG19 and DenseNet both obtained high ROC AUC scores of 0.9051 and 0.7960 under the MDV approach. DenseNet demonstrated strong performance in the DBE setting, achieving a ROC AUC of 0.8033, while ResNet50 recorded a ROC AUC of 0.8042. These enhancements demonstrate how beneficial multi-view fusion is for boosting model robustness. The impact of domain shift is further highlighted by generalization tests, which emphasize the need for diverse datasets in training. These results offer practical advice for improving CNN architectures and integration tactics, which will aid in the creation of trustworthy, broadly applicable AI-assisted breast cancer screening tools. Full article

Background and Objectives: The aim of our study was to assess the diagnostic accuracy of four imaging modalities—digital mammography (DM), digital breast tomosynthesis (DBT), ultrasound (US), and breast magnetic resonance imaging (MRI)—applied individually and in combination in early cancer detection in women with dense breasts. Methods: This single-center retrospective study was conducted from January 2021 to September 2024 at the Oncology Institute of Vojvodina in Serbia and included 168 asymptomatic and symptomatic women with dense breasts. Based on the exclusion criteria, the final number of women who were screened with all four imaging methods was 156. The reference standard for checking the diagnostic accuracy of these methods is the result of a histopathological examination, if a biopsy is performed, or a stable radiological finding in the next 12–24 months. Results: The findings underscore the superior diagnostic performance of breast MRI with the highest sensitivity (95.1%), specificity (78.7%), and overall accuracy (87.2%). In contrast, DM showed the lowest sensitivity (87.7%) and low specificity (49.3%). While the combination of DM + DBT + US demonstrated improved sensitivity to 96.3%, its specificity drastically decreased to 32%, illustrating as ensitivity–specificity trade-off. Notably, the integration of all four modalities increased sensitivity to 97.5% but decreased specificity to 29.3%, suggesting an overdiagnosis risk. DBT significantly improved performance over DM alone, likely due to enhanced tissue differentiation. US proved valuable in dense breast tissue but was associated with a high false-positive rate. Breast MRI, even when used alone, confirmed its status as the gold standard for dense breast imaging. However, its widespread use is constrained by economic and logistical barriers. ROC curve analysis further emphasized MRI’s diagnostic superiority (AUC = 0.958) compared with US (0.863), DBT (0.828), and DM (0.820). Conclusions: This study provides a unique, comprehensive comparison of all four imaging modalities within the same patient cohort, offering a rare model for optimizing diagnostic pathways in women with dense breasts. The findings support the strategic integration of complementary imaging approaches to improve early cancer detection while highlighting the risk of increased false-positive rates. In settings where MRI is not readily accessible, a combined DM + DBT + US protocol may serve as a pragmatic alternative, though its limitations in specificity must be carefully considered. Full article

Background and Objectives: Breast cancer remains a significant global health burden, demanding continuous innovation in diagnostic and prognostic tools. This meta-analysis and systematic review aims to synthesize evidence from 2015 to 2025 regarding the diagnostic utility of artificial intelligence (AI) in mammography and the prognostic value of the Systemic Immune-Inflammation Index (SII) in breast cancer patients. Materials and Methods: A systematic literature search was conducted in PubMed, Google Scholar, EMBASE, Web of Science, and Scopus. Studies evaluating AI performance in mammographic breast cancer detection and those assessing the prognostic significance of SII (based on routine hematologic parameters) were included. The risk of bias was assessed using QUADAS-2 and the Newcastle–Ottawa Scale. Meta-analyses were conducted using bivariate and random-effects models, with subgroup analyses by clinical and methodological variables. Results: Twelve studies were included, five assessing AI and seven assessing SII. AI demonstrated high diagnostic accuracy, frequently matching or surpassing that of human radiologists, with AUCs of up to 0.93 and notable reductions in radiologist reading times (17–91%). Particularly in dense breast tissue, AI improved detection rates and workflow efficiency. SII was significantly associated with poorer outcomes, including reduced overall survival (HR ~1.97) and disease-free survival (HR ~2.07). However, variability in optimal cut-off values for SII limits its immediate clinical standardization. Conclusions: AI enhances diagnostic precision and operational efficiency in mammographic screening, while SII offers a cost-effective prognostic biomarker for systemic inflammation in breast cancer. Their integration holds promise for more personalized care. Nevertheless, challenges persist regarding prospective validation, standardization, and equitable access, which must be addressed through future translational research. Full article

Breast cancer continues to be the most common malignancy among women worldwide, presenting a considerable public health issue. Mammography, though the gold standard for screening, has limitations that catalyzed the advancement of non-invasive, radiation-free alternatives, such as thermal imaging (thermography). This research introduces a novel computer-aided diagnosis (CAD) framework aimed at improving breast cancer detection via thermal imaging. The suggested framework mitigates the limitations of current CAD systems, which frequently utilize intricate convolutional neural network (CNN) structures and resource-intensive preprocessing, by incorporating streamlined CNN designs, transfer learning strategies, and multi-architecture ensemble methods. Features are primarily obtained from various layers of MobileNet, EfficientNetB0, and ShuffleNet architectures to assess the impact of individual layers on classification performance. Following that, feature transformation methods, such as discrete wavelet transform (DWT) and non-negative matrix factorization (NNMF), are employed to diminish feature dimensionality and enhance computational efficiency. Features from all layers of the three CNNs are subsequently incorporated, and the Minimum Redundancy Maximum Relevance (MRMR) algorithm is utilized to determine the most prominent features. Ultimately, support vector machine (SVM) classifiers are employed for classification purposes. The results indicate that integrating features from various CNNs and layers markedly improves performance, attaining a maximum accuracy of 99.4%. Furthermore, the combination of attributes from all three layers of the CNNs, in conjunction with NNMF, attained a maximum accuracy of 99.9% with merely 350 features. This CAD system demonstrates the efficacy of thermal imaging and multi-layer feature amalgamation to enhance non-invasive breast cancer diagnosis by reducing computational requirements through multi-layer feature integration and dimensionality reduction techniques. Full article

Background: The integration of artificial intelligence (AI) in digital mammography (DM) screening holds promise for early breast cancer detection, potentially enhancing accuracy and efficiency. However, AI performance is not identical to that of human observers. We aimed to identify common morphological image characteristics of true cancers that are missed by either AI or human screening when their interpretations are discrepant. Methods: Twenty-six breast cancer-positive cases, identified from a large retrospective multi-institutional digital mammography dataset based on discrepant AI and human interpretations, were included in a reader study. Ground truth was confirmed by histopathology or ≥1-year follow-up. Fourteen radiologists assessed lesion visibility, morphological features, and likelihood of malignancy. AI performance was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC). The reader study results were analyzed using interobserver agreement measures and descriptive statistics. Results: AI demonstrated high discriminative capability in the full dataset, with AUCs ranging from 0.903 (95% CI: 0.862–0.944) to 0.946 (95% CI: 0.896–0.996). Cancers missed by AI had a significantly smaller median size (9.0 mm, IQR 6.5–12.0) compared to those missed by human readers (21.0 mm, IQR 10.5–41.0) (p = 0.0014). Cancers in discrepant cases were often described as having ‘low visibility’, ‘indistinct margins’, or ‘irregular shape’. Calcifications were observed in 27% of human-missed cancers (42/154) versus 18% of AI-missed cancers (38/210). A very high likelihood of malignancy was assigned in 32.5% (50/154) of human-missed cancers compared to 19.5% (41/210) of AI-missed cancers. Overall inter-rater agreement was poor to fair (<0.40), indicating interpretation challenges of the selected images. Among the human-missed cancers, calcifications were more frequent (42/154; 27%) than among the AI-missed cancers (38/210; 18%) (p = 0.396). Furthermore, 50/154 (32.5%) human-missed cancers were deemed to have a very high likelihood of malignancy, compared to 41/210 (19.5%) AI-missed cancers (p = 0.8). Overall inter-rater agreement on the items assessed during the reader study was poor to fair (<0.40), suggesting that interpretation of the selected images was challenging. Conclusions: Lesions missed by AI were smaller and less often calcified than cancers missed by human readers. Cancers missed by AI tended to show lower levels of suspicion than those missed by human readers. While definitive conclusions are premature, the findings highlight the complementary roles of AI and human readers in mammographic interpretation. Full article

Breast cancer remains a leading cause of cancer-related deaths among women worldwide, highlighting the urgent need for early detection. While mammography is the gold standard, it faces cost and accessibility barriers in resource-limited areas. Infrared thermography is a promising cost-effective, non-invasive, painless, and radiation-free alternative that detects tumors by measuring their thermal signatures through thermal infrared radiation. However, challenges persist, including limited clinical validation, lack of Food and Drug Administration (FDA) approval as a primary screening tool, physiological variations among individuals, differing interpretation standards, and a shortage of specialized radiologists. This survey uniquely focuses on integrating texture analysis and machine learning within infrared thermography for breast cancer detection, addressing the existing literature gaps, and noting that this approach achieves high-ranking results. It comprehensively reviews the entire processing pipeline, from image preprocessing and feature extraction to classification and performance assessment. The survey critically analyzes the current limitations, including over-reliance on limited datasets like DMR-IR. By exploring recent advancements, this work aims to reduce radiologists’ workload, enhance diagnostic accuracy, and identify key future research directions in this evolving field. Full article

Early detection of cancer is crucial for improving patient outcomes. Traditional modalities such as mammography and low-dose computed tomography are effective but exhibit inherent limitations, including radiation exposure and accessibility challenges. This review explores innovative, non-invasive cancer screening methods, focusing on liquid biopsy and volatile organic compound (VOC)-based detection platforms. Liquid biopsy analyzes circulating tumor DNA and other biomarkers in bodily fluids, offering potential for early detection and monitoring of treatment response. VOC-based detection leverages unique metabolic signatures emitted by cancer cells, detectable in exhaled breath or other bodily emissions, providing a rapid and patient-friendly screening option. We provide a comprehensive overview of these advanced multi-cancer detection techniques to enhance diagnostic accuracy, accessibility, and patient adherence, and ultimately enhance survival rates and patient outcomes. Full article

Background/Objectives: Digital mammography is widely used for breast cancer screening; however, variations in system design and automatic exposure control (AEC) strategies can lead to significant differences in radiation dose, potentially affecting the diagnostic quality and patient safety. In this study, we aimed to determine the effect of various mammographic technologies on the in vivo mean glandular doses (MGDs) that are received in clinical settings. Methods: The MGDs and applied acquisition parameters from 194,608 mammograms, acquired employing AEC using different digital mammography systems (GE, Siemens, and two different models of Hologic), were retrospectively collected. The potential variation in MGD resulting from different technologies (system and target/filter combination) was assessed employing the Kruskal–Wallis test, followed by Dunn’s post hoc. The AEC optimization of acquisition parameters (kVp, mAs) within each system was investigated through a multi-regression analysis as a function of the compressed breast thickness (CBT). The trend line of these parameters in addition to the MGD and source-to-breast distance were also plotted and compared. Results: There were significant variations in delivered doses per CBT based on which technology was used (p < 0.001). The regression analyses revealed system-specific differences in AEC adjustments of mAs and kVp in response to CBT changes. As the CBT increases, the MGD increases with different degrees, rates, and patterns across systems due to differences in AEC strategies. Conclusions: The MGD is affected by the applied technology, which is different between systems. Clinicians need to be aware of these variations and how they affect the MGD. Additionally, manufacturers may need to consider standardizing the implemented technology effects on the MGDs. 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

Innovative biosensor technologies are revolutionizing cancer detection by offering non-invasive, sensitive, and rapid diagnostic tools, addressing the limitations of conventional screening. Non-invasive samples like breath, saliva, urine, and sweat, analyzed using advanced technologies like electronic nose systems and AI, show promise for early detection and frequent monitoring, though validation is needed. AI integration enhances data analysis and personalization. While blood-based methods remain the gold standard, combining them with less invasive sample types like saliva or sweat, and using sensitive techniques, is a promising direction. Conventional methods (mammography, MRI, etc.) offer proven efficacy, but are costly and invasive. Innovative methods using biosensors offer reduced infrastructure needs, lower costs, and patient-friendly sampling. However, challenges remain in validation, standardization, and low biomarker concentrations. Integrating both methodologies could create a comprehensive framework, combining reliability with accessibility. Future research should focus on robust biosensor development, standardization, expanding application to other cancers, exploring less-studied samples like sweat, and improving affordability for wider adoption, especially in resource-limited settings. The future lies in integrating diverse approaches for more sensitive, specific, and patient-friendly screening, improving early detection and outcomes. Full article

Breast cancer is the predominant cause of cancer in developing nations, and screening through breast self-examinations and mammograms is crucial in mitigating morbidity and mortality. Nonetheless, geographic disparities in screening methods persist, attributable to sociodemographic variation and healthcare accessibility. Background/Objectives: This study aimed to analyze the influence of women’s screening practices for breast cancer and other risks, stratified by urban and rural areas in Indonesia. Methods: A case–control design was adopted, including all women who had breast cancer in 2014 as the study subjects. The Indonesian Family Life Survey data from 2007, with subjects aged at least 15 years, and from 2014 were used. Unconditional logistic regression was used to analyze the risk factors of breast cancer. Results: After controlling for confounders, the odds of breast cancer diagnosis were higher in women who performed breast self-examination (BSE) (aOR 10.22; 95% CI 1.04–50.81 and aOR 11.10; 95% CI 3.32–37.08) and those married before the age of 19 (aOR 4.81; 95% CI 1.93–6.05 and aOR 5.35; 95% CI 1.49–19.7), in urban and rural areas, respectively. In addition, women who had undergone mammography (aOR 48.04; 95% CI 10.33–83.45) had significantly higher odds of being diagnosed with breast cancer in urban areas. In rural areas, a paternal history of cancer-related death had higher odds of breast cancer (aOR 30.63; 95% CI 6.04–60.41) than those without a parental history of cancer. Conclusions: This study highlights the importance of intensifying national breast cancer screening, including BSE campaigns and expanding mammography infrastructure, particularly in rural areas, for improving breast cancer prevention and early diagnosis. Full article

Breast cancer is the most commonly diagnosed cancer worldwide, underscoring the critical need for effective early detection methods to reduce mortality. Traditional detection techniques, such as mammography, present significant limitations, particularly in women with dense breast tissue, highlighting the need for alternative screening approaches. Breathomics, based on the analysis of Volatile Organic Compounds (VOCs) present in exhaled breath, offers a non-invasive, potentially transformative diagnostic tool. These VOCs are metabolic byproducts from various organs of the human body whose presence and varying concentrations in breath are reflective of different health conditions. This review explores the potential of breathomics, highlighting its promise as a rapid, cost-effective screening approach for breast cancer, facilitated through the integration of portable solutions like electronic noses (e-noses). Key considerations for clinical translation—including patient selection, environmental confounders, and different breath collection methods—will be examined in terms of how each of them affects the breath profile. However, there are also challenges such as patient variability in VOC signatures, and the need for standardization in breath sampling protocols. Future research should prioritize standardizing sampling and analytical procedures and validating their clinical utility through large-scale clinical trials. Full article

The early detection of breast cancer, particularly in dense breast tissues, faces significant challenges with traditional imaging techniques such as mammography. This study utilizes a Near-infrared Scan (NIRscan) probe and an advanced convolutional neural network (CNN) model to enhance tumor localization accuracy and efficiency. CNN processed data from 133 breast phantoms into 266 samples using data augmentation techniques, such as mirroring. The model significantly improved image reconstruction, achieving an RMSE of 0.0624, MAE of 0.0360, R² of 0.9704, and Fuzzy Jaccard Index of 0.9121. Subsequently, we introduced a multitask CNN that reconstructs images and classifies them based on depth, length, and health status, further enhancing its diagnostic capabilities. This multitasking approach leverages the robust feature extraction capabilities of CNNs to perform complex tasks simultaneously, thereby improving the model’s efficiency and accuracy. It achieved exemplary classification accuracies in depth (100%), length (92.86%), and health status, with a perfect F1 Score. These results highlight the promise of NIRscan technology, in combination with a multitask CNN model, as a supportive tool for improving real-time breast cancer screening and diagnostic workflows. Full article