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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.

Background Purpose Methods Results Conclusions Diffuse optical tomography (DOT) provides three‐dimensional image reconstruction of chromophore perturbations within a turbid volume. Two leading strategies to optimize DOT image quality include, (i) arrays of regular, interlacing, high‐density (HD) grids of sources and detectors with closest spacing less than 15 mm, or (ii) source modulated light of order ∼100 MHz.However, the general principles for how these crucial design parameters of array density and modulation frequency may interact to provide an optimal system design have yet to be elucidated.Herein, we systematically evaluated how these design parameters effect image quality via multiple key metrics. Specifically, we simulated 32 system designs with realistic measurement noise and quantified localization error, spatial resolution, signal‐to‐noise, and localization depth of field for each of ∼85 000 point spread functions in each model.We found that array density had a far stronger effect on image quality metrics than modulation frequency. Additionally, model fits for image quality metrics revealed that potential improvements diminish with regular arrays denser than 9 mm closest spacing. Further, for a given array density, 300 MHz source modulation provided the deepest reliable imaging compared to other frequencies.Our results indicate that both array density and modulation frequency affect the spatial sampling of tissue, which asymptotically saturates due to photon diffusivity within a turbid volume. In summary, our results provide comprehensive perspectives for optimizing future DOT system designs in applications from wearable functional brain imaging to breast tumor detection. [ABSTRACT FROM AUTHOR]

Background: Theoretically, endocrine fluctuations occurring during infertility treatments, including ovulation induction (OI) and assisted reproductive techniques (ART), could be associated with an increased risk of benign breast diseases (BBDs). To date, no studies have been conducted on this association. Therefore, the present study investigated the association between different types of infertility treatments and BBDs. Methods: This case‒control study was conducted in Arash Women's Hospital, Tehran, Iran. The case group included infertile women diagnosed with BBDs without atypia, and the control group included infertile women without breast disease. Breast imaging studies (mammography/ultrasound) were performed for BBD screening, and the diagnosis was confirmed by histopathological examination. Study variables were collected retrospectively from medical records, hospital databases, and questionnaires. Results: Finally, 154 infertile women, including 50 cases (BBDs) and 104 controls (no BBDs), were compared. Our data showed that 66% of cases and 61.4% of controls had undergone at least one course of infertility treatment. There was no association between BBD risk and previous infertility treatments (OR = 1.21; 95% CI = 0.59–2.46), ART (OR = 1.14; 95% CI = 0.90–1.44), or OI cycles (OR = 1.13; 95% CI = 0.98–1.32). Stratification by confounding variables did not change these results. Conclusions: It seems that there is no association between BBDs in infertile women and the type, duration, or number of prior infertility treatments; however, considering the small sample size of the study, the clinical significance of this finding should not be neglected. Therefore, we consider it essential to carry out more extensive, detailed, and prospective studies to distinguish the association of BBDs with different infertility treatments and medications. [ABSTRACT FROM AUTHOR]

Objectives: To identify preoperative breast MR imaging and clinicopathological variables related to recurrence and develop a risk prediction model for recurrence in young women with breast cancer treated with upfront surgery. Methods: This retrospective study analyzed 438 consecutive women with breast cancer aged 35 years or younger between January 2007 and December 2016. Breast MR images before surgery were independently reviewed by breast radiologists blinded to patient outcomes. The clinicopathological data including patient demographics, clinical features, and tumor characteristics were reviewed. Univariate and multivariate logistic regression analyses were used to identify the independent factors associated with recurrence. The risk prediction model for recurrence was developed, and the discrimination and calibration abilities were assessed. Results: Of 438 patients, 95 (21.7%) developed recurrence after a median follow-up of 65 months. Tumor size at MR imaging (HR = 1.158, p = 0.006), multifocal or multicentric disease (HR = 1.676, p = 0.017), and peritumoral edema on T2WI (HR = 2.166, p = 0.001) were identified as independent predictors of recurrence, while adjuvant endocrine therapy (HR = 0.624, p = 0.035) was inversely associated with recurrence. The prediction model showed good discrimination ability in predicting 5-year recurrence (C index, 0.707 in the development cohort; 0.686 in the validation cohort) and overall recurrence (C index, 0.699 in the development cohort; 0.678 in the validation cohort). The calibration plot demonstrated an excellent correlation (concordance correlation coefficient, 0.903). Conclusion: A prediction model based on breast MR imaging and clinicopathological features showed good discrimination to predict recurrence in young women with breast cancer treated with upfront surgery, which could contribute to individualized risk stratification. Clinical relevance statement: Our prediction model, incorporating preoperative breast MR imaging and clinicopathological features, predicts recurrence in young women with breast cancer undergoing upfront surgery, facilitating personalized risk stratification and informing tailored management strategies. Key Points: Younger women with breast cancer have worse outcomes than those diagnosed at more typical ages. The described prediction model showed good discrimination performance in predicting 5-year and overall recurrence. Incorporating better risk stratification tools in this population may help improve outcomes. [ABSTRACT FROM AUTHOR]

Breast imaging has several modalities, each unique in terms of its imaging position, evaluation index, and imaging method. Breast diagnosis is made by combining a large number of past imaging features with the clinical course and histological findings. Artificial intelligence (AI), which extracts the features from image data and evaluates them based on comprehensive analysis, has been making rapid progress in this regard. Many previous studies have demonstrated the usefulness and development potential of AI, such as machine learning and deep learning, in breast imaging. However, despite studies showing the good performance of AI models, their overall utilization remains low, since a large amount of diverse imaging data is required, and prospective verification is necessary to prove its high reproducibility and robustness. Sharing information and collaborating with multiple institutions to collect and verify images of different conditions and backgrounds are vital. If image diagnosis using AI can indeed ensure a more detailed diagnosis, such as breast cancer subtypes or prognosis, it can help develop personalized medicine, which is urgently required. The positive results of AI research, using such image information, can make each modality more valuable than ever. The current review summarized the results of previous studies using AI in each evaluation field and discussed the related future prospects. [ABSTRACT FROM AUTHOR]

Purpose: To assess the accuracy of preoperative sonographic staging in patients with primary invasive breast cancer. Methods: We retrospectively analyzed a prospectively kept service database of patients with newly diagnosed, unifocal, cT1-3, invasive breast cancer. All patients were diagnosed at a single center institution between January 2013 and December 2021. Clinical T stage was assessed preoperatively by ultrasound and correlated with the definite postoperative pathologic T stage. Demographics, clinical and pathological characteristics were collected. Factors influencing accuracy, over- and underdiagnosis of sonographic staging were analyzed with multivariable regression analysis. Results: A total of 2478 patients were included in the analysis. Median patients' age was 65 years. 1577 patients (63.6%) had clinical T1 stage, 864 (34.9%) T2 and 37 (1.5%) T3 stage. The overall accuracy of sonography and histology was 76.5% (n = 1896), overestimation was observed in 9.1% (n = 225) of all cases, while underestimation occurred in 14.4% (n = 357) of all cases. Accuracy increased when clinical tumor stage cT was higher (OR 1.23; 95% CI 1.10–1.38, p ≤ 0.001). The highest accuracy was seen for patients with T2 stage (82.8%). The accuracy was lower in Luminal B tumors compared to Luminal A tumors (OR 0.71; 95% CI 0.59–0.87, p ≤ 0.001). We could not find any association between sonographic accuracy in HER2 positive patients, and demographic characteristics, or tumor-related factors. Conclusion: Our unicentric study showed a high accuracy of sonography in predicting T stage, especially for tumors with clinical T2 stage. Tumor stage and biological tumor factors do affect the accuracy of sonographic staging. [ABSTRACT FROM AUTHOR]

Recognition of patients at high risk (HR) for breast cancer allows earlier screening and opportunities for risk reduction. We compare patients referred to our breast clinic as HR vs referrals for other reason (ROR) and found to be HR. We evaluate under-recognized factors and treatment differences. A retrospective chart review of patients found to be HR but referred for any reason to our breast clinic from July 2012 to December 2022 was performed. Referral reason, demographics, hormonal history, family history, and other risk factors were evaluated and compared (HR vs ROR). While other risk models were used for screening, Gail and Tyrer-Cuzick version 7 (TCv7) were used for comparison. Breast imaging received, hormonal therapy, and genetics referral evaluations were compared. 195 patients were referred to our breast team, 113 (58%) were referred as HR while 82 (42%) were ROR. Average age was 47 years old. 175 (91%) were Caucasian. 74 (65.5%) were referred for genetic testing, and 32 (26%) tested positive for a genetic mutation (n = 10, 12% ROR). 67 (35%) were recommended chemoprevention (n = 32, 16.4% took chemoprevention). 6 (3.1%) underwent prophylactic mastectomies and 163 (85%) had supplemental breast imaging. Comparison of HR vs ROR did not show significant differences in hormonal factors or treatments received; however, TCv7 was higher in the group referred as HR (P < .001). Our study showed that HR patients are more commonly referred secondary to family history but undergo similar treatments as those ROR. Accessibility to screening tools and education of risk factors, especially in minorities and those not otherwise being screened, may help better recognize HR. [ABSTRACT FROM AUTHOR]

To assess the capabilities of large language models (LLMs), including Open AI (GPT-4.0) and Microsoft Bing (GPT-4), in generating structured reports, the Breast Imaging Reporting and Data System (BI-RADS) categories, and management recommendations from free-text breast ultrasound reports. In this retrospective study, 100 free-text breast ultrasound reports from patients who underwent surgery between January and May 2023 were gathered. The capabilities of Open AI (GPT-4.0) and Microsoft Bing (GPT-4) to convert these unstructured reports into structured ultrasound reports were studied. The quality of structured reports, BI-RADS categories, and management recommendations generated by GPT-4.0 and Bing were evaluated by senior radiologists based on the guidelines. Open AI (GPT-4.0) was better than Microsoft Bing (GPT-4) in terms of performance in generating structured reports (88% vs. 55%; p < 0.001), giving correct BI-RADS categories (54% vs. 47%; p = 0.013) and providing reasonable management recommendations (81% vs. 63%; p < 0.001). As the ability to predict benign and malignant characteristics, GPT-4.0 performed significantly better than Bing (AUC, 0.9317 vs. 0.8177; p < 0.001), while both performed significantly inferior to senior radiologists (AUC, 0.9763; both p < 0.001). This study highlights the potential of LLMs, specifically Open AI (GPT-4.0), in converting unstructured breast ultrasound reports into structured ones, offering accurate diagnoses and providing reasonable recommendations. [ABSTRACT FROM AUTHOR]

Due to the diversity of image sources, content-based multi-source image fusion and retrieval have shown promising capabilities in computer vision tasks, and especially when applied in Computer-Aided Diagnosis (CAD) to automate and improve the accuracy of medical image analysis. The combination of computer vision and CAD systems has the potential to revolutionize healthcare by augmenting the expertise of clinicians, improving overall diagnostic accuracy and helping experts in the clinical decision-making process by classifying and retrieving similar annotated clinical images to a given query. In the context of multi-view mammography interpretation, the concept of multi-view feature fusion has recently been studied to improve retrieval performance while effectively guaranteeing the complementarity of both MLO and CC views. However, conventional multi-view feature fusion makes descriptors long and lacks to take into consideration the relationship between descriptors. To deal with this issue, we propose two hierarchical multi-view feature fusion methods, for multi-view mammogram retrieval, based on the Canonical Correlation Analysis (CCA), which is the most commonly used multivariate parametric test. In fact, we have adapted CCA to determine the relationship between two descriptors by processing latent correlation factors. Moreover, after extracting descriptors for each view, a comparative study of texture and shape fusion descriptors is proposed in order to identify the more discriminative features for multi-view mammogram retrieval. Then, a query-dependent distance metric preserving both visual resemblance and semantic similarity is carried out to dynamically determine the more appropriate distance measure for each query image. Extensive experiments on the challenging Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) have demonstrated the effectiveness of the proposed hierarchical multi-view feature fusion for mammogram retrieval, which outperforms the performance achieved either by conventional fused information or by single view information. To improve the transparency of our paper, the source code of the proposed method and the related dataset (including readme files) are publicly accessible through the following GitHub link: https://github.com/ABDERRAHIMMAR/Multi-View-Feature-Fusion-for-Mammogram-Retrieval. This open-access resource empowers researchers and practitioners to delve deeper into our methodology, fostering collaboration and advancements in the field of computer-aided diagnosis and medical image analysis. [ABSTRACT FROM AUTHOR]

Breast cancer has been a significant contributor to cancer-related mortality, but advancements in early detection through regular mammography and improvements in treatment modalities have contributed to declining mortality rates in several regions. This study presents a novel approach to cancer diagnosis utilizing Full-Field Digital Mammography images through predictive analysis methods. By using predictive analytic techniques and mammography images, this study offers a novel way to cancer detection. The research involves the application of deep learning techniques to extract valuable insights from cancer images captured by mammography devices. The CBIS-DDSM (Curated Breast Imaging Subset of Digital Database for Screening Mammography) dataset including images from patients with varying types and stages of cancer, is collected and pre-processed to ensure uniformity and quality. Relevant features, including color, texture, and shape characteristics, are extracted, and a rigorous feature selection process is employed to identify discriminative markers. The Residual Network (ResNet) model is selected and trained on the dataset, with a focus on classification accuracy and robust predictive performance. Validation metrics, such as accuracy, IoU (Intersection over Union) score, dice score, and ROC (Receiver Operating Characteristic) curve are employed to evaluate the model's efficiency. After analysis, the proposed method had the best degree of mass lesion detection accuracy, at 99.24%. This research contributes to the advancement of non-invasive and efficient diagnostic tools, potentially enhancing early detection and intervention in cancer patients. The proposed method not only demonstrates promising results in terms of diagnostic accuracy but also emphasizes interpretability, seamless integration into clinical workflows, and adherence to ethical standards. [ABSTRACT FROM AUTHOR]

Purpose: This study compared field-of-view (FOV) optimized and constrained undistorted single-shot diffusion-weighted imaging (FOCUS DWI) with deep-learning-based reconstruction (DLR) to conventional DWI for breast imaging. Methods: This study prospectively enrolled 49 female patients suspected of breast cancer from July to December 2023. The patients underwent conventional and FOCUS breast DWI and data were reconstructed with and without DLR. Two radiologists independently evaluated three images per patient using a 5-point Likert scale. Objective evaluations, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC), were conducted using manual region of interest-based analysis. The subjective and objective evaluations were compared using the Friedman test. Results: The scores for the overall image quality, anatomical details, lesion conspicuity, artifacts, and distortion in FOCUS-DLR DWI were higher than in conventional DWI (all P < 0.001). The SNR of FOCUS-DLR DWI was higher than that of conventional and FOCUS DWI (both P < 0.001), while FOCUS and conventional DWI were similar (P = 0.096). Conventional, FOCUS, and FOCUS-DLR DWI had similar CNR and ADC values. Conclusion: Our findings indicate that images produced by FOCUS-DLR DWI were superior to conventional DWI, supporting the applicability of this technique in clinical practice. DLR provides a new approach to optimize breast DWI. [ABSTRACT FROM AUTHOR]

Summary Imaging research pathways focus increasingly on the development of individualised approaches to breast cancer detection, diagnosis and management. Detection of breast cancer with X‐ray mammography may fail in some cancer subtypes with limited changes in morphology/tissue density and in women with dense breasts. International organisations offer recommendations for contrast‐enhanced breast imaging, as it provides superior sensitivity for screening, local staging and assessment of neoadjuvant treatment response, when compared with standard X‐ray mammography (including tomosynthesis) and breast ultrasound. Arguably, the evidence base is stronger for contrast‐enhanced MRI (CE‐MRI). Unfortunately, patient access to breast MRI in rural and remote areas is limited by practical limitations and equipment licensing restrictions. Moreover, breast MRI is an expensive test, likely to be out of reach for many women. Contrast‐enhanced mammography (CEM) offers an attractive alternative to improve patient access to functional breast imaging. It is a new type of digital, dual energy X‐ray mammography that can be performed on most modern units, following a relatively inexpensive hard‐ and software upgrade. In this paper, we review the rapidly accumulating evidence that CEM can provide similar diagnostic accuracy to CE‐MRI, though at a significantly lower cost and offering greater comfort to the patient. The adoption of CEM can help meet the anticipated increased demand for CE‐MRI. [ABSTRACT FROM AUTHOR]

Background: To assess the reliability and comprehensibility of breast radiology reports simplified by artificial intelligence using the large language model (LLM) ChatGPT-4o. Methods: A radiologist with 20 years' experience selected 21 anonymized breast radiology reports, 7 mammography, 7 breast ultrasound, and 7 breast magnetic resonance imaging (MRI), categorized according to breast imaging reporting and data system (BI-RADS). These reports underwent simplification by prompting ChatGPT-4o with "Explain this medical report to a patient using simple language". Five breast radiologists assessed the quality of these simplified reports for factual accuracy, completeness, and potential harm with a 5-point Likert scale from 1 (strongly agree) to 5 (strongly disagree). Another breast radiologist evaluated the text comprehension of five non-healthcare personnel readers using a 5-point Likert scale from 1 (excellent) to 5 (poor). Descriptive statistics, Cronbach's α, and the Kruskal–Wallis test were used. Results: Mammography, ultrasound, and MRI showed high factual accuracy (median 2) and completeness (median 2) across radiologists, with low potential harm scores (median 5); no significant group differences (p ≥ 0.780), and high internal consistency (α > 0.80) were observed. Non-healthcare readers showed high comprehension (median 2 for mammography and MRI and 1 for ultrasound); no significant group differences across modalities (p = 0.368), and high internal consistency (α > 0.85) were observed. BI-RADS 0, 1, and 2 reports were accurately explained, while BI-RADS 3–6 reports were challenging. Conclusion: The model demonstrated reliability and clarity, offering promise for patients with diverse backgrounds. LLMs like ChatGPT-4o could simplify breast radiology reports, aid in communication, and enhance patient care. Relevance statement: Simplified breast radiology reports generated by ChatGPT-4o show potential in enhancing communication with patients, improving comprehension across varying educational backgrounds, and contributing to patient-centered care in radiology practice. Key Points: AI simplifies complex breast imaging reports, enhancing patient understanding. Simplified reports from AI maintain accuracy, improving patient comprehension significantly. Implementing AI reports enhances patient engagement and communication in breast imaging. [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. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Digital breast tomosynthesis (DBT) has outpaced digital mammography in clinical adoption in the United States; however, substantial technological limitations remain to image quality in DBT, including undersampling from a one‐dimensional (1D) scan geometry, x‐ray source motion during acquisition, and patient motion artifacts from long exam times.A thermionic cathode x‐ray system employing two‐dimensional (2D, planar) multiple x‐ray‐source arrays (MXA) is proposed to improve DBT image quality.A 1D MXA, consisting of a linear array of thermionic cathodes was used to simulate a 2D MXA. The 1D MXA included 11 focal spots separated by a distance of Δd${{\Delta}}d$ = 23 mm. The 11 cathodes were paired with 11 molybdenum 50 mm diameter anode disks, mounted on a rotating shaft within a single vacuum enclosure. Image quality was investigated as a function of MXA configuration by integrating the 1D MXA with a 200 × 250 mm2 flat panel detector at a source‐to‐detector distance of 630 mm, resulting in a 20° tomographic arc. To simulate a 2D MXA, the detector (with phantom) was translated orthogonally to the linear array by a distance (δ$\delta $) ranging from δ$\delta $ = 0 mm (conventional 1D) to δ$\delta $ = 57 mm. All sources operated at 30 kV with 80 mA and 4.5 mAs/pulse, yielding ∼100 mAs per DBT dataset. DBT reconstructions involved 22 projections and used filtered backprojection with a ramp and Hann apodization filter. Volumetric reconstructions for each source were weighted by sampling differences between sources, and averaged. Image quality was assessed in terms of contrast‐to‐noise ratio (CNR), background clutter noise and power spectrum, and slice sensitivity profile (SSP) using a set of physical phantoms, including: (i) contrast‐detail signals coupled to spherical clutter (PMMA in air); (ii) an SSP phantom; (iii) a commercial “breast” phantom (CIRS BR3D, Sun Nuclear, Norfolk, VA); and (iv) bovine muscle.Background clutter noise amplitude reduced monotonically from the 1D MXA (σclutter = 5.9 A.U., δ$\delta $ = 0 mm) and 2D MXA arrays with increasing δ$\delta $, with statistical significance between the 1D MXA and 2D MXA with δ$\delta $ = 57 mm (σclutter = 5.0 A.U., p < 0.001). The contrast‐detail/clutter phantom demonstrated CNR from the 2D MXA (δ  = 57 mm) outperforming the 1D MXA in all combinations of contrast and detail. 2D power spectrum analysis of clutter demonstrated a pronounced Fourier domain null cone for the 1D MXA in the anterior field‐of‐view (away from the 1D MXA position), whereas the 2D MXA geometry (δ = 57 mm) did not exhibit the null cone. The SSP was 15%–50% narrower (FWHM) for the 2D versus the 1D geometry, across all reconstruction setups.The advantages of a 2D source geometry for DBT imaging were demonstrated quantitatively compared to a conventional 1D line of x‐ray sources. The improvement in the 2D geometry was attributed both to improved Fourier domain sampling and reduced SSP. We conclude that 2D MXA sources have the potential to substantially improve DBT imaging in comparison to existing commercial DBT systems. [ABSTRACT FROM AUTHOR]

In order to assess the perceptions and expectations of the radiology staff about artificial intelligence (AI), we conducted an online survey among ESR members (January–March 2024). It was designed considering that conducted in 2018, updated according to recent advancements and emerging topics, consisting of seven questions regarding demographics and professional background and 28 AI questions. Of 28,000 members contacted, 572 (2%) completed the survey. AI impact was predominantly expected on breast and oncologic imaging, primarily involving CT, mammography, and MRI, and in the detection of abnormalities in asymptomatic subjects. About half of responders did not foresee an impact of AI on job opportunities. For 273/572 respondents (48%), AI-only reports would not be accepted by patients; and 242/572 respondents (42%) think that the use of AI systems will not change the relationship between the radiological team and the patient. According to 255/572 respondents (45%), radiologists will take responsibility for any AI output that may influence clinical decision-making. Of 572 respondents, 274 (48%) are currently using AI, 153 (27%) are not, and 145 (25%) are planning to do so. In conclusion, ESR members declare familiarity with AI technologies, as well as recognition of their potential benefits and challenges. Compared to the 2018 survey, the perception of AI's impact on job opportunities is in general slightly less optimistic (more positive from AI users/researchers), while the radiologist's responsibility for AI outputs is confirmed. The use of large language models is declared not only limited to research, highlighting the need for education in AI and its regulations. Critical relevance statement: This study critically evaluates the current impact of AI on radiology, revealing significant usage patterns and clinical implications, thereby guiding future integration strategies to enhance efficiency and patient care in clinical radiology. Key Points: The survey examines ESR member's views about the impact of AI on radiology practice. AI use is relevant in CT and MRI, with varying impacts on job roles. AI tools enhance clinical efficiency but require radiologist oversight for patient acceptance. [ABSTRACT FROM AUTHOR]

Objective: To determine whether adding elastography strain ratio (SR) and a deep learning based computer-aided diagnosis (CAD) system to breast ultrasound (US) can help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3 and 4a-c categories and avoid unnecessary biopsies. Methods: This prospective, multicentre study included 1049 masses (691 benign, 358 malignant) with assigned BI-RADS 3 and 4a-c between 2020 and 2022. CAD results was dichotomized possibly malignant vs. benign. All patients underwent SR and CAD examinations and histopathological findings were the standard of reference. Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after reclassified (new BI-RADS 3) with SR and CAD were the outcome measures. Results: Following the routine conventional breast US assessment, 48.6% (336 of 691 masses) underwent unnecessary biopsies. After reclassifying BI-RADS 4a masses (SR cut-off <2.90, CAD dichotomized possibly benign), 25.62% (177 of 691 masses) underwent an unnecessary biopsies corresponding to a 50.14% (177 vs. 355) reduction of unnecessary biopsies. After reclassification, only 1.72% (9 of 523 masses) malignancies were missed in the new BI-RADS 3 group. Conclusion: Adding SR and CAD to clinical practice may show an optimal performance in reclassifying BI-RADS 4a to 3 categories, and 50.14% masses would be benefit by keeping the rate of undetected malignancies with an acceptable value of 1.72%. Advances in knowledge: Leveraging the potential of SR in conjunction with CAD holds immense promise in substantially reducing the biopsy frequency associated with BI-RADS 3 and 4A lesions, thereby conferring substantial advantages upon patients encompassed within this cohort. [ABSTRACT FROM AUTHOR]

A bilayer MOF reporter (ZIF-67@FAM-mRNA@ZIF-8) was synthesized, and the ZIF-67 was used as a carrier and fluorescent quencher to connect the FAM reporter through electrostatic adsorption and coordination effect. The ZIF-8 covering the outer layer can improve the stability of the probe and cell permeability, which helps the FAM reporter effectively release. After entering the cancer cells, the acidic environment in the cells induced the decomposition of ZIF-8. The excess ATP in the tumor cells competitively binds ZIF-67, causing the FAM reporter to shed and restore fluorescence. The shed FAM reporter was specifically bound to the overexpressed miRNA-21 in breast cancer cells to achieve fluorescence imaging and therapy of breast cancer. The results of specific imaging and apoptosis experiments of breast cancer cells indicate that bilayer MOF nanomachine provides an effective nanotherapy platform for accurate fluorescence imaging. [ABSTRACT FROM AUTHOR]

This paper presents a prototype of a portable and modular electrical impedance tomography (EIT) system for breast tumor detection. The proposed system uses MATLAB to generate three-dimensional representations of breast tissue. The modular architecture of the system allows for flexible customization and scalability. It consists of several interconnected modules. Each module can be easily replaced or upgraded, facilitating system maintenance and future enhancements. Testing of the prototype has shown promising results in preliminary screening based on experimental studies. Agar models were used for the experimental stage of this project. The 3D representations provide clinicians with valuable information for accurate diagnosis and treatment planning. Further research and refinement of the system is warranted to validate its performance in future clinical trials. [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]

In the United Kingdom, radiographers with a qualification in image interpretation have interpreted mammograms since 1995. These radiographers work under the title of radiography advanced practitioners (RAP) or Consultant Radiographer. This study extends upon what has been very recently published by exploring further clinical, non-clinical and experiential factors that may impact the reporting performance of RAPs. Fifteen RAPs interpreted an image test set of 60 2D mammograms of known truth using the Detected-X software platform. Unknown to the reader, twenty cases contained a malignancy. Sensitivity, specificity, lesion sensitivity, receiver operating characteristic (ROC) and jack-knife free response operating characteristic (AFROC) values were established for each RAP. Specific features that had significant impact on accuracy were identified using Student's-T and Mann Whitney tests. RAPs with more than 10 years' experience in image interpretation, compared to those with less than 10 years' experience, demonstrated lower specificity (51.3% vs 84.8%, p = 0.0264), ROC (0.83 vs 0.91, p = 0.0264) and AFROC (0.75 vs 0.87, p = 0.0037) values. Further, higher sensitivity values of 90.7% were seen in those RAPs who had an eye test in the last year compared to those who had not, 82% (p = 0.021). Other changes are presented in the paper. These data reveal previously unidentified factors that impact the diagnostic efficacy of RAPs when interpreting mammographic images. Highlighting such findings will empower screening authorities to better examine ways of standardising performance and offer a baseline for performance benchmarks. This study for the first time performs an initial exploration of the factors that may be associated with RAP performance when interpreting screening mammograms. [ABSTRACT FROM AUTHOR]

Background: In breast diffusion‐weighted imaging (DWI), distortion and physiologic artifacts affect clinical interpretation. Image quality can be optimized by addressing the effect of phase encoding (PE) direction on these artifacts. Purpose: To compare distortion artifacts in breast DWI acquired with different PE directions and polarities, and to discuss their clinical implications. Study Type: Prospective. Population: Eleven healthy volunteers (median age: 47 years old; range: 22–74 years old) and a breast phantom. Field Strength/Sequence: Single‐shot echo planar DWI and three‐dimensional fast gradient echo sequences at 3 T. Assessment: All DWI data were acquired with left–right, right–left, posterior–anterior, and anterior–posterior PE directions. In phantom data, displacement magnitude was evaluated by comparing the location of landmarks in anatomical and DWI images. Three breast radiologists (5, 17, and 23 years of experience) assessed the presence or absence of physiologic artifacts in volunteers' DWI datasets and indicated their PE‐direction preference. Statistical Tests: Analysis of variance with post‐hoc tests were used to assess differences in displacement magnitude across DWI datasets and observers. A binomial test and a chi‐squared test were used to evaluate if each in vivo DWI dataset had an equal probability (25%) of being preferred by radiologists. Inter‐reader agreement was evaluated using Gwet's AC1 agreement coefficient. A P‐value <0.05 was considered statistically significant. Results: In the phantom study, median displacement was the significantly largest in posterior–anterior data. While the displacement in the anterior–posterior and left–right data were equivalent (P = 0.545). In the in vivo data, there were no physiological artifacts observed in any dataset, regardless of PE direction. In the reader study, there was a significant preference for the posterior–anterior datasets which were selected 94% of the time. There was good agreement between readers (0.936). Data Conclusion: This study showed the impact of PE direction on distortion artifacts in breast DWI. In healthy volunteers, the posterior‐to‐anterior PE direction was preferred by readers. Level of Evidence: 2 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Breast cancer is the second most prevalent cause of cancer death and the most common malignancy among women, posing a life-threatening risk. Treatment for breast cancer can be highly effective, with a survival chance of 90% or higher, especially when the disease is detected early. This paper introduces a groundbreaking deep U-Net framework for mammography breast cancer images to perform automatic detection of abnormalities. The objective is to provide segmented images that show areas of tumors more accurately than other deep learning techniques. The proposed framework consists of three steps. The first step is image preprocessing using the Li algorithm to minimize the cross-entropy between the foreground and the background, contrast enhancement using contrast-limited adaptive histogram equalization (CLAHE), normalization, and median filtering. The second step involves data augmentation to mitigate overfitting and underfitting, and the final step is implementing a convolutional encoder-decoder network-based U-Net architecture, characterized by high precision in medical image analysis. The framework has been tested on two comprehensive public datasets, namely INbreast and CBIS-DDSM. Several metrics have been adopted for quantitative performance assessment, including the Dice score, sensitivity, Hausdorff distance, Jaccard coefficient, precision, and F1 score. Quantitative results on the INbreast dataset show an average Dice score of 85.61% and a sensitivity of 81.26%. On the CBIS-DDSM dataset, the average Dice score is 87.98%, and the sensitivity reaches 90.58%. The experimental results ensure earlier and more accurate abnormality detection. Furthermore, the success of the proposed deep learning framework in mammography shows promise for broader applications in medical imaging, potentially revolutionizing various radiological practices. [ABSTRACT FROM AUTHOR]

Breast cancer, a prevalent and life-threatening disease, necessitates early detection for the effective intervention and the improved patient health outcomes. This paper focuses on the critical problem of identifying breast cancer using a model called Attention U-Net. The model is utilized on the Breast Ultrasound Image Dataset (BUSI), comprising 780 breast images. The images are categorized into three distinct groups: 437 cases classified as benign, 210 cases classified as malignant, and 133 cases classified as normal. The proposed model leverages the attention-driven U-Net's encoder blocks to capture hierarchical features effectively. The model comprises four decoder blocks which is a pivotal component in the U-Net architecture, responsible for expanding the encoded feature representation obtained from the encoder block and for reconstructing spatial information. Four attention gates are incorporated strategically to enhance feature localization during decoding, showcasing a sophisticated design that facilitates accurate segmentation of breast tumors in ultrasound images. It displays its efficacy in accurately delineating and segregating tumor borders. The experimental findings demonstrate outstanding performance, achieving an overall accuracy of 0.98, precision of 0.97, recall of 0.90, and a dice score of 0.92. It demonstrates its effectiveness in precisely defining and separating tumor boundaries. This research aims to make automated breast cancer segmentation algorithms by emphasizing the importance of early detection in boosting diagnostic capabilities and enabling prompt and targeted medical interventions. [ABSTRACT FROM AUTHOR]

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

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]

Current breast cancer screening relies on mammography, digital breast tomosynthesis and breast ultrasound. In select populations, breast MRI is also of great utility. However, multiple factors limit widespread use of breast MRI for screening. Efforts have been made to increase the availability of breast MRI for screening, in large part due to the increased cancer detection rate of breast MRI compared to mammography. Techniques include shortening standard breast MRI protocols with the potential for accommodating MRI screening in a higher number of patients. This review will explain the role of abbreviated breast MRI and ultrafast breast MRI in breast imaging, and detail how these approaches differ from standard dynamic contrast-enhanced breast MRI. In addition, limitations and advantages of these techniques will also be discussed. [ABSTRACT FROM AUTHOR]

Introduction: The purpose of the study is to assess the role of preoperative magnetic resonance (MR) imaging on the surgical management of invasive lobular carcinoma (ILC) and to evaluate whether breast density and background parenchymal enhancement (BPE) influence surgical treatment. Methods: This retrospective study was conducted on 56 patients who were diagnosed with ILC between 2014 and 2020. All patients had mammogram and ultrasound. Preoperative MRI was available in 34 patients. Age, menopausal status, breast density, BPE, multifocality/multicentricity and surgical treatment were collected. Results: Mean pathological tumour size was 36.4 mm (range 5–140 mm). Dense breasts had larger tumours compared to non‐dense breasts (P = 0.072). Of the 34 patients with MRI, 6 opted for mastectomy. Of the remaining 28 cases, MRI findings upgraded surgery to mastectomy in 54% (15/28) because mammogram/ultrasound underestimated tumour extent in 25% (7/28), or multifocal/multicentric disease was identified in 29% (8/28). Tumour size was underestimated by MRI in 7% (2/28). In the non‐MRI subgroup, 64% (14/22) of patients underwent breast‐conserving surgery, but 29% of them (4/14) required a second‐stage mastectomy due to extensive margin involvement. There was no difference in mastectomy rate between patients with MRI (62%) and without MRI (55%) (P = 0.061). Tumour size correlation between MRI and histopathology demonstrated an excellent intraclass correlation coefficient (P < 0.001). Surgical treatment recommendation was not significantly impacted by breast density or BPE. Conclusion: Breast MRI improves surgical management of patients with ILC in providing additional diagnostic information often missed with standard imaging modalities, and without increasing mastectomy rate. Surgical treatment is not impacted by breast density or BPE. [ABSTRACT FROM AUTHOR]

Significance: Near-infrared optical imaging methods have shown promise for monitoring response to neoadjuvant chemotherapy (NAC) for breast cancer, with endogenous contrast coming from oxy- and deoxyhemoglobin. Spatial frequency domain imaging (SFDI) could be used to detect this contrast in a low-cost and portable format, but it has limited imaging depth. It is possible that local tissue compression could be used to reduce the effective tumor depth. Aim: To evaluate the potential of SFDI for therapy response prediction, we aim to predict how changes to tumor size, stiffness, and hemoglobin concentration would be reflected in contrast measured by SFDI under tissue compression. Approach: Finite element analysis of compression on an inclusion-containing soft material is combined with Monte Carlo simulation to predict the measured optical contrast. Results: When the effect of compression on blood volume is not considered, contrast gain from compression increases with the size and stiffness of the inclusion and decreases with the inclusion depth. With a model of reduction of blood volume from compression, compression reduces imaging contrast, an effect that is greater for larger inclusions and stiffer inclusions at shallower depths. Conclusions: This computational modeling study represents a first step toward tracking tumor changes induced by NAC using SFDI and local compression. [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]

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

Previous studies have emphasized the potential of threshold image segmentation for early breast cancer detection. However, traditional methods encounter challenges regarding low segmentation efficiency and accuracy. Addressing this, the ant colony optimization algorithm for continuous optimization (ACOR) shows promise. Yet, existing ACOR variants still grapple with poor initial population quality, affecting convergence speed and avoiding local optimization. These issues impact segmentation efficiency and accuracy. To tackle them, this study introduces RESACO, an enhanced ACOR version integrating three novel optimization strategies: resampling initialization (RIS), elite exploration (EES), and strengthened convergence mechanism (SCM). RIS enhances initial population quality by resampling regions with individuals demonstrating superior fitness and segmentation efficiency. EES promotes exploration across the search space, preventing local optima entrapment and enhancing model stability. SCM expediting convergence, segmentation efficiency, and precision. RESACO's performance is assessed through extensive experiments using IEEE CEC 2014 and IEEE CEC 2022 benchmark functions, including ablation experiments and comparisons with basic and improved algorithms and ACOR variants. Subsequently, the threshold image segmentation model based on RESACO is compared with other models using metaheuristic algorithms for segmenting realistic breast cancer medical images. Results demonstrate the proposed model's faster convergence and higher segmentation accuracy, preserving more lesion tissue details. [ABSTRACT FROM AUTHOR]

Addressing the challenge of achieving precise subtype classification of breast cancer histopathology images with limited resources, a lightweight model incorporating multi-stage information fusion and an attention mechanism is proposed for this task. Using MobileNetV3 as the backbone, a multi-stage fusion strategy captures the rich image information in breast cancer histopathology images. Additionally, the selective kernel (SK) attention mechanism is introduced in the initial stages of feature extraction, while an improved squeeze-and-excitation coordinate attention (SCA) mechanism is integrated in the later stages to enhance the extraction of both underlying and semantic features. The final feature representations for subtype classification are determined based on the attention map weights computed at each stage. The experimental results demonstrate the model's outstanding recognition performance on the BreakHis dataset, achieving subtype classification accuracies of 96.259%, 94.763%, 95.511%, and 94.015% at four different magnifications. [ABSTRACT FROM AUTHOR]

Dedicated breast CT is an imaging modality that provides true 3D imaging of the breast with many advantages over current conventional breast imaging modalities. The addition of intravascular contrast increases the sensitivity of breast CT substantially. As such, there are immediate potential applications in the clinical workflow. These include using breast CT to replace much of the traditional diagnostic workup when faced with indeterminate breast lesions. Contrast-enhanced breast CT may be appropriate as a supplemental screening tool for women at high risk of breast cancer, similar to breast MRI. In addition, emerging studies are demonstrating the utility of breast CT in neoadjuvant chemotherapy tumor response monitoring as well as planning for surgical treatment options. While short exam times and fully 3D imaging in a noncompressed position are advantages of this modality, limited coverage of chest wall/axilla due to prone positioning and use of ionizing radiation are drawbacks. To date, several studies have reported on the performance characteristics of this promising modality. [ABSTRACT FROM AUTHOR]

Background Due to the superficial location, suspicious findings of the nipple-areolar complex (NAC) are not amenable to stereotactic or MRI-guided sampling and have historically necessitated surgical biopsy or skin-punch biopsy. There are limited reports of US-guided core biopsy of the nipple (US-CBN). Objective We report our nearly 3-year pilot experience with US-CBN at an academic breast imaging center. Methods An institutional review board–exempt and HIPAA-compliant retrospective review was performed. We assessed patient demographics, breast imaging characteristics, procedural data, pathology, and outcomes. Results Nine female patients aged 27 to 64 underwent US-CBN from January 2021 to October 2023. Initial imaging abnormalities included abnormal MRI enhancement, mammographic calcifications, and sonographic masses. After initial or second-look US, all imaging findings had sonographic correlates for biopsy specimens, the majority of which were sonographic masses (8/9). US-CBN was performed by 6 breast radiologists using a variety of devices. All biopsy specimen results were concordant with sonographic abnormalities, although 1 was considered discordant from the initial abnormality seen on MRI. There were no complications, and discomfort during the procedure was well-treated. Two patients (22%, 2/9) were diagnosed with malignancy. Conclusion This pilot study demonstrated that US-CBN can be performed by a breast radiologist for definitive diagnosis of suspicious nipple abnormalities seen on breast imaging, avoiding surgery, and maintaining nipple integrity. In our population, 22% (2/9) of US-CBNs revealed malignancy. [ABSTRACT FROM AUTHOR]

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.

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: The use of iodine-based contrast agent for better delineation of tumors in breast CT (bCT) has been shown to be compelling, similar to the tumor enhancement in contrast-enhanced breast MRI. Contrast-enhanced bCT (CE-bCT) is a relatively new tool, and a structured evaluation of different imaging parameters at play has yet to be conducted. In this investigation, data sets of acquired bCT images from 253 patients imaged at our institution were used in concert with simulated mathematically inserted spherical contrast-enhanced lesions to study the role of contrast enhancement on detectability. PURPOSE: To quantitatively evaluate the improvement in lesion detectability due to contrast enhancement across lesion diameter, section thickness, view plane, and breast density using a pre-whitened matched filter (PWMF) model observer. METHODS: The relationship between iodine concentration and Hounsfield units (HU) was measured using spectral modeling. The lesion enhancement from clinical CE-bCT images in 22 patients was evaluated, and the average contrast enhancement (ΔHU) was determined. Mathematically generated spherical mass lesions of varying diameters (1, 3, 5, 9, 11, 15 mm) and contrast enhancement levels (0, 0.25, 0.50, 0.75, 1) were inserted at random locations in 253 actual patient bCT datasets. Images with varying thicknesses (0.4-19.8 mm) were generated by slice averaging, and the role of view plane (coronal and axial planes) was studied. A PWMF was used to generate receiver operating characteristic (ROC) curves across parameters of lesion diameter, contrast enhancement, section thickness, view plane, and breast density. The area under the ROC curve (AUC) was used as the primary performance metric, generated from over 90,000 simulated lesions. RESULTS: An average 20% improvement (ΔAUC = 0.1) in lesion detectability due to contrast enhancement was observed across lesion diameter, section thickness, breast density, and view plane. A larger improvement was observed when stratifying patients based on breast density. For patients with VGF ≤ 40%, detection performance improved up to 20% (until AUC →1), and for patients with denser breasts (VGF > 40%), detection performance improved more drastically, ranging from 20% to 80% for 1- and 5-mm lesions. For the 1 mm lesion, detection performance raised slightly at the 1.2 mm section thickness before falling off as thickness increased. For larger lesions, detection performance was generally unaffected as section thickness increased up until it reached 5.8 mm, where performance began to decline. Detection performance was higher in the axial plane compared to the coronal plane for smaller lesions and thicker sections. CONCLUSIONS: For emerging diagnostic tools like CE-bCT, it is important to optimize imaging protocols for lesion detection. In this study, we found that intravenous contrast can be used to detect small lesions in dense breasts. Optimal section thickness for detectability has dependencies on breast density and lesion size, therefore, display thickness should be adjusted in real-time using display software. These findings may be useful for the development of CE-bCT as well as other x-ray-based breast imaging modalities.

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]

For high-risk women, breast magnetic resonance (MR) is the preferred supplemental imaging option, but spatial access differences may exacerbate disparities in breast care. This was a cross-sectional study examining distance between ZIP codes and the nearest breast imaging facility (MR, mammography, ultrasound) using 2023 data from the Food and Drug Administration and the American College of Radiology. Linear regression was used to assess distance differences controlling for Area Deprivation Index (ADI), urbanicity, and population size. Analyses were conducted in 2024. Among the 29,629 ZIP codes with an ADI and known urbanicity, unadjusted mean distance to breast MR was 23.2±25.1 miles (SD) compared with 8.2±8.3 for mammography and 22.2±25.0 for ultrasound. Hence, the average distance to breast MR facilities was 2.8 times further than to mammography facilities. ADI and urbanicity were associated with increased distance to the nearest breast imaging facility. The additional miles associated with the least advantaged areas compared with most advantaged areas was 12.2 (95%CI: 11.3, 13.2) for MR, 11.5 miles (95%CI: 10.6, 12.3) for ultrasound, and 2.4 (95%CI: 2.1, 2.7) for mammography. Compared with metropolitan areas, the additional miles to breast MR facilities was 23.2 (95%CI: 22.5, 24.0) for small/rural areas. Spatial access is substantially better for mammography sites compared with breast MR or ultrasound sites. Given these findings, consideration of options to mitigate the impact of differential access should be considered. For example, mammography sites could offer contrast-enhanced mammography. Future research should examine the feasibility and effectiveness of this and other options. [ABSTRACT FROM AUTHOR]

BACKGROUND & OBJECTIVE: CEM is a valuable imaging diagnostic tool for diagnosing and characterizing breast lesions. It offers potential to overcome some of the limitations of conventional mammography. The objective of this study was to report the initial experience of implementation of CEM as a breast imaging modality, its diagnostic utility, imaging features of lesions and cost analysis. METHODS: Patients who underwent CEM examinations from January 1, 2022, to June 31, 2023, were included. The data included patient demographics, prior imaging, clinical indications, enhancement patterns on CEM, and histological diagnoses. The patients with insufficient electronic health records / imaging / histopathology were excluded. RESULTS: The study included 36 patients who underwent CEM with an average age of 47.1 years. The most common clinical indication for performing CEM was preoperative staging, accounting for 30 of the total cases. Background enhancement was predominantly mild in 19 cases. Out of the 30 (83%) malignant cases, 29 showed enhancement of the lesions on CEM. No enhancement was seen in one case of response evaluation which revealed complete pathological response on post-surgical pathology. The remaining six cases with no enhancing lesion, on ultrasound were reported as BIRADS I and BIRADS II on ultrasonography. On CEM, 26 (87%) of the malignant lesions showed mass-like enhancement and all were infiltrating ductal carcinoma on histopathology. CONCLUSION: CEM has shown promise as a low-cost and accurate diagnostic technique for breast cancer diagnosis and local staging. It has the potential to overcome the limitations of traditional mammography and MRI. [ABSTRACT FROM AUTHOR]

In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI‐enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI‐enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. Level of Evidence: 5 Technical Efficacy: Stage 6 [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 (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.

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]