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Abstract: Purpose of Review: The purpose of this paper is to review a novel technique for stereotactic-guided breast biopsies utilizing the lateral arm approach. Recent Findings: Successful stereotactic-guided biopsies require careful consideration of lesion location and patient anatomy. Cases that cannot be safely performed are often referred for surgical biopsy, which can increase patient anxiety and delay diagnosis. The lateral arm approach provides an alternative needle approach for biopsy that can mitigate some of the most common exclusion criteria for standard techniques. Although there are many benefits to the lateral arm approach, it has not yet been widely adopted. This article reviews the lateral arm approach, including patient selection, procedural technique, advantages, and disadvantages. Case examples utilizing the technique will also be discussed to illustrate our institutional experience (including procedural tips) and how the lateral arm approach can be integrated into breast imaging practice. Summary: The lateral arm approach can expand the patient population that can undergo stereotactic-guided breast biopsy, which may obviate the need for surgical biopsy or short-term follow-up. The technique also allows for a creative approach to biopsy in challenging cases. Proficiency with the lateral arm approach can expand a breast imager’s interventional skills, improve patient care, and allow for more expedient diagnosis of suspicious breast findings.

Angiosarcoma (AS) of the breast is a rare malignancy arising from the vascular endothelium. It can develop as a primary tumor or as a secondary tumor, the latter of which is most commonly associated with prior radiation therapy. AS may present in the breast as a palpable mass, unilateral diffuse breast enlargement, skin thickening, erythema, plaque-like violaceous discoloration, or painful nodules. The imaging features of AS vary across imaging modalities. Because AS of the breast is aggressive and prone to early metastasis, prompt detection is crucial to the improvement of generally low five-year survival rates. In this paper, we share one case of AS of the breast and one of a common mimic, hemangioma, with associated imaging and key clinical details that can aid radiologists in the timely identification and diagnosis of AS of the breast.

Tubular adenomas are rare, benign breast tumors primarily found in postmenarchal, premenopausal women. We report a case of tubular adenoma in a 56-year-old man with a history of alcoholic cirrhosis. The breast mass was incidentally discovered on computed tomography imaging performed as part of a patient liver transplant evaluation. Dedicated breast imaging and ultrasound-guided biopsy confirmed the diagnosis of tubular adenoma.

Breast cancer is a leading cause of mortality among women globally, necessitating precise classification of breast ultrasound images for early diagnosis and treatment. Traditional methods using CNN architectures such as VGG, ResNet, and DenseNet, though somewhat effective, often struggle with class imbalances and subtle texture variations, leading to reduced accuracy for minority classes such as malignant tumors. To address these issues, we propose a methodology that leverages EfficientNet-B7, a scalable CNN architecture, combined with advanced data augmentation techniques to enhance minority class representation and improve model robustness. Our approach involves fine-tuning EfficientNet-B7 on the BUSI dataset, implementing RandomHorizontalFlip, RandomRotation, and ColorJitter to balance the dataset and improve model robustness. The training process includes early stopping to prevent overfitting and optimize performance metrics. Additionally, we integrate Explainable AI (XAI) techniques, such as Grad-CAM, to enhance the interpretability and transparency of the model's predictions, providing visual and quantitative insights into the features and regions of ultrasound images influencing classification outcomes. Our model achieves a classification accuracy of 99.14%, significantly outperforming existing CNN-based approaches in breast ultrasound image classification. The incorporation of XAI techniques enhances our understanding of the model's decision-making process, thereby increasing its reliability and facilitating clinical adoption. This comprehensive framework offers a robust and interpretable tool for the early detection and diagnosis of breast cancer, advancing the capabilities of automated diagnostic systems and supporting clinical decision-making processes. [ABSTRACT FROM AUTHOR]

Optoacoustic imaging (OAI) is an emerging field with increasing applications in patients and exploratory clinical trials for breast cancer. Optoacoustic imaging (or photoacoustic imaging) employs non-ionizing, laser light to create thermoelastic expansion in tissues and detect the resulting ultrasonic emission. By combining high optical contrast capabilities with the high spatial resolution and anatomic detail of grayscale ultrasound, OAI offers unique opportunities for visualizing biological function of tissues in vivo. Over the past decade, human breast applications of OAI, including benign/malignant mass differentiation, distinguishing cancer molecular subtype, and predicting metastatic potential, have significantly increased. We discuss the current state of optoacoustic breast imaging, as well as future opportunities and clinical application trends. Clinical relevance statement: Optoacoustic imaging is a novel breast imaging technique that enables the assessment of breast cancer lesions and tumor biology without the risk of ionizing radiation exposure, intravenous contrast, or radionuclide injection. Key Points: • Optoacoustic imaging (OAI) is a safe, non-invasive imaging technique with thriving research and high potential clinical impact. • OAI has been considered a complementary tool to current standard breast imaging techniques. • OAI combines parametric maps of molecules that absorb light and scatter acoustic waves (like hemoglobin, melanin, lipids, and water) with anatomical images, facilitating scalable and real-time molecular evaluation of tissues. [ABSTRACT FROM AUTHOR]

Breast cancer screening trends of transgender and gender diverse (TGD) people remain largely unknown. This is concerning, as data are necessary to inform recommendations made by clinicians to their patients and by national guidelines to clinicians. The aim of this review is to explore the state of existing research literature and provide a summary report of current breast cancer screening rates in TGD adults. All articles were identified using Medical Subject Headings terms. Inclusion criteria were all the following: (1) documents inclusion of at least one participant who identifies as a TGD person; (2) at least one TGD participant with top surgery or currently receiving estrogen-based gender-affirming hormone therapy; (3) results that report rates of breast cancer screening or mammogram referral. Twelve articles met inclusion criteria, six cross-sectional studies and six retrospective chart reviews. Three studies conducted secondary analysis of the Behavioral Risk Factor Surveillance System national dataset, and nine articles recruited their own sample with number of TGD participants ranging from 30 to 1,822 and number of cisgender women ranging from 242 to 18,275. Three studies found lower rates of screening in transfeminine persons receiving gender-affirming care compared with cisgender women; two studies found lower rates among TGD people compared with cisgender women; and three studies found no differences between the breast cancer screening rates of TGD and cisgender participants. Limited studies recruit and report trends in breast cancer screening of TGD people. Those that do include TGD participants have mixed results, but overall TGD people had lower rates of breast cancer screening. More research is needed regarding breast cancer screening of TGD people to inform the development of protocols that ensure equitable access to preventative care. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: For time‐consuming diffusion‐weighted imaging (DWI) of the breast, deep learning‐based imaging acceleration appears particularly promising. Purpose: To investigate a combined k‐space‐to‐image reconstruction approach for scan time reduction and improved spatial resolution in breast DWI. Study Type: Retrospective. Population: 133 women (age 49.7 ± 12.1 years) underwent multiparametric breast MRI. Field Strength/Sequence: 3.0T/T2 turbo spin echo, T1 3D gradient echo, DWI (800 and 1600 sec/mm2). Assessment: DWI data were retrospectively processed using deep learning‐based k‐space‐to‐image reconstruction (DL‐DWI) and an additional super‐resolution algorithm (SRDL‐DWI). In addition to signal‐to‐noise ratio and apparent diffusion coefficient (ADC) comparisons among standard, DL‐ and SRDL‐DWI, a range of quantitative similarity (e.g., structural similarity index [SSIM]) and error metrics (e.g., normalized root mean square error [NRMSE], symmetric mean absolute percent error [SMAPE], log accuracy error [LOGAC]) was calculated to analyze structural variations. Subjective image evaluation was performed independently by three radiologists on a seven‐point rating scale. Statistical Tests: Friedman's rank‐based analysis of variance with Bonferroni‐corrected pairwise post‐hoc tests. P < 0.05 was considered significant. Results: Both DL‐ and SRDL‐DWI allowed for a 39% reduction in simulated scan time over standard DWI (5 vs. 3 minutes). The highest image quality ratings were assigned to SRDL‐DWI with good interreader agreement (ICC 0.834; 95% confidence interval 0.818–0.848). Irrespective of b‐value, both standard and DL‐DWI produced superior SNR compared to SRDL‐DWI. ADC values were slightly higher in SRDL‐DWI (+0.5%) and DL‐DWI (+3.4%) than in standard DWI. Structural similarity was excellent between DL‐/SRDL‐DWI and standard DWI for either b value (SSIM ≥ 0.86). Calculation of error metrics (NRMSE ≤ 0.05, SMAPE ≤ 0.02, and LOGAC ≤ 0.04) supported the assumption of low voxel‐wise error. Data Conclusion: Deep learning‐based k‐space‐to‐image reconstruction reduces simulated scan time of breast DWI by 39% without influencing structural similarity. Additionally, super‐resolution interpolation allows for substantial improvement of subjective image quality. Evidence Level: 4 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Background: The objective of this study was to quantify disparities in cancer treatment delivery between minority‐serving hospitals (MSHs) and non‐MSHs for breast, prostate, nonsmall cell lung, and colon cancers from 2010 to 2019 and to estimate the impact of improving care at MSHs on national disparities. Methods: Data from the National Cancer Database (2010–2019) identified patients who were eligible for definitive treatments for the specified cancers. Hospitals in the top decile by minority patient proportion were classified as MSHs. Multivariable logistic regression adjusted for patient and hospital characteristics compared the odds of receiving definitive treatment at MSHs versus non‐MSHs. A simulation was used to estimate the increase in patients receiving definitive treatment if MSH care matched the levels of non‐MSH care. Results: Of 2,927,191 patients from 1330 hospitals, 9.3% were treated at MSHs. MSHs had significant lower odds of delivering definitive therapy across all cancer types (adjusted odds ratio: breast cancer, 0.83; prostate cancer, 0.69; nonsmall cell lung cancer, 0.73; colon cancer, 0.81). No site of care–race interaction was significant for any of the cancers (p >.05). Equalizing treatment rates at MSHs could result in 5719 additional patients receiving definitive treatment over 10 years. Conclusions: The current findings underscore systemic disparities in definitive cancer treatment delivery between MSHs and non‐MSHs for breast, prostate, nonsmall cell lung, and colon cancers. Although targeted improvements at MSHs represent a critical step toward equity, this study highlights the need for integrated, system‐wide efforts to address the multifaceted nature of racial and ethnic health disparities. Enhancing care at MSHs could serve as a pivotal strategy in a broader initiative to achieve health care equity for all. Minority‐serving hospitals (MSHs) consistently show lower odds of delivering definitive treatment for breast, prostate, non–small cell lung, and colon cancers than non‐MSHs. By aligning treatment rates of MSHs with those of non‐MSHs, additional patients could receive definitive care, potentially reducing racial and ethnic disparities in US cancer outcomes. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate a high-resolution deep-learning (DL)-based diffusion-weighted imaging (DWI) sequence for breast magnetic resonance imaging (MRI) in comparison to a standard DWI sequence (DWIStd) at 1.5 T. It is a prospective study of 38 breast cancer patients, who were scanned with DWIStd and DWIDL. Both DWI sequences were scored for image quality, sharpness, artifacts, contrast, noise, and diagnostic confidence with a Likert-scale from 1 (non-diagnostic) to 5 (excellent). The lesion diameter was evaluated on b 800 DWI, apparent diffusion coefficient (ADC), and the second subtraction (SUB) of the contrast-enhanced T1 VIBE. SNR was also calculated. Statistics included correlation analyses and paired t-tests. High-resolution DWIDL offered significantly superior image quality, sharpness, noise, contrast, and diagnostic confidence (each p < 0.02)). Artifacts were significantly higher in DWIDL by one reader (M = 4.62 vs. 4.36 Likert scale, p < 0.01) without affecting the diagnostic confidence. SNR was higher in DWIDL for b 50 and ADC maps (each p = 0.07). Acquisition time was reduced by 22% in DWIDL. The lesion diameters in DWI b 800DL and Std and ADCDL and Std were respectively 6% lower compared to the 2nd SUB. A DL-based diffusion sequence at 1.5 T in breast MRI offers a higher resolution and a faster acquisition, including only minimally more artefacts without affecting the diagnostic confidence. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the rate of axillary node–positive disease in patients with early breast cancer who had a suspicious axillary lymph node on radiation planning computed tomography (CT). A retrospective review was conducted of the medical records of all patients with breast cancer who were referred for axillary ultrasound from the radiation unit to the breast imaging unit at the Meirav Breast Center, Sheba Medical Center, from 2012 to 2022. Ethics approval was obtained. Only the records of patients who were referred due to an abnormal axillary lymph node seen on radiation planning CT were further evaluated. During the study period, a total of 21 patients were referred to the breast imaging unit for evaluation of suspicious nodes seen on radiation planning CT. Of these, 3 cases were excluded. A total of 15 out of the 18 (83%) patients included had an abnormal lymph node in the ultrasound, and an ultrasound-guided biopsy was recommended (BI-RADS 4). Of these, 3 (out of 15, 20%) had a positive biopsy for tumor cells from the axillary lymph node. Two were cases after primary systemic therapy without complete pathologic response. Thickening of the lymph node cortex and complete loss of the central fatty hilum were associated with pathologic lymph node. Sonar had limited ability to differentiate reactive nodes from involved nodes. The presence of lymph nodes with loss of cortical-hilum differentiation on ultrasound together with clinical features are parameters that can help guide the need of further biopsy. Histopathology evaluation is important to make the diagnosis of residual axillary disease. Future studies and guidelines are needed to improve the diagnostic abilities and reduce the number of patients who are undergoing biopsy for noninvolved nodes. [ABSTRACT FROM AUTHOR]

The main aim of this study was to determine whether the use of contrast-enhanced ultrasound (CEUS) could improve the categorization of suspicious breast lesions based on the Breast Imaging Reporting and Data System (BI-RADS), thereby reducing the number of benign breast lesions referred for biopsy. This prospective study, conducted between January 2017 and December 2018, enrolled consenting patients from eight teaching hospitals in China, who had been diagnosed with solid breast lesions classified as BI-RADS 4 using conventional ultrasound. CEUS was performed within 1 wk of diagnosis for reclassification of breast lesions. Histopathological results obtained from core needle biopsies or surgical excision samples served as the reference standard. The simulated biopsy rate and cancer-to-biopsy yield were used to compare the accuracy of CEUS and conventional ultrasound (US). Among the 1490 lesions diagnosed as BI-RADS 4 with conventional ultrasound, 486 malignant and 1004 benign lesions were confirmed based on histology. Following CEUS, 2, 395, and 211 lesions were reclassified as CEUS-based BI-RADS 2, 3, and 5, respectively, while 882 (59%) remained as BI-RADS 4. The actual cancer-to-biopsy yield based on US was 32.6%, which increased to 43.4% when CEUS-based BI-RADS 4A was used as the cut-off point to recommend biopsy. The simulated biopsy rate decreased to 73.4%. Overall, in this preselected BI-RADS 4 population, only 2.5% (12/486) of malignant lesions would have been miscategorized as BI-RADS 3 using CEUS-based reclassification. The diagnostic accuracy, sensitivity, and specificity of contrast-enhanced ultrasound reclassification were 57.65%, 97.53%, and 38.35%, respectively. Our collective findings indicate that CEUS is a valuable tool in further triage of BI-RADS category 4 lesions and facilitates a reduction in the number of biopsies while increasing the cancer-to-biopsy yield. [ABSTRACT FROM AUTHOR]

Objectives: To perform a survey among members of the European Society of Breast Imaging (EUSOBI) regarding the use of contrast-enhanced mammography (CEM). Methods: A panel of nine board-certified radiologists developed a 29-item online questionnaire, distributed to all EUSOBI members (inside and outside Europe) from January 25 to March 10, 2023. CEM implementation, examination protocols, reporting strategies, and current and future CEM indications were investigated. Replies were exploratively analyzed with descriptive and non-parametric statistics. Results: Among 434 respondents (74.9% from Europe), 50% (217/434) declared to use CEM, 155/217 (71.4%) seeing less than 200 CEMs per year. CEM use was associated with academic settings and high breast imaging workload (p < 0.001). The lack of CEM adoption was most commonly due to the perceived absence of a clinical need (65.0%) and the lack of resources to acquire CEM-capable systems (37.3%). CEM protocols varied widely, but most respondents (61.3%) had already adopted the 2022 ACR CEM BI-RADS® lexicon. CEM use in patients with contraindications to MRI was the most common current indication (80.6%), followed by preoperative staging (68.7%). Patients with MRI contraindications also represented the most commonly foreseen CEM indication (88.0%), followed by the work-up of inconclusive findings at non-contrast examinations (61.5%) and supplemental imaging in dense breasts (53.0%). Respondents declaring CEM use and higher CEM experience gave significantly more current (p = 0.004) and future indications (p < 0.001). Conclusions: Despite a trend towards academic high-workload settings and its prevalent use in patients with MRI contraindications, CEM use and progressive experience were associated with increased confidence in the technique. Clinical relevance statement: In this first survey on contrast-enhanced mammography (CEM) use and perspectives among the European Society of Breast Imaging (EUSOBI) members, the perceived absence of a clinical need chiefly drove the 50% CEM adoption rate. CEM adoption and progressive experience were associated with more extended current and future indications. Key Points: • Among the 434 members of the European Society of Breast Imaging who completed this survey, 50% declared to use contrast-enhanced mammography in clinical practice. • Due to the perceived absence of a clinical need, contrast-enhanced mammography (CEM) is still prevalently used as a replacement for MRI in patients with MRI contraindications. • The number of current and future CEM indications marked by respondents was associated with their degree of CEM experience. [ABSTRACT FROM AUTHOR]

Objectives: To assess the diagnostic performance of 3D automated breast ultrasound (3D-ABUS) in breast cancer screening in a clinical setting. Materials and methods: All patients who had 3D-ABUS between January 2014 and January 2022 for screening were included in this retrospective study. The images were reported by 1 of 6 breast radiologists based on the Breast Imaging Reporting and Data Systems (BI-RADS). The 3D-ABUS was reviewed together with the digital breast tomosynthesis (DBT). Recall rate, biopsy rate, positive predictive value (PPV) and cancer detection yield were calculated. Results: In total, 3616 studies were performed in 1555 women (breast density C/D 95.5% (n = 3455/3616), breast density A/B 4.0% (n = 144/3616), density unknown (0.5% (n = 17/3616)). A total of 259 lesions were detected on 3D-ABUS (87.6% (n = 227/259) masses and 12.4% (n = 32/259) architectural distortions). The recall rate was 5.2% (n = 188/3616) (CI 4.5–6.0%) with only 36.7% (n = 69/188) cases recalled to another date. Moreover, recall declined over time. There were 3.4% (n = 123/3616) biopsies performed, with 52.8% (n = 65/123) biopsies due to an abnormality detected in 3D-ABUS alone. Ten of 65 lesions were malignant, resulting in a positive predictive value (PPV) of 15.4% (n = 10/65) (CI 7.6–26.5%)). The cancer detection yield of 3D-ABUS is 2.77 per 1000 screening tests (CI 1.30–5.1). Conclusion: The cancer detection yield of 3D-ABUS in a real clinical screening setting is comparable to the results reported in previous prospective studies, with lower recall and biopsy rates. 3D-ABUS also may be an alternative for screening when mammography is not possible or declined. Clinical relevance statement: 3D automated breast ultrasound screening performance in a clinical setting is comparable to previous prospective studies, with better recall and biopsy rates. Key Points: • 3D automated breast ultrasound is a reliable and reproducible tool that provides a three-dimensional representation of the breast and allows image visualisation in axial, coronal and sagittal. • The diagnostic performance of 3D automated breast ultrasound in a real clinical setting is comparable to its performance in previously published prospective studies, with improved recall and biopsy rates. • 3D automated breast ultrasound is a useful adjunct to mammography in dense breasts and may be an alternative for screening when mammography is not possible or declined. [ABSTRACT FROM AUTHOR]

Breast cancer is a complex disease that includes entities with different characteristics, behaviors, and responses to treatment. Breast cancers are categorized into subgroups based on histological type and grade, and these subgroups affect clinical presentation and oncological outcomes. The subgroup of "special types" encompasses all those breast cancers with insufficient features to belong to the subgroup "invasive ductal carcinoma not otherwise specified". These cancers account for around 25% of all cases, some of them having a relatively good prognosis despite high histological grade. The purpose of this paper is to review and illustrate the radiological appearance of each special type, highlighting insights and pitfalls to guide breast radiologists in their routine work. [ABSTRACT FROM AUTHOR]

In recent years, material extrusion-based additive manufacturing, particularly fused filament fabrication (FFF), has gained significant attention due to its versatility and cost-effectiveness in producing complex geometries. This paper presents the characterization of seven novel materials for FFF and twenty-two commercially available filaments in terms of X-ray computed tomography (CT) numbers, as tissue mimicking materials for the realization of 3D printed radiological phantoms. Two technical approaches, by 3D printing of cube samples and by producing cylinders of melted materials, are used for achieving this goal. Results showed that the CT numbers, given in Hounsfield unit (HU), of all the samples depended on the beam kilovoltage (kV). The CT numbers ranged from +411 HU to +3071 HU (at 80 kV), from −422 HU to +3071 HU (at 100 kV), and from −442 HU to +3070 HU (at 120 kV). Several commercial and custom-made filaments demonstrated suitability for substituting soft and hard human tissues, for realization of 3D printed phantoms with FFF in CT imaging. For breast imaging, an anthropomorphic phantom with two filaments could be fabricated using ABS-C (conductive acrylonitrile butadiene styrene) as a substitute for breast adipose tissue, and ASA-A (acrylic styrene acrylonitrile) for glandular breast tissue. [ABSTRACT FROM AUTHOR]

Contrast-enhanced breast magnetic resonance imaging (MRI) is currently recommended as a screening tool for high-risk women and has been advocated for women with radiologically dense breast tissue. While breast MRI is acknowledged for its high sensitivity (with an exception for lower-grade ductal carcinoma in situ (DCIS) where emerging techniques like diffusion-weighted imaging offer improvement), its limitations include sensitivity to hormonal changes and a relatively high false-positive rate, potentially leading to overdiagnosis, increased imaging uncertainty, and unnecessary biopsies. These factors can exacerbate patient anxiety and impose additional costs. Molecular imaging with breast-targeted Positron Emission Tomography (PET) has shown the capability to detect malignancy independent of breast density and hormonal changes. Furthermore, breast-targeted PET has shown higher specificity when compared with MRI. However, traditional PET technology is associated with high radiation dose, which can limit its widespread use particularly in repeated studies or for undiagnosed patients. In this case report, we present a clinical application of low-dose breast imaging utilizing a breast-targeted PET camera (Radialis PET imager, Radialis Inc). The case involves a 33-year-old female patient who had multiple enhanced lesions detected on breast MRI after surgical removal of a malignant phyllodes tumor from the right breast. A benign core biopsy was obtained from the largest lesion seen in the left breast. One month after the MRI, 18F-fluorodeoxyglucose (18F-FDG) PET imaging session was performed using the Radialis PET Imager. Although the Radialis PET Imager has proven high count sensitivity and the capability to detect breast lesions with low metabolic activity (at a dose similar to mammography), no areas of increased 18F-FDG uptake were visualized in this particular case. The patient underwent a right-sided nipplesparing mastectomy and left-sided lumpectomy, with bilateral reconstruction. The excised left breast tissue was completely benign, as suggested by both core biopsy and the PET results. The case presented highlights a promising clinical application of low-dose breast-targeted PET imaging to mitigate the uncertainty associated with MRI while keeping radiation doses within the safe range typically used in X-ray mammography. [ABSTRACT FROM AUTHOR]

Showing the mammogram images in different view enables the radiological to recognize more early details about the cancer tumour. The synthetic view was addressed to find the overlapping among the overlapped structural information of the x-ray breast mammography images. The details of the proposed method to produce a synthetic mammography image using the x-ray breast cancer mammogram images from each CranioCaudual (CC) and MedioLateral-Oblique (MLO) view have been introduced in this article. There are five phases to the proposed method. In the first phase, the cancer tumor has been identified in the full-field x-ray mammography images. The cancerous tumor has been secondly recognized and highlighted as a region of interest. A technique to collect data from the cancer tumor and identify the areas of interest on the cancer mass has been discussed in the third phase. In the fourth phase, formatting the points of interest within the context of advanced features has been implemented. The final phase demonstrates how to execute the synthetic digital process using the supervised learning methodology. Several contributions have been presented based on the proposed method, which are facilitate the computer aided detection application in context of the digital breast Tomosynthesis. Compared with other method, the proposed method has shown its ability to save memory and running time resources. Beside of the digital synthetic view, the proposed method was detected the breast tumour in x-ray breast mammography images. [ABSTRACT FROM AUTHOR]

Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1–15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives. [ABSTRACT FROM AUTHOR]

To explore the diagnostic efficacy of tomosynthesis spot compression (TSC) compared with conventional spot compression (CSC) for ambiguous findings on full-field digital mammography (FFDM). In this retrospective study, 122 patients (including 108 patients with dense breasts) with ambiguous FFDM findings were imaged with both CSC and TSC. Two radiologists independently reviewed the images and evaluated lesions using the Breast Imaging Reporting and Data System. Pathology or at least a 1-year follow-up imaging was used as the reference standard. Diagnostic efficacies of CSC and TSC were compared, including area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The mean glandular dose was recorded and compared for TSC and CSC. Of the 122 patients, 63 had benign lesions and 59 had malignant lesions. For Reader 1, the following diagnostic efficacies of TSC were significantly higher than those of CSC: AUC (0.988 vs. 0.906, P = 0.001), accuracy (93.4% vs. 77.8%, P = 0.001), specificity (87.3% vs. 63.5%, P = 0.002), PPV (88.1% vs. 70.5%, P = 0.010), and NPV (100% vs. 90.9%, P = 0.029). For Reader 2, TSC showed higher AUC (0.949 vs. 0.909, P = 0.011) and accuracy (83.6% vs. 71.3%, P = 0.022) than CSC. The mean glandular dose of TSC was higher than that of CSC (1.85 ± 0.53 vs. 1.47 ± 0.58 mGy, P < 0.001) but remained within the safety limit. TSC provides better diagnostic efficacy with a slightly higher but tolerable radiation dose than CSC. Therefore, TSC may be a candidate modality for patients with ambiguous findings on FFDM. [ABSTRACT FROM AUTHOR]

Contrast-enhanced mammography (CEM) is a relatively new imaging technique that allows morphologic, anatomic and functional imaging of the breast. The aim of our study was to validate contrast-enhanced mammography (CEM) compared to mammography (MMG) and digital breast tomosynthesis (DBT) in daily clinical practice. This retrospective study included 316 consecutive patients who underwent MMG, DBT and CEM at the Centre for Prevention and Diagnosis of Chronic Diseases of Primorsko-goranska County. Two breast radiologists independently analyzed the image data, without available anamnestic information and without the possibility of comparison with previous images, to determine the presence of suspicious lesions and their morphological features according to the established criteria of the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The diagnostic value of MMG, DBT and CEM was assessed by ROC analysis. The interobserver agreement was excellent. CEM showed higher diagnostic accuracy in terms of sensitivity and specificity compared to MMG and DBT, the reporting time for CEM was significantly shorter, and CEM findings resulted in a significantly lower proportion of equivocal findings (BI-RADS 0), suggesting fewer additional procedures. In conclusion, CEM achieves high diagnostic accuracy while maintaining simplicity, reproducibility and applicability in complex clinical settings. [ABSTRACT FROM AUTHOR]

This study proposes an innovative geometry of a microstrip sensor for high-resolution microwave imaging (MWI). The main intended application of the sensor is early detection of breast, lung, and brain cancer. The proposed design consists of a microstrip patch antenna fed by a coplanar waveguide with a metamaterial (MTM) layer-based lens implemented on the back side, and an artificial magnetic conductor (AMC) realized on as a separate layer. The analysis of the AMC's permeability and permittivity demonstrate that the structure exhibits negative epsilon (ENG) qualities near the antenna resonance point. In addition, reflectivity, transmittance, and absorption are also studied. The sensor prototype has been manufactures using the FR4 laminate. Excellent electrical and field characteristics of the structure are confirmed through experimental validation. At the resonance frequency of 4.56 GHz, the realized gain reaches 8.5 dBi, with 3.8 dBi gain enhancement contributed by the AMC. The suitability of the presented sensor for detecting brain tumors, lung cancer, and breast cancer has been corroborated through extensive simulation-based experiments performed using the MWI system model, which employs four copies of the proposed sensor, as well as the breast, lung, and brain phantoms. As demonstrated, the directional radiation pattern and enhanced gain of the sensor enable precise tumor size discrimination. The proposed sensor offers competitive performance in comparison the state-of-the-art sensors described in the recent literature, especially with respect to as gain, pattern directivity, and impedance matching, all being critical for MWI. [ABSTRACT FROM AUTHOR]

Medical datasets often have a skewed class distribution and a lack of high-quality annotated images. However, deep learning methods require a large amount of labeled data for classification. In this study, we present a few-shot learning approach for the classification of ultrasound breast cancer images using meta-learning methods. We used prototypical networks and model agnostic meta-learning (MAML) algorithms as meta-learning methods. The breast ultrasound images (BUSI) dataset, which has three classes and is difficult to use in meta-learning, was used for meta-testing in a cross-domain approach along with other datasets for meta-training. Our proposed approach yielded an accuracy range of 0.882–0.889, achieved by implementing the ResNet50 backbone with ProtoNet in a 10-shot setting. These results represent a significant improvement ranging from 6.27 to 7.10% over the baseline accuracy of 0.831. The results showed that ProtoNet outperformed the MAML method for all k-shot settings. In addition, the use of ResNet models as the backbone network for feature extraction was found to be more successful than the use of a four-layer convolutional model. Our proposed method is the first attempt to apply meta-learning for few-shot classification in the BUSI dataset while providing higher accuracy compared to deep learning methods for medical images with small-scale datasets and few classes. The methodology used in this study can be adapted to other datasets with similar problems. [ABSTRACT FROM AUTHOR]

Objectives In recent years, many deep learning-based classification models have been proposed. Because the size of histopathology images is extremely large, current methods usually divide it into many small patches of the same size and then build the classification model. The models first extract the features of patches and then use pooling methods etc. to integrate patch-level features into slide-level features for classification, in which the extraction of patch-level features and independent with the integration and classification process, making it impossible to adapt the feature extractor with the feedback of classification results. Therefore, they can not obtain the feature which is most useful for the whole slide image (WSI) classification. The objective of the paper is to solve the problems faced by above methods.Methods We propose an end-to-end attention pooling method based on a patch-scoring model. First, we build a patch scoring model to score each patch with the multiple instance learning methods, selecting patches based on the scores. Second, we utilize an attention-pooling module to integrate the features of selected patches to build a classification model. Besides, according to the character of the histopathology image, we propose to use a customized squared average function to normalize the attention weights to focus more on the high scored patches in positive WSI, making the classification results more interpretable.Results On the public available dataset CAMELYON16, the proposed method achieves F1 score at 0.644. And it achieves F1 score at 0.593 at the public available BACH dataset. When the new normalization function is used, the average attention weights difference between positive patches and negative patches is 0.017, which is much higher than when softmax function was used.Conclusions We can conclude from the experiments that the end-to-end attention pooling method is a better method for breast histopathology image classification. The proposed normalization function can make the attention module more interpretable. [ABSTRACT FROM AUTHOR]

(1) Purpose: The purpose of this study was to describe the outcomes of diagnostic breast imaging and the incidence of delayed breast cancer diagnosis in the study population. (2) Methods: We collected the outcome data from diagnostic mammograms and/or breast ultrasounds (USs) performed on women between the ages of 30 and 50 with symptomatic breast clinical presentations between 2018 and 2019. (3) Results: Out of 171 eligible patients, 10 patients (5.8%) had BIRADS 0, 90 patients (52.6%) had benign findings (BIRADS 1 and 2), 41 (24.0%) patients had probable benign findings requiring short-term follow-up (BIRADS 3), while 30 (17.5%) patients had findings suspicious of malignancy (BIRADS 4 and 5). In the BIRADS 3 group, 92.7% had recommended follow-up, while in BIRADS 4 and 5, only 83.3% underwent recommended biopsy at a mean time of 1.7 weeks (range 0–22 wks) from their follow-up scan. Ten (6%) patients were diagnosed with breast cancer, all of whom had BIRADS 4 or 5, with a mean time of breast cancer diagnosis from initial diagnostic imaging of 2.2 weeks (range 1–22 wks). No patients had delayed breast cancer diagnosis in our cohort. (4) Conclusions: We conclude that diagnostic mammograms and breast US are appropriate investigations for clinical breast concerns in women aged 30–50 years. [ABSTRACT FROM AUTHOR]

Purpose: Women with high breast density (HBD) carry an increased risk for breast cancer (BC). The aim of the study was to provide data on awareness and knowledge gaps among women with vs w/o HBD about BC risk factors (BCRFs), which is the basis for effective communication about screening. Patients and Methods: This was a web-based survey of 3000 women aged ≥ 30 and ≤ 70 from six countries. It comprised of 45 questions. T-tests and chi-square tests with False Discovery Rate adjustments were conducted as applicable, with significant differences reported at α=0.05. Results: Three-thousand women were included in the analysis, 733 (24.4%) had HBD. Overall, 39% of women were familiar with the concept of HBD in the context of BC. Thirty-one percent of women were aware of HBD as BCRF and for 24% of women HBD was personally applicable. A significantly higher proportion of women with HBD were aware of almost all BCRFs compared to women w/o HBD (p ≤ 0.05). Similarly, a significantly higher proportion of women with HBD have undergone screening procedures compared to women w/o HBD (p ≤ 0.05). Women with HBD were significantly better aware of basic facts about BC (p ≤ 0.05). A total of 1617 women underwent mammography, 904 ultrasound and 150 MRI during their last screening. The most relevant source of information about BC was the health care professional, as reported by 63% of women. Conclusion: Overall 39% of women were familiar with HBD as BCRF. Lack of BCRF awareness may contribute to delayed screenings, missed opportunities for early detection, and potentially poorer outcomes for individuals with dense breast tissue. Thus, this information should be communicated more widely. [ABSTRACT FROM AUTHOR]

This study seeks to enhance the classification performance of breast ultrasound images, addressing the challenges of difficult and costly collection of breast ultrasound datasets as well as the discrepancies in feature distribution of the collected datasets. Performance is enhanced by using a mix of generative adversarial networks (GAN) and domain adaptive networks. First, an adaptive layer is first added to the basic model of the gradually vanishing bridge (GVB), to better match the feature distributions of the source and target domains of the dataset. The multi-kernel maximum mean discrepancy (MK-MMD), which is the most efficient of existing adaptive approaches, is implemented in the fully connected layer of the original model's feature extraction network. Finally, through the process of fine-tuning, the model that has the highest level of overall performance is determined. In experiments, the proposed method surpassed the conventional unsupervised domain adaptation (DDC) and adversarial domain adaptation (MK_DAAN, GVB) in performance, achieving 85.11% accuracy, 97.48% recall, and 0.92 F1-score. [ABSTRACT FROM AUTHOR]

Background: Breast pain is the second most common breast symptom (after breast lump) for which women seeks medical attention. Breast pain can affect daily life, including sleeping, or physical, social, and work-school life. Mastalgia is one of the common causes for women in tier 2 city visiting health care facility but there are only few studies which focus on demographic findings and ultrasonographic findings in mastalgia. Aims and Objectives: The objective of this study was to study demographics of breast pain in a tier 2 city and ultrasound evaluation of mastalgia. Materials and Methods: In this prospective study, 143 patients presenting with mastalgia irrespective of focality, duration, or cyclical nature were included. In these patients, we studied demographic profile and ultrasonographic findings in mastalgia. Results: About 37% women were in 20–29 years of age group and least number was in 70–79 years of age group. About 86% of women in our study were in reproductive age group. Most patient with mastalgia have associated palpable lump (58%) followed by 43% patients with mastalgia and nipple discharge, only 27% of patients have mastalgia alone whereas 4% women have pain with skin changes. The majority (49%) of mastalgia cases fall under the breast imaging-reporting data system (BIRADS)-I category while cases in BI-RADS-IV and V category were 2 and 1, respectively. Conclusion: Women in their reproductive age group and in their 3rd decade of life are more prone for mastalgia. Ultrasonography is also helpful in detecting cancers in setups where routine mammography is not possible. [ABSTRACT FROM AUTHOR]

Breast imaging plays a crucial role in the early detection and management of breast cancer, with visual quality, modality innovation and diagnostic performance being key factors in achieving accurate diagnoses and optimal patient outcomes. This paper presents a comprehensive bibliometric analysis of the literature on the three above elements focusing on breast imaging, aiming to uncover publication trends, identify influential works and authors, and highlight future research directions. We employed a methodical bibliometric approach, making use of Scopus and Web of Science (WoS) databases for gathering literatures. We planned our search strategy, concentrating on terms linked to "breast imaging," "image quality," and "diagnostic accuracy" to ensure a systematic examination of the subject. The enhanced search functions in these databases enabled us to narrow down and improve our findings, choosing only the articles, conference papers, and book sections that are most relevant. After conducting a thorough screening process to remove duplicates and evaluate significance, we utilized ScientoPy and VOSviewer software for an in-depth bibliometric analysis. This helped to explore trends in publications, patterns of citations, and thematic groups, giving us a better understanding of how the field has changed and where it currently stands. Our approach prioritized assessing methodological quality and bias in the studies we included, guaranteeing the reliability of our findings. We reviewed 2984 relevant publications, revealing a consistent annual growth rate of 2.8% in breast imaging research, with the United States and Europe leading in contributions. The study found that advancements in radiological technologies and international collaboration are driving forces behind the field's expansion. Key subject areas such as 'Radiology, Nuclear Medicine, and Medical Imaging' dominated, underscoring their impact on diagnostic quality. Notable authors and institutions have been identified for their influential research, characterized by high citation metrics and significant scholarly impact. The study shows a continuous increase in research on breast imaging, considered by new technologies and teamwork defining the present time. The assessment highlights a key move towards utilizing digital imaging methods and computational analysis, affecting the improvement of future diagnostic procedures and patients' results. The study highlights the importance of continued international collaborations to tackle the new barriers in breast imaging and make the most of technological progress. This study shows a focus on using interdisciplinary methods and cutting-edge technology in breast imaging to help healthcare professionals improve their performance and accuracy in diagnosis. Recognizing vital research and emerging trends should guide clinical guidelines, radiology training, and patient care plans to encourage the use of effective techniques and stimulate innovation in diagnostic approaches. [ABSTRACT FROM AUTHOR]

Background: The study aims to evaluate the diagnostic efficacy of contrast-enhanced ultrasound (CEUS) and shear-wave elastography (SWE) in detecting small malignant breast nodules in an effort to inform further refinements of the Breast Imaging Reporting and Data System (BI-RADS) classification system. Methods: This study retrospectively analyzed patients with breast nodules who underwent conventional ultrasound, CEUS, and SWE at Gongli Hospital from November 2015 to December 2019. The inclusion criteria were nodules ≤ 2 cm in diameter with pathological outcomes determined by biopsy, no prior treatments, and solid or predominantly solid nodules. The exclusion criteria included pregnancy or lactation and low-quality images. Imaging features were detailed and classified per BI-RADS. Diagnostic accuracy was assessed using receiver operating characteristic curves. Results: The study included 302 patients with 305 breast nodules, 113 of which were malignant. The diagnostic accuracy was significantly improved by combining the BI-RADS classification with CEUS and SWE. The combined approach yielded a sensitivity of 88.5%, specificity of 87.0%, positive predictive value of 80.0%, negative predictive value of 92.8%, and accuracy of 87.5% with an area under the curve of 0.877. Notably, 55.8% of BI-RADS 4A nodules were downgraded to BI-RADS 3 and confirmed as benign after pathological examination, suggesting the potential to avoid unnecessary biopsies. Conclusion: The integrated use of the BI-RADS classification, CEUS, and SWE enhances the accuracy of differentiating benign and malignant small breast nodule, potentially reducing the need for unnecessary biopsies. [ABSTRACT FROM AUTHOR]

Background: Accurate breast density evaluation allows for more precise risk estimation but suffers from high inter‐observer variability. Purpose: To evaluate the feasibility of reducing inter‐observer variability of breast density assessment through artificial intelligence (AI) assisted interpretation. Study Type: Retrospective. Population: Six hundred and twenty‐one patients without breast prosthesis or reconstructions were randomly divided into training (N = 377), validation (N = 98), and independent test (N = 146) datasets. Field Strength/Sequence: 1.5 T and 3.0 T; T1‐weighted spectral attenuated inversion recovery. Assessment: Five radiologists independently assessed each scan in the independent test set to establish the inter‐observer variability baseline and to reach a reference standard. Deep learning and three radiomics models were developed for three classification tasks: (i) four Breast Imaging‐Reporting and Data System (BI‐RADS) breast composition categories (A–D), (ii) dense (categories C, D) vs. non‐dense (categories A, B), and (iii) extremely dense (category D) vs. moderately dense (categories A–C). The models were tested against the reference standard on the independent test set. AI‐assisted interpretation was performed by majority voting between the models and each radiologist's assessment. Statistical Tests: Inter‐observer variability was assessed using linear‐weighted kappa (κ) statistics. Kappa statistics, accuracy, and area under the receiver operating characteristic curve (AUC) were used to assess models against reference standard. Results: In the independent test set, five readers showed an overall substantial agreement on tasks (i) and (ii), but moderate agreement for task (iii). The best‐performing model showed substantial agreement with reference standard for tasks (i) and (ii), but moderate agreement for task (iii). With the assistance of the AI models, almost perfect inter‐observer variability was obtained for tasks (i) (mean κ = 0.86), (ii) (mean κ = 0.94), and (iii) (mean κ = 0.94). Data Conclusion: Deep learning and radiomics models have the potential to help reduce inter‐observer variability of breast density assessment. Level of Evidence: 3 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Unlike many other subspecialties in radiology, breast radiologists practice in a patient-facing and interdisciplinary environment where team building, communication, and leadership skills are critical. Although breast radiologists can improve these skills over time, strong mentorship can accelerate this process, leading to a more successful and satisfying career. In addition to providing advice, insight, feedback, and encouragement to mentees, mentors help advance the field of breast radiology by contributing to the development of the next generation of leaders. During the mentorship process, mentors continue to hone their listening, problem-solving, and networking skills, which in turn creates a more supportive and nurturing work environment for the entire breast care team. This article reviews important mentorship skills that are essential for all breast radiologists. Although some of the principles apply to all mentoring relationships, ensuring that every breast radiologist has the skills to be both an effective mentor and mentee is key to the future of the profession. [ABSTRACT FROM AUTHOR]

Cancer remains one of the most challenging afflictions facing humanity, lacking a definitive cure. Among its prevalent forms, breast cancer stands as a significant health concern, disproportionately affecting women aged 20-59. Nonetheless, mortality rates can be mitigated through timely screening and treatment interventions. The intricacies of breast cancer detection and classification, particularly distinguishing between cancerous and healthy cells, pose formidable challenges. This study proposes an innovative approach harnessing artificial intelligence (AI) in conjunction with the Internet of Medical Things (IoMT) to address early-stage breast cancer detection and classification. The devised artificial intelligence-enabled heuristic framework (AI-HF) leverages machine intelligence techniques (MIT) to identify cancerous cells by analyzing shape and texture-based features. Subsequently, machine learning (ML) classification techniques, notably support vector machines (SVMs) are employed to discern cancerous cells from healthy ones. Experimental findings, derived from real datasets, underscore the efficacy of the proposed method, achieving a commendable accuracy rate of 98.0% in detecting and categorizing abnormal cells within breast cancer images. Importantly, the AI-HF classifier emerges as a swift and efficient model, outperforming traditional approaches by swiftly distinguishing between cancerous and healthy cells based on extracted cell image attributes. [ABSTRACT FROM AUTHOR]

Initial diagnostic ultrasound of a 22-year-old female patient presenting with a palpable breast mass revealed a suspicious mass initially thought to arise from the breast. However, follow-up diagnostic mammography was normal without evidence of the 5 cm mass seen on ultrasound, and pathology results from ultrasound-guided core needle biopsy raised suspicion for giant cell tumor, making chest wall origin of the mass more likely. Further CT and MRI imaging indeed revealed a locally invasive mass arising from the anterior fifth rib. The patient was treated with denosumab to decrease tumor burden before surgery, and subsequently underwent successful surgical resection of the tumor with mesh overlay and flap reconstruction of the chest wall defect. This case highlights the importance of keeping chest wall lesions in the differential for lesions presenting clinically as breast lesions. Despite the rarity of giant cell tumor of the anterior rib and its unusual presentation as a breast mass, appropriate diagnostic imaging work-up allowed for successful diagnosis and treatment in this case.

Background: Breast pain is the second most common breast symptom (after breast lump) for which women seeks medical attention. Breast pain can affect daily life, including sleeping, or physical, social, and work-school life. Mastalgia is one of the common causes for women in tier 2 city visiting health care facility but there are only few studies which focus on demographic findings and ultrasonographic findings in mastalgia. Aims and Objectives: The objective of this study was to study demographics of breast pain in a tier 2 city and ultrasound evaluation of mastalgia. Materials and Methods: In this prospective study, 143 patients presenting with mastalgia irrespective of focality, duration, or cyclical nature were included. In these patients, we studied demographic profile and ultrasonographic findings in mastalgia. Results: About 37% women were in 20–29 years of age group and least number was in 70–79 years of age group. About 86% of women in our study were in reproductive age group. Most patient with mastalgia have associated palpable lump (58%) followed by 43% patients with mastalgia and nipple discharge, only 27% of patients have mastalgia alone whereas 4% women have pain with skin changes. The majority (49%) of mastalgia cases fall under the breast imaging-reporting data system (BI-RADS)-I category while cases in BI-RADS-IV and V category were 2 and 1, respectively. Conclusion: Women in their reproductive age group and in their 3rd decade of life are more prone for mastalgia. Ultrasonography is also helpful in detecting cancers in setups where routine mammography is not possible.

Breast cancer is the most common cancer in women; approximately 1 in 8 women is diagnosed with breast cancer in their lifetime. Some women are at significantly higher risk of developing breast cancer, including women carrying mutations in the BRCA1/2, TP53, or other genes and women with other risk factors. Women with a high lifetime risk for breast cancer are frequently offered annual breast magnetic resonance imaging (MRI) examinations for early breast cancer detection. Breast MRI is commonly performed using a multiparametric imaging protocol, including dynamic contrast-enhanced T1-weighted acquisitions. The dynamic contrast-enhanced T1-weighted acquisitions are frequently transformed into subtraction series, allowing the focused visualization of areas with high signal intensity and masses associated with elevated contrast agent uptake, which are among the hallmarks of suspicious findings. Here, we report a case in which a suspicious lesion-mimicking swap artifact occurred using a T1-weighted contrast-enhanced DIXON acquisition technique in a high-risk breast cancer screening MRI examination.

Medical imaging research has experienced significant growth over the past decade, particularly in the fields of computer vision and pattern recognition. Computational approaches have been proposed to address the challenges in breast cancer detection, classification and segmentation. However, recent advancements in computational technology such as machine learning (ML) methods have dramatically changed the landscape of breast cancer imaging research. This survey aims to provide a compilation of information for future breast cancer imaging researchers by 1) comprehensively examining how various ML techniques are being used to address the main challenges in breast cancer imaging; 2) providing an in-depth discussion and review of publicly available datasets for the development and evaluation of novel breast cancer detection, classification and segmentation approaches; and 3) outlining current evaluation metrics used by breast cancer imaging researchers. The insights and findings presented in this survey will serve as a valuable resource for researchers and clinicians interested in breast cancer imaging. By providing an overview of the current state-of-the-art techniques and highlighting areas for future research, we hope to facilitate the development of more accurate and effective breast cancer imaging ML techniques, and contribute to advancing our understanding and improving the diagnosis and treatment of breast cancer. [ABSTRACT FROM AUTHOR]

Introduction: Mammography is an excellent resource to reduce the burden of premature death associated with breast cancer; however, screening is only recommended between the ages of 50 and 69 years. General objective: To compare the frequency of suspicious and non-diagnostic mammograms for breast cancer when screening Chilean women between the ages of 40 and 50 years. Patients and methods: Cross-sectional study nested in a breast cancer screening programme in Chilean women >40 years old, conducted between 2017 and 2021. Demographic variables and risk factors are described. To establish the effect of age on screening, we calculated the number needed to screen for a Breast Imaging Reporting and Data Reporting System 4 or 5 mammogram when screening over 40 years or over 50 years. Results: We studied 137,690 women screened for breast cancer since the age of 40 years. The median age was 54 years (range 40-93 years). 64.7% of women were post-menopausal, 4.79% were nulliparous and 14% of post-menopausal women were receiving hormone replacement therapy. To find a suspicious mammogram, 170 women over 40 years and 149 women over 50 years would have to be screened. Conclusion: By changing the starting age of screening from 50 to 40 years 21 more women would have to be screened to detect a suspicious mammogram and if screened from age 50 and not from age 40 years 21% of total suspicious mammograms would remain unidentified. [ABSTRACT FROM AUTHOR]

Objective: To determine the added value of digital breast tomosynthesis (DBT) in the assessment of lesions detected by contrast-enhanced mammography (CEM). Material and methods: A retrospective study was conducted in a tertiary university medical center. All CEM studies including DBT performed between January 2016 and December 2020 were included. Lesions were categorized and scored by four dedicated breast radiologists according to the recent CEM and DBT supplements to the Breast Imaging Reporting and Data System (BIRADS) lexicon. Changes in the BIRADS score of CEM-detected lesions with the addition of DBT were evaluated according to the pathology results and 1-year follow-up imaging study. Results: BIRADS scores of CEM-detected lesions were upgraded toward the lesion’s pathology with the addition of DBT (p > 0.0001), overall and for each reader. The difference in BIRADS scores before and after the addition of DBT was more significant for readers who were less experienced. The reason for changes in the BIRADS score was better lesion margin visibility. The main BIRADS descriptors applied in the malignant lesions were spiculations, calcifications, architectural distortion, and sharp or obscured margins. Conclusions: The addition of DBT to CEM provides valuable information on the enhancing lesion, leading to a more accurate BIRADS score.

Artificial Intelligence (AI) is defined as the simulation of human intelligence by a digital computer or robotic system and has become a hype in current conversations. A subcategory of AI is deep learning, which is based on complex artificial neural networks that mimic the principles of human synaptic plasticity and layered brain architectures, and uses large-scale data processing. AI-based image analysis in breast screening programmes has shown non-inferior sensitivity, reduces workload by up to 70% by pre-selecting normal cases, and reduces recall by 25% compared to human double reading. Natural language programs such as ChatGPT (OpenAI) achieve 80% and higher accuracy in advising and decision making compared to the gold standard: human judgement. This does not yet meet the necessary requirements for medical products in terms of patient safety. The main advantage of AI is that it can perform routine but complex tasks much faster and with fewer errors than humans. The main concerns in healthcare are the stability of AI systems, cybersecurity, liability and transparency. More widespread use of AI could affect human jobs in healthcare and increase technological dependency. AI in senology is just beginning to evolve towards better forms with improved properties. Responsible training of AI systems with meaningful raw data and scientific studies to analyse their performance in the real world are necessary to keep AI on track. To mitigate significant risks, it will be necessary to balance active promotion and development of quality-assured AI systems with careful regulation. AI regulation has only recently included in transnational legal frameworks, as the European Union’s AI Act was the first comprehensive legal framework to be published, in December 2023. Unacceptable AI systems will be banned if they are deemed to pose a clear threat to people’s fundamental rights. Using AI and combining it with human wisdom, empathy and affection will be the method of choice for further, fruitful development of tomorrow’s senology.

Different image enhancement techniques are applied to improve the visual quality of an image on a display device. Contrast stretching, intensity level slicing with and without background, histogram equalization, logarithmic transformation and power law transformation are some image enhancement techniques. Most of the research work focuses on adaptive gamma correcting factors for better visualization of extremely low contrast images, giving less importance to the constant for enhanced visualization. This research proposes an efficient and less complex enhanced power law transformation (EPLT) approach to improve the contrast of dimmed and extremely bright images. The approach is a quick way to compute the value of C, i.e. constant for enhanced visualization. For better picture quality, it is very important to determine C automatically and the gamma correcting factor. This technique offers a novel and unique perspective on image contrast manipulation. The proposed enhancement technique is experimented on histopathology images of breast cancer, bright images and extremely dark images. The average peak signal-to-noise ratio (PSNR) for clinical data and BreakHis dataset is high for the proposed method are 16.52487 and 17.69335 respectively. The average RMSE for clinical data and BreakHis dataset is low for the proposed method are 40.88251 and 44.2546 respectively. It is observed that the proposed method yields the most satisfactory contrast enhancements based on performance comparison with other state-of-art enhancement algorithms and works efficiently on all types of images. [ABSTRACT FROM AUTHOR]

Background: Breast imaging clinics in the United States (U.S.) are increasingly implementing breast cancer risk assessment (BCRA) to align with evolving guideline recommendations but with limited uptake of risk-reduction care. Effectively communicating risk information to women is central to implementation efforts, but remains understudied in the U.S. This study aims to characterize, and identify factors associated with women's interest in and preferences for breast cancer risk communication. Methods: This is a cross-sectional survey study of U.S. women presenting for a mammogram between January and March of 2021 at a large, tertiary breast imaging clinic. Survey items assessed women's interest in knowing their risk and preferences for risk communication if considered to be at high risk in hypothetical situations. Multivariable logistic regression modeling assessed factors associated with women's interest in knowing their personal risk and preferences for details around exact risk estimates. Results: Among 1119 women, 72.7% were interested in knowing their breast cancer risk. If at high risk, 77% preferred to receive their exact risk estimate and preferred verbal (52.9% phone/47% in-person) vs. written (26.5% online/19.5% letter) communications. Adjusted regression analyses found that those with a primary family history of breast cancer were significantly more interested in knowing their risk (OR 1.5, 95% CI 1.0, 2.1, p = 0.04), while those categorized as "more than one race or other" were significantly less interested in knowing their risk (OR 0.4, 95% CI 0.2, 0.9, p = 0.02). Women 60 + years of age were significantly less likely to prefer exact estimates of their risk (OR 0.6, 95% CI 0.5, 0.98, p < 0.01), while women with greater than a high school education were significantly more likely to prefer exact risk estimates (OR 2.5, 95% CI 1.5, 4.2, p < 0.001). Conclusion: U.S. women in this study expressed strong interest in knowing their risk and preferred to receive exact risk estimates verbally if found to be at high risk. Sociodemographic and family history influenced women's interest and preferences for risk communication. Breast imaging centers implementing risk assessment should consider strategies tailored to women's preferences to increase interest in risk estimates and improve risk communication. [ABSTRACT FROM AUTHOR]

Positron emission tomography (PET) plays a crucial role in breast cancer management. This review addresses the role of PET imaging in breast cancer care. We focus primarily on the utility of 18F-fluorodeoxyglucose (FDG) PET in staging, recurrence detection, and treatment response evaluation. Furthermore, we delve into the growing interest in precision therapy and the development of novel radiopharmaceuticals targeting tumor biology. This includes discussing the potential of PET/MRI and artificial intelligence in breast cancer imaging, offering insights into improved diagnostic accuracy and personalized treatment approaches. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the most commonly diagnosed cancer type, especially in women. Identifying the subtype of malignant (cancer) lesions can help to give proper treatment to cancer patients. The subtype of benign lesions can help to estimate the risk of developing cancer in the future. The computer-aided diagnosis (CAD) is an automated and more precise method for histopathological image classification. With the recent development in computer vision and deep learning, advanced convolution neural networks have achieved great success in image classification and widely used in medical image processing. This paper proposes a deep based multi-scale multi-stream feature network (M2S2-FNet) with attention mechanism for classification H&E stained breast histopathological microscopy image either as benign or malignant, and then categorizing malignant and benign cases into four different subtypes each. The proposed network processes the patch of breast cancer image through each multi-scale multi-stream to extract the robust; and refined features from the attention module. The proposed M2S2-FNet follows the knowledge of sharing strategy by sharing learned features at each stream across the network and the attention mechanism. M2S2-FNet achieved an accuracy for different magnification factors (× 40, × 100, × 200, and × 400) but the superior accuracy i.e. 98.07% for multi-class and 99.60% for binary at × 200. Accuracy, performance measure parameters like Recall, Precision and Sensitivity etc. and weight of M2S2-FNet model were better than existing models like VGG16, Xception, ResNet152 and BreastNet. [ABSTRACT FROM AUTHOR]

Significance: The accurate correlation between optical measurements and pathology relies on precise image registration, often hindered by deformations in histology images. We investigate an automated multi-modal image registration method using deep learning to align breast specimen images with corresponding histology images. Aim: We aim to explore the effectiveness of an automated image registration technique based on deep learning principles for aligning breast specimen images with histology images acquired through different modalities, addressing challenges posed by intensity variations and structural differences. Approach: Unsupervised and supervised learning approaches, employing the VoxelMorph model, were examined using a dataset featuring manually registered images as ground truth. Results: Evaluation metrics, including Dice scores and mutual information, demonstrate that the unsupervised model exceeds the supervised (and manual) approaches significantly, achieving superior image alignment. The findings highlight the efficacy of automated registration in enhancing the validation of optical technologies by reducing human errors associated with manual registration processes. Conclusions: This automated registration technique offers promising potential to enhance the validation of optical technologies by minimizing human-induced errors and inconsistencies associated with manual image registration processes, thereby improving the accuracy of correlating optical measurements with pathology labels. [ABSTRACT FROM AUTHOR]

There is a growing trend of using artificial intelligence, particularly deep learning algorithms, in medical diagnostics, revolutionizing healthcare by improving efficiency, accuracy, and patient outcomes. However, the use of artificial intelligence in medical diagnostics comes with the critical need to explain the reasoning behind artificial intelligence‐based predictions and ensure transparency in decision‐making. Explainable artificial intelligence has emerged as a crucial research area to address the need for transparency and interpretability in medical diagnostics. Explainable artificial intelligence techniques aim to provide insights into the decision‐making process of artificial intelligence systems, enabling clinicians to understand the factors the algorithms consider in reaching their predictions. This paper presents a detailed review of saliency‐based (visual) methods, such as class activation methods, which have gained popularity in medical imaging as they provide visual explanations by highlighting the regions of an image most influential in the artificial intelligence's decision. We also present the literature on non‐visual methods, but the focus will be on visual methods. We also use the existing literature to experiment with infrared breast images for detecting breast cancer. Towards the end of this paper, we also propose an "attention guided Grad‐CAM" that enhances the visualizations for explainable artificial intelligence. The existing literature shows that explainable artificial intelligence techniques are not explored in the context of infrared medical images and opens up a wide range of opportunities for further research to make clinical thermography into assistive technology for the medical community. [ABSTRACT FROM AUTHOR]

This article, titled "Hold Your Breath for Breast Bone Imaging: A Pictorial Essay of Sternoclavicular Joint and Sternum Disorders," explores the various pathologies that can affect the sternoclavicular joint and sternum. The authors conducted a retrospective review of radiology imaging studies to identify and differentiate between different conditions. They found that CT and MRI were effective in identifying abnormalities, while some pathologies required histologic diagnosis. The article emphasizes the importance of a multidisciplinary team approach in accurately diagnosing and treating these conditions. [Extracted from the article]

Upconversion nanoparticles (UCNPs) doped with lanthanides are introduced as a significant tool in bioimaging applications. Here in, a comparative study has been performed to understand the cell internalization capacity of folic acid (FA) and arginine-glycine-aspartic acid-lysine (RGDK) ligands. To achieve this goal, polyacrylic acid (PAA) coated UCNPs (NaYF4:Yb3+, Er3+) are conjugated with various surface ligands such as FA and RGDK through a straightforward ligand exchange procedure. Ligand conjugation to UCNPs was characterized with a transmission electron microscope (TEM), Fourier-transform infrared (FT-IR) spectroscopy, zeta potential measurements, nuclear magnetic resonance (NMR) spectroscopy, and NanoDrop measurements. The cellular uptake of the nanoparticles was investigated on the breast cancer MCF-7 cell line. The obtained results demonstrated that folic acid and RGDK functionalized UCNPs showed remarkably higher cellular uptake, which clearly indicates that the specific targeting of UCNPs provides a better quality of sub-cellular imaging at lower energy band region. [ABSTRACT FROM AUTHOR]