搜索结果：Journal of breast imaging

Background: Breast cancer is the most common malignancy among women and represents a leading cause of worldwide cancer-related mortality. Mammographic screening substantially reduces breast cancer-specific mortality by enabling its early detection. Organized mammographic screening is recognized as the most effective strategy for early detection, mortality reduction, and for improving quality of life. Romania currently lacks an organized, functional, invitation-based system. National data regarding the utilization of mammography remain limited and poorly characterized. Materials and Methods: A cohort of 2,500 women aged 40-90 years diagnosed with breast cancer was analyzed. The study was conducted in four medical centers in Bucharest, Romania: the Prof. Dr. Alexandru Trestioreanu Institute of Oncology, Medicover Pipera Hospital, Profmedica Clinic, and CIB Medical Clinic, between June and December 2025. Information regarding mammographic examinations performed prior to diagnosis was obtained through a structured interview and subsequently validated by reviewing medical records. The sociodemographic variables analyzed included age, place of residence, and educational level. Patients were categorized into two groups according to their pre-diagnostic mammography status: those who had never undergone mammography in their lifetime and those who had undergone at least one mammographic examination prior to breast cancer detection. For patients in the latter group, the interval between the most recent mammography and the time of diagnosis was recorded and analyzed. Results: Overall, 76% of the patients had not undergone any mammographic examination prior to diagnosis. Among those who had undergone at least one mammography, 37.3% had their most recent examination more than four years before diagnosis. When these two subgroups were combined, it was found that 85% of patients diagnosed with breast cancer had not received a recent mammographic evaluation within the four years preceding diagnosis that might have enabled earlier detection of the disease. Conclusion: This study highlights the limited use of mammography for the early detection of breast cancer in Romania through periodic examinations within an opportunistic screening setting. Consequently, most cases are diagnosed only after the onset of signs and symptoms. This finding reflects insufficient public awareness of the benefits of early detection of this disease. Among the 2,500 women with breast cancer who were interviewed in this study, 76% had never undergone a mammographic examination in their lifetime. Moreover, 85% had not undergone any mammography within the four years preceding diagnosis. The development and consolidation of public information and medical education initiatives are essential to increase participation and improve population-level understanding of the benefits of early detection for breast cancer. However, even when it is widely implemented, opportunistic screening alone is unlikely to achieve a meaningful population-level impact. A reduction in breast cancer mortality through early diagnosis can only be achieved through the implementation of an organized, national screening program.

As of September 2024, federal legislation mandates that patients be informed of their breast density, a modest breast cancer risk factor and known cancer-masking agent. This binary metric, dense vs nondense, applies to 40% to 50% of women and is subjectively assessed with interreader variability, limiting its utility for guiding supplemental imaging. To compare the performance of a deep learning (DL) breast cancer risk model vs radiologist-assessed breast density in estimating future breast cancer and false-negative (FN) screening results. This retrospective cohort study included consecutive bilateral screening mammograms from women 30 years or older performed from January 1, 2009, to December 31, 2018, across 5 sites of a large academic health system, with follow-up through December 31, 2023, to allow ascertainment of 5-year breast cancer outcomes. A DL risk model applied to standard screening mammograms and radiologist-assessed breast density categorized using the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) Atlas. Primary outcomes were breast cancer diagnoses within 5 years of mammography and FN screening results, defined as BI-RADS 1 or 2 examinations followed by a cancer diagnosis within 1 year. DL risk scores were stratified as low (<1.7%), intermediate (1.7%-3.0%), or high (>3.0%). Cancer and FN rates were compared across DL risk groups and breast density categories. Discriminatory performance was assessed using the area under the receiver operating characteristic curve (AUROC) and compared using the DeLong test. Among 123&#x202f;091 mammograms in 67&#x202f;019 women (median [IQR] age, 58.0 [50.0-67.0] years), 50 974 (41.4%) were classified as dense. The DL model demonstrated significantly higher discriminatory accuracy than breast density in predicting future cancer (AUROC, 0.71 [95% CI, 0.70-0.72] vs 0.53 [95% CI, 0.52-0.54]; P&#x2009;<&#x2009;.001). FN rates increased across DL risk groups (2.1 per 1000 examinations in high-risk vs 1.0 and 0.6 in intermediate and low-risk groups, respectively). Women with dense breasts had higher FN rates than those with nondense breasts (1.7 vs 0.6 per 1000 examinations; P&#x2009;<&#x2009;.001). Adding breast density to the DL model did not improve performance. In this cohort study of screening mammography, a DL risk model outperformed breast density in estimating risk of future breast cancer and stratified FN screening results across risk groups. These findings support transitioning from density-based policy triggers toward more precise image-derived risk models to guide access to supplemental imaging.

Early detection of small metastatic foci is crucial for improving breast cancer patient survival. Plastin-3 (PLS3) has emerged as a key biomarker in breast cancer metastasis due to its role in cytoskeletal remodeling and cell motility. Herein, we developed a multigated DNA cascade amplifier (MDCA) for the highly sensitive and specific detection of PLS3 mRNA. This system integrates a multigated activation strategy and dual signal amplification, ensuring both precise spatiotemporal control and high sensitivity in imaging of breast cancer metastasis. DNA hairpin probes were preadsorbed on MnO2 nanoflowers to facilitate intracellular delivery, while intracellular glutathione (GSH) and external UV light enabled programmed probe release. The combination of PLS3 mRNA-triggered hybridization chain reaction (HCR) and APE1-mediated enzyme catalytic amplification leads to a notable increase in detection sensitivity, achieving a detection limit of 5.8 aM. The MDCA was applied for in vitro sensing of PLS3 mRNA in breast cancer cell lines at the single-cell level. More importantly, with the aid of machine learning models, the MDCA facilitates the visualization of breast cancer metastasis in clinical tissue samples, with an excellent accuracy of more than 92%. This method holds great promise for early diagnosis, evaluating metastasis risk, and advancing precision oncology.

Until recently, no breast cancer database in the United States records how breast cancers are initially detected, and, therefore, current data lack the fundamental ability to link breast cancer outcomes to screening directly. Consequently, the magnitude and relative contributions that screening and treatment play in the reduction of breast cancer mortality are unknown, and the debate over breast cancer screening continues. To address these shortcomings, advocates have proposed that the Method of Detection (MOD) be documented with every breast cancer diagnosis. MOD data can then be abstracted to cancer registries to provide direct evidence of the impact of screening on breast cancer diagnoses and subsequent outcomes and thereby resolve the debate about the benefits of screening. Given the widespread support of MOD reporting, there is also great potential to improve and facilitate its clinical implementation. Barriers to MOD adoption include a lack of an organized national breast cancer screening program, the cost of implementation, a lack of radiologist MOD training, and technical lapses, among others. The American College of Radiology and the Society of Breast Imaging have addressed barriers to MOD implementation through educational opportunities and the Breast Imaging Reporting and Data System 6th Edition provides comprehensive education on the MOD program. However, consistent and accurate MOD reporting and linkage to tumor registries on a national level will require a multifaceted approach to achieve success. Ultimately, the information gained from the direct role of MOD will result in breast cancer screening recommendations built on science-based policy.

Dynamic light scattering (DLS) provides valuable information on nanoscale and microscale dynamics, but its systematic application to in vivo tissue evaluation remains limited. This study introduces field effect detection by spectral analysis (FEDSA), a frequency-domain approach designed to analyze backscattered light signals and identify tissue abnormalities associated with the field cancerization effect in breast tissue. The objective was to establish a proof of concept showing that FEDSA can differentiate normal from abnormal tissue. A two-stage proof-of-concept study was conducted. First, FEDSA was validated using suspensions of alumina particles (60-300 nm and 100-400 nm) and polystyrene particles (315 nm) to test its performance as a dynamic light scattering technique. Second, in vivo measurements were obtained from 26 women (19 with normal tissue and 7 with abnormal tissue confirmed by imaging or clinical diagnosis). Power spectra were decomposed into frequency bands, transformed through principal component analysis, and analyzed by logistic regression. FEDSA reproduced the expected behavior of a dynamic light scattering-type technique when applied to suspensions of particles. In breast tissue experiments, statistically significant differences were observed between normal and abnormal groups, particularly in the 150-160 kHz frequency band. A PCA-logistic regression model showed discriminatory potential. The ROC analysis yielded an AUC of 0.83; however, cross-validation grouped with patients provided a more conservative performance estimate (AUC &#x2248; 0.68-0.74), supporting the feasibility of the approach while suggesting uncertainty due to the limited cohort size. This proof-of-concept study demonstrates the feasibility of FEDSA as a non-invasive, low-cost, and non-ionizing frequency-domain technique inspired by DLS principles to differentiate normal from abnormal breast tissue. Although further validation with larger and more diverse cohorts is required, these findings suggest the potential of FEDSA as a complementary tool for early breast cancer risk assessment.

While the feasibility of MRI as a supplemental screening tool for breast cancer has been established, there is no consensus regarding the optimal examination protocol. This reader study aims to provide a systematic comparison of abbreviated contrast-enhanced MRI, IV-contrast-free, and a full multiparametric protocol, to diagnose breast cancer in a population of women with mammographically dense breasts. This IRB-approved retrospective reader study was performed in 166 patients with mammographically dense breasts recruited from a tertiary care university hospital. MR images were acquired at 1.5 or 3&#xa0;T MRI units in line with international recommendations. Three blinded off-site readers evaluated the images in three different approaches in a region-wise analysis: (1) full multiparametric protocol, (2) abbreviated first pass MRI protocol, and (3) unenhanced DWI and T2w/STIR images. Histopathology and/or imaging follow-up of at least 24&#xa0;months served as a reference standard. Statistics included generalized estimating equation methodology based logistic regression for repeated measures. 1660 regions (166 women, mean age, 45 +/- 12&#xa0;years) with 41 histologically verified cancers were read. At a BI-RADS cutoff&#xa0;>&#xa0;3, sensitivity was significantly higher (p&#xa0;<&#xa0;0.001) using the full protocol (80.4-90.2%) followed by the abbreviated (70.7-78.1%) and lastly the unenhanced protocol (40.6-53.5%). Specificity was significantly lower (p&#xa0;<&#xa0;0.001) using the abbreviated protocol. Inter-reader agreement was fair to moderate. The full multiparametric protocol demonstrated superior sensitivity compared to the abbreviated and unenhanced protocol, while specificity of the full and unenhanced protocols were superior to the abbreviated protocol.

Breast cancer remains a prevalent health concern, affecting approximately one in eight women during their lifetime. While screening mammography has significantly reduced mortality through early detection, its sensitivity is compromised in women with dense breast tissue-a factor that not only increases cancer risk but also obscures malignancies on imaging. Digital breast tomosynthesis has enhanced screening capabilities over traditional 2D mammography, yet limitations persist for dense breasts. In response, recent US federal legislation mandates that mammography lay summaries inform patients about the implications of breast density, including reduced detection rates and elevated risk. Additionally, insurance coverage for supplemental imaging is expanding across the United States. Supplemental screening modalities such as magnetic resonance imaging, whole-breast ultrasound, contrast-enhanced mammography, and molecular breast imaging offer improved detection in dense tissue, but guidance for average-risk women remains unclear. This lack of consensus can lead to confusion among patients and providers. This article aims to equip clinicians with a comprehensive understanding of breast density's impact on screening efficacy, the available supplemental imaging options, and current societal recommendations. By clarifying these considerations, clinicians can better navigate shared decision-making with average-risk patients regarding breast cancer screening.

Detecting malignancy before gender-affirming chest masculinization surgery (GACMS) can alter surgical planning and prevent reoperation, yet a lack of standardized preoperative breast imaging guidelines has resulted in inconsistent, surgeon-dependent practices and potential missed diagnoses. Limited data evaluating the efficacy of pre-GACMS imaging further contributes to this gap. This study aimed to characterize patterns, indications, and outcomes of preoperative breast imaging before GACMS, and to assess the impact of preoperative imaging on cancer detection, surgical decision-making, and timing to surgery. A single-institution, retrospective review of adults who underwent GACMS between January 2017-September 2024 was conducted. Descriptive statistics summarize preoperative imaging frequency, indications, modalities, outcomes, and postoperative pathology. Alterations in surgical management based on preoperative versus postoperative cancer detection, as well as an institution-wide screening algorithm, are described. Of 368 patients, 91.8% (n&#x2009;=&#x2009;338) were under 40 (mean 27.2, range 18-63). Preoperative breast imaging was recommended in 11.7% (n&#x2009;=&#x2009;43) and performed in 11.1% (n&#x2009;=&#x2009;41). Modalities included screening mammography (70.7%, n&#x2009;=&#x2009;29), diagnostic mammography (29.3%, n&#x2009;=&#x2009;12), MRI (9.8%, n&#x2009;=&#x2009;4), and ultrasound (7.3%, n&#x2009;=&#x2009;3). Indications included age (41.9%, n&#x2009;=&#x2009;18), family history (30.2%, n&#x2009;=&#x2009;13), physical exam finding (23.3%, n&#x2009;=&#x2009;10), and BRCA2 mutation (2.3%, n&#x2009;=&#x2009;1). Imaging revealed irregular findings in 17.1% (n&#x2009;=&#x2009;7), with malignancy confirmed in 2 patients (4.9% of imaged; 0.5% overall). One patient who did not receive preoperative imaging was found to have invasive ductal carcinoma on postoperative pathology, resulting in 0.8% (n&#x2009;=&#x2009;3) overall breast cancer diagnoses perioperatively. Preoperative detection altered surgical planning. Median time to surgery did not significantly differ between imaged and non-imaged patients (3.1 vs. 3.7&#xa0;months, p&#x2009;=&#x2009;0.2). Preoperative breast cancer imaging before GACMS identified malignancies that significantly influenced surgical planning, preventing additional procedures postoperatively. Implementing a decision-making algorithm could guide and standardize breast imaging before GACMS.

Current guidelines for advanced breast cancer do not recommend routine brain imaging in neurologically asymptomatic patients. Prospective clinical evidence on the effectiveness of screening for early detection of brain metastasis (BM) remains limited. We conducted a prospective cohort study to evaluate the utility of magnetic resonance imaging (MRI) screening in patients with advanced human epidermal growth factor receptor 2-positive (HER2+) or triple-negative breast cancer (TNBC). In this single-arm, prospective study, screening brain MRI was carried out at diagnosis in asymptomatic patients with advanced HER2+ or TNBC. Patients without BM on baseline MRI were monitored for the development of neurologic symptoms. Follow-up brain MRI studies were carried out at the initiation of second- and third-line systemic therapy. The primary endpoint was the detection rate of BM on screening MRI. MRI detected asymptomatic BM in 11/112 (9.8%) patients at baseline; the cumulative detection rates increased to 17.0% and 19.6% by the initiation of second- and third-line therapy, respectively. Through this serial screening strategy, two-thirds of all BM cases (22/33) were identified at an asymptomatic stage. Patients with baseline metastatic involvement of three or more organ sites outside the central nervous system had an increased risk of BM (hazard ratio 3.38), and 38.5% of patients in this subgroup were diagnosed with BM by MRI screening. Stereotactic radiosurgery (66.7%) was the most common initial treatment for BM, and the median overall survival after BM diagnosis was 23.3 months. Two-thirds of BM cases in patients with advanced HER2+ or TNBC were diagnosed at an asymptomatic stage in this prospective serial brain MRI screening program.

Female breast cancer still represents a substantial public health obstacle in many countries. The condition remains costly for healthcare providers and imposes heavy burdens on healthcare systems in countries globally. There is a significant gap in information regarding the economic burden of carcinoma of the breast affecting the women of Antigua and Barbuda. Consequently, this research aims to quantify the costs related to female breast carcinoma from the perspective of the healthcare provider in the country. This study employed a prevalence-based cost of illness methodology. Data on female breast cancer were collected from four research sites in Antigua and Barbuda for the years 2017 to 2021 to calculate the average yearly prevalence. Both top down and bottom up costing methods were employed to compute direct medical costs, using the price levels from 2021 and converting the amounts to United States Dollars. Estimated total annual direct medical costs for female breast carcinoma was USD3.1 million. Treatment for clinical stages I to IV accounted for 78% of costs. Our leading contributors to annual direct medical costs were treatment (USD2,458,305.82), post-treatment care (USD390,474.79), and diagnosis and imaging (USD143,045.38). Overall direct medical unit costs was estimated at USD177,618.02, with lead drivers being surgery, systemic therapy, and 'other complications of treatment'. Our study presented findings regarding direct medical costs of female breast carcinoma in Antigua and Barbuda. Our cost estimates appeared considerable given the local context. These findings provided a reference for informing health policy, advising on resources allocation, and encouraging cost containment in female breast cancer management in Antigua and Barbuda.

This article provides a comprehensive overview of breast cancer screening guidelines and the evolving role of established and emerging imaging technologies. It reviews the proven mortality benefit of mammography, the widespread adoption of digital breast tomosynthesis, and the importance of MR imaging for high-risk populations. Supplemental modalities such as abbreviated MR imaging, contrast-enhanced mammography, whole-breast ultrasound, and molecular breast imaging are discussed, with emphasis on their advantages, limitations, and appropriate use. The article also explores the expanding role of artificial intelligence in improving screening accuracy, efficiency, and individualized risk assessment, highlighting future directions in personalized screening.

The current pathological diagnosis of lymph node metastasis is time-consuming, labor-intensive, and dependent on sectioning of paraffin blocks. Herein, in a prospective cohort of patients with breast cancer, we validated dynamic full-field optical coherence tomography (D-FFOCT), a virtual pathology tool integrating deep learning for nodal metastasis detection, and offering rapid and label-free histologic approximations of fresh tissues. In a prospective dual-center cohort of 155 patients with breast cancer, 747 freshly bisected lymph node slides were obtained via D-FFOCT. Surgeons interpreted each slide with histopathology as the gold standard. A deep learning model was trained on 28,911 patches (corresponding to 590 slides) and tested on 7,736 patches (corresponding to 157 slides). The results were mapped to the slide level for potential intraoperative evaluation. D-FFOCT strongly correlated with hematoxylin and eosin (H&E)-stained histological images. Surgeons achieved 97.10% specificity in nodal diagnosis with D-FFOCT. The performance of the artificial intelligence (AI) model was not inferior to that of human experts and had a sensitivity/specificity of 87.88%/91.94% and an area under the receiver operating characteristic curve of 0.899 at the slide level. The human-AI collaborative system reduced labor requirements by 75% and increased the specificity by 6.5%, to 98.39%. D-FFOCT has excellent potential as a tool for assessing lymph node metastatic status without tissue preparation or consumption. The integration of D-FFOCT with deep learning decreases labor demands and maintains high accuracy, thereby enabling streamlined nodal prediction independent of routine pathology procedures.

Rapid adoption of artificial intelligence methods in breast imaging research emphasizes the need for large, appropriately curated image databases for development and validation. For digital breast tomosynthesis (DBT), there are few public databases with only limited lesion annotation. Recently, we have developed Malm&#xf6; Breast ImaginG (M-BIG), a large database of 104&#xa0;791 women screened at Sk&#xe5;ne University Hospital, Malm&#xf6;. M-BIG also includes all images from the Malm&#xf6; Breast Tomosynthesis Screening Trial, MBTST of 14&#xa0;848 women, with 139 biopsy-confirmed cancers from DBT screening. To annotate lesions in M-BIG, we designed a semi-automated custom software tool for DBT, and corresponding digital mammography (DM) images. A reader manually draws an outline; or marks nodes around the lesion which are automatically connected by an edge-following algorithm. Our custom tool enables detailed annotation of DBT and DM lesions, as opposed to the rectangular regions present in other published material, allowing extensive evaluation of tumor segmentation, and analysis of size and shape descriptors.

This study aimed to incorporate clinicopathological, conventional ultrasound (US) and contrast-enhanced US (CEUS) imaging features to establish a predictive model for evaluating recurrence-free survival (RFS) in patients with invasive ductal carcinoma (IDC) of the breast. Patients confirmed with IDC in our hospital between 2016 and 2020 were retrospectively analysed. We performed Cox regression analyses based on the clinicopathological and USdata to identify independent factors. A nomogram model was constructed and verified to predict the 1-, 3-, and 5-year RFS. Nomogram performance, calibration, and clinical applicability were evaluated with the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. A total of 377 patients were included and divided into training group (n=226) and validation group (n=151). Axillary lymph node burden, oestrogen receptor status, colour Doppler flow imaging blood flow, maximum length diameter on CEUS, and perfusion defects were independent risk factors for poor RFS. The AUC values of nomogram model were 0.876, 0.823, and 0.753 in the training group and 0.731, 0.763, and 0.714 in the validation group. The calibration curves exhibited good concordance between the predicted survival probabilities and the actual values. The nomogram, which integrates clinicopathological and US features, serves as a supplementary prognostic tool for IDC patients. It offers individualised risk stratification to support clinical decision-making, potentially minimising interventions for low-risk patients and prioritising monitoring for high-risk cases, while complementing traditional staging systems.

High mammographic density is a well-known risk factor for breast cancer and reduces the sensitivity of mammography-based screening. While automated machine and deep learning-based methods provide more consistent and precise measurements compared to subjective Breast Imaging Reporting and Data System (BI-RADS) assessments, they often fail to account for the longitudinal evolution of density. Many of these methods assess mammographic density in a cross-sectional manner, overlooking correlations in repeated measures, irregular visit intervals, missing data, and informative dropouts. Joint models address these limitations by simultaneously modeling the relationship between longitudinal biomarkers and time-to-event outcomes. We introduce the DeepJoint algorithm, an open-source method combining deep learning-based mammographic density estimation with joint modeling to assess its longitudinal relationship with breast cancer risk. Our approach adequately analyzes processed mammograms from various manufacturers, estimating both dense area and percent density, two established risk factors for breast cancer. We utilize a joint model to explore their association with breast cancer risk and provide individualized risk predictions. Bayesian inference and the consensus Monte Carlo algorithm make the approach reliable for large screening datasets. By integrating deep learning with joint modeling, our new method provides a robust, comprehensive framework for evaluating breast cancer risk based on longitudinal density profiles. The complete pipeline is publicly available, promoting broader application and comparison with other&#xa0;methods.

Breast cancer is the most commonly diagnosed cancer in women, and a major cause of cancer-related mortality. Neoadjuvant chemotherapy (NAC) is a key treatment for locally advanced breast cancer, but the response varies among patients. Radiomics extracts quantitative imaging features from computed tomography (CT), and thus offers a non-invasive approach to predict NAC response. This systematic review evaluates the predictive performance of CT-based radiomics models and their clinical applicability. Following PRISMA guidelines, a comprehensive literature search was conducted of PubMed, EMBASE, and Cochrane CENTRAL. Studies assessing CT-based radiomics for NAC response prediction in breast cancer were included. Key outcomes included area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity. Thirteen studies, prospective and retrospective, with a range of 39-549 patients were included. Machine learning models including LASSO regression and convolutional neural networks, were used in the studies. AUC values ranged from 64 % to 100 %, with deep learning models achieving superior performance. Hybrid models integrating clinical and radiomic features outperformed radiomics alone. Sensitivity and specificity varied, with deep learning models generally having better specificity, and hybrid models better sensitivity. In conclusion, CT-based radiomics shows promise in predicting NAC response in breast cancer, particularly with deep learning and hybrid models. However, variability in feature selection and validation methods highlight the need for standardization. Future research should focus on integrating multimodal imaging and clinical data to enhance predictive reliability.

Surgical resection remains the first-line treatment for breast cancer, underscoring the need for precision tools for intraoperative margin delineation. Due to high heterogeneity, accurate identification of tumor margins poses a significant challenge in breast cancer. Herein, we report a rapid-clearance near-infrared fluorescent probe, Cy-JW (&#x3bb;ab = 775 nm, &#x3bb;em = 808 nm, compatible with current clinical intraoperative fluorescence imaging devices), which can target the tumor-overexpressed enzyme KIAA1363. Cy-JW exhibited high aqueous solubility and potent affinity for KIAA1363 (IC50 = 36.90 nM) and enabled clear visualization in three representative breast cancer cell lines. In mouse xenograft models, both intratumoral and intravenous administration yielded high tumor-to-background ratios (TBR > 5), supporting complete and precise tumor resection. Biodistribution and safety evaluations indicated rapid plasma clearance (t1/2 < 0.5 h) and predominant renal excretion within 48 h, suggesting a favorable safety profile with a reduced risk of systemic toxicity. Furthermore, preclinical assessment of freshly resected specimens from breast cancer patients (across three subtypes) using a clinical fluorescence imaging device showed strong agreement between fluorescence-guided demarcation and histopathological analysis. Collectively, these results establish Cy-JW as a promising agent for fluorescence-guided surgery (FGS) applications across diverse breast cancer subtypes.

Reconciling cutoff thresholds for short-term (5-year) and long-term (lifetime) breast cancer risk could support tailored and evidence-based approaches to supplemental screening and risk management most relevant to short-term clinical actions. This study aims to consistently classify women at increased risk and provide 5-year risk cutoff that corresponds to a 20% lifetime risk. Using U.S. Surveillance, Epidemiology and End Results (SEER) program population incidence data for women 40 to 74 years of age, this study reports both lifetime and 5-year population-based risk estimates controlling for competing risk and age varying breast cancer incidence. A cut point for 5-year risk equivalent to lifetime risk of 20% which triggers increased screening is generated. This computation is a weighted average incorporating age, remaining life expectancy, and population risk distribution. The primary outcome is breast cancer incidence (in situ and invasive). A lifetime risk threshold of 20% corresponded to markedly age-dependent 5-year risk cut points, increasing from &#x223c;1.3% at ages 40-44 to &#x223c;10.9% at ages 70-74. For women 40-74, 20% lifetime risk corresponds to a 5-year risk cut-off of 3.16%. Aligning lifetime risk of &#x2265;20% and the 5-year breast cancer risk cutoff enhances consistency of classification of women at increased risk and clinical decision-making. Women with a &#x2265;3.16% 5-year risk of breast cancer have risk equivalent to a lifetime risk of &#x2265;20% on average. This can facilitate rational and evidence-based approaches to short-term and long-term risk assessment results for both risk reduction and tailored screening.

Early detection remains critical for reducing breast-cancer mortality, yet millions of women worldwide, particularly those in low-resource, rural, or underserved communities, face significant barriers to screening. Clinic-based imaging modalities such as mammography, ultrasound, and MRI require specialized infrastructure, trained personnel, and in-person attendance, contributing to persistent underscreening and sometimes late-stage diagnoses. Feminai is a disposable, wearable self-breast-examination patch that integrates heat, blood flow, tissue conductivity, and density sensing, with AI-driven analysis. This study evaluates the device's accuracy in identifying abnormal breast findings. This prospective, noninterventional validation study enrolled 150 women aged 25 to 75 undergoing breast-cancer screening at the "Merav" Clinic, Tel Hashomer Hospital. Participants completed a medical questionnaire and underwent a 5-minute scan using the Feminai wearable patch. All participants underwent standard imaging with mammography, with diagnostic ultrasound or MRI as indicated. Sensor-derived heat and tissue conductivity data were analyzed using a proprietary AI algorithm and compared against radiological assessments and biopsy results. Among 150 women (mean age 49 years), screening mammography identified 75 as BI-RADS 1 to 2, and 75 as BI-RADS 4 to 5. The Feminai device identified 70 of 75 BI-RADS 4 to 5 cases as suspicious, correctly detecting all biopsy-proven malignant lesions and five benign cases as nonsuspicious, corresponding to 96% sensitivity, 82% specificity, and 98% negative predictive value. The Feminai Breast Examination Kit showed high accuracy, particularly in sensitivity and NPV, indicating strong potential as a remote, cost-effective, and user-friendly early-detection tool. By enabling self-administered screening without reliance on specialized facilities, Feminai offers a scalable pathway to improve access in rural settings, low-resource communities, and medically underserved populations, groups most vulnerable to delayed diagnosis. Further large-scale validation is warranted, but these findings support its promise as an impactful addition to global breast-cancer screening strategies.