Breast Imaging Research Articles

Diagnostic Accuracy and Incremental Value of Contrast-Enhanced Mammography Compared With Full Field Digital Mammography in a Tertiary Cancer Care Center.

To assess the diagnostic accuracy and incremental value of contrast-enhanced mammography (CEM) compared with full-field digital mammography (FFDM). A retrospective analysis was performed with 150 consecutive patients who underwent CEM at our institute between November 2020 and February 2021, fulfilling the inclusion criteria. The first round of analysis included a review of FFDM with an interpretation of findings as per the Breast Imaging Reporting and Data System (BIRADS) lexicon and the assignment of the BIRADS category to the detected abnormalities. After this documentation, a second round of analysis included a review of recombined subtracted images of CEM. The diagnostic accuracy of FFDM and CEM was calculated with histopathology as the gold standard. Among the 150 cases assessed, 202 lesions were detected with histopathological correlation, of which 42 were benign and 160 were malignant. The sensitivity of FFDM was 90.6% compared to 98.12% for CEM. The specificity of FFDM was 66.7% compared to 76.19% for CEM. The negative predictive value (NPV) of FFDM was low, at 65.12%; CEM showed a better NPV, at 91.43%. The positive predictive value (PPV) was almost the same, at 94.01% for CEM and 91.19% for FFDM. The area under the curve (AUC) was superior for CEM compared to that of FFDM, with a value of 0.87. FFDM had a low sensitivity, especially in dense breast parenchyma, at 88.79% and a specificity of 70%, whereas CEM showed a higher sensitivity, specificity, and NPV, measuring 99.14%, 76.67%, and 95.83%, respectively. Superior sensitivity and high NPV for CEM make it a preferable modality compared with FFDM, especially in dense breast parenchyma, where CEM overcomes the limitations of FFDM. We conclude that CEM is superior to FFDM in evaluating the extent of disease, additional satellite lesion detection, and ruling out ambiguous findings.

Lesion conspicuity and contrast kinetics as predictors to differentiate benign and malignant breast lesions in contrast-enhanced mammogram

BackgroundContrast-enhanced mammography (CEM) is a recently developed, cost-effective imaging technique that offers both anatomical and functional breast imaging. Lesion conspicuity, a newly introduced lexicon in the ACR BIRADS supplementary atlas on CEM (2022), lacks sufficient data to correlate with malignancy likelihood. The feasibility of assessing contrast kinetics with CEM remains uncertain, and there is a scarcity of available data. Our research aims to address these gaps.ResultsTwo radiologists, blinded to pathological reports, independently evaluated 504 CEM enhanced breast lesions with histopathology reports, out of which 176 were benign and 328 were malignant. Subjective qualitative assessment of lesion conspicuity and contrast kinetics was done for each enhancing lesion. The lesion conspicuity was classified as low, moderate, or high. The kinetic behavior of each lesion was categorized into either persistent, plateau, or washout. The distribution of lesion conspicuity among benign and malignant lesions, respectively, was as follows: for low conspicuity, 74.4% versus 25.6%; for moderate conspicuity, 30.6% versus 69.4%; and for high conspicuity, 8.4% versus 91.6%. Regarding contrast kinetics and their distribution between benign and malignant lesions, persistent kinetics was detected in 95.6% compared to 4.4%, plateau kinetics in 43.4% versus 56.6%, and washout kinetics in 3.5% versus 96.5%. Statistically significant differences in distribution between benign and malignant lesions were observed for both lexicons (P < 0.001). The inter-observer agreement for lesion conspicuity (kappa = 0.97) and contrast kinetics (kappa = 0.92) was deemed excellent.ConclusionThe addition of lesion conspicuity and contrast kinetics as lexicons in CEM could enhance its diagnostic accuracy.

Effects of Left Internal Mammary Artery (LIMA) Harvesting During Coronary Artery By-Pass Grafting Surgery on Benign Breast Diseases

Introduction: Coronary artery bypass surgery of the primary conduits employed in coronary artery bypass grafting surgery (CABG) is the left internal mammary artery (LIMA), which has garnered considerable attention due to its favorable characteristics and outcomes. There are not enough studies examining the relationship between benign breast diseases (BBD) and LIMA removal. This study is one of the pioneering studies investigating the effects of LIMA removal after bypass surgery in patients with benign breast diseases. This study aims to evaluate the effect of LIMA removal on benign breast diseases in female patients undergoing bypass surgery by analyzing pre- and post-operative breast imaging. Material and Methods: A total of 47 of 452 female patients who underwent bypass surgery between February 18, 2019, and December 31, 2021, were included in the study. All patients’ LIMA were harvested and evaluated, regarding any progression of benign breast diseases. Results: There was a significant difference between the Breast Imaging Reporting and Data System (BI-RADS) classes of the patients before and after CABG (p

Overcoming challenges in conducting early phase breast cancer prevention trials: Bazedoxifene and conjugated estrogens vs waitlist control

BackgroundThe combination of bazedoxifene 20 mg (BZA) and conjugated estrogens 0.45 mg (CE) marketed as Duavee® is approved for vasomotor symptom relief and osteoporosis prevention. Our pilot study suggested it had potential breast cancer risk reduction, and we proposed a multisite Phase IIB primary prevention trial assessing change in breast imaging and tissue risk biomarkers. By the time funding was acquired in February 2021, Duavee® was unavailable with an uncertain return date. A redesign was needed to salvage the study. MethodsThe basic trial design was minimally altered. Women age 45–64 at elevated risk for breast cancer with vasomotor symptoms and no menses for at least 2 months have mammography, phlebotomy, and benign breast tissue sampling before and after 6 months of intervention. However, instead of Duavee® (single pill) vs placebo, women are randomized to 6 months of BZA + CE vs Waitlist. Those initially randomized to Waitlist can receive BZA + CE after 6 months. The primary endpoint is between arm difference in change in a fully automated measure of mammographic density with blood and tissue-based secondary endpoints. OutcomesAccrual initiation was delayed due to contractual difficulties surrounding BZA importation during COVID-19 and deploying a fully automated method (Volpara®) to assess the primary endpoint. To accommodate this delay, a mid-grant no cost extension along with amended eligibility requirements were employed. 61/120 participants needed were entered in the initial 27 months of accrual and 37 months of funding. Despite a late start, accrual is likely to be completed within the funding period.

Green Nanotechnology Through Papain Nanoparticles: Preclinical in vitro and in vivo Evaluation of Imaging Triple-Negative Breast Tumors.

Recent advancements in nanomedicine and nanotechnology have expanded the scope of multifunctional nanostructures, offering innovative solutions for targeted drug delivery and diagnostic agents in oncology and nuclear medicine. Nanoparticles, particularly those derived from natural sources, hold immense potential in overcoming biological barriers to enhance therapeutic efficacy and diagnostic accuracy. Papain, a natural plant protease derived from Carica papaya, emerges as a promising candidate for green nanotechnology-based applications due to its diverse medicinal properties, including anticancer properties. This study presents a novel approach in nanomedicine and oncology, exploring the potential of green nanotechnology by developing and evaluating technetium-99m radiolabeled papain nanoparticles (99mTc-P-NPs) for imaging breast tumors. The study aimed to investigate the efficacy and specificity of these nanoparticles in breast cancer models through preclinical in vitro and in vivo assessments. Papain nanoparticles (P-NPs) were synthesized using a radiation-driven method and underwent thorough characterization, including size, surface morphology, surface charge, and cytotoxicity assessment. Subsequently, P-NPs were radiolabeled with technetium-99m (99mTc), and in vitro and in vivo studies were conducted to evaluate cellular uptake at tumor sites, along with biodistribution, SPECT/CT imaging, autoradiography, and immunohistochemistry assays, using breast cancer models. The synthesized P-NPs exhibited a size mean diameter of 9.3 ± 1.9 nm and a spherical shape. The in vitro cytotoxic activity of native papain and P-NPs showed low cytotoxicity in HUVEC, MDA-MB231, and 4T1 cells. The achieved radiochemical yield was 94.2 ± 3.1% that were sufficiently stable (≥90%) for 6 h. The tumor uptake achieved in the 4T1 model was 2.49 ± 0.32% IA/g at 2h and 1.51 ± 0.20% IA/g at 6h. In the spontaneous breast cancer model, 1.19 ± 0.20% IA/g at 2h and 0.86 ± 0.31% IA/g at 6h. SPECT/CT imaging has shown substantial tumor uptake of the new nanoradiopharmaceutical and clear tumor visualization. 99mTc-P-NPs exhibited a high affinity to tumoral cells confirmed by ex vivo autoradiography and immunohistochemistry assays. The findings underscore the potential of green nanotechnology-driven papain nanoparticles as promising agents for molecular imaging of breast and other tumors through SPECT/CT imaging. The results represent a substantial step forward in the application of papain nanoparticles as carriers of diagnostic and therapeutic radionuclides to deliver diagnostic/therapeutic payloads site-specifically to tumor sites for the development of a new generation of nanoradiopharmaceuticals.

Combining Radiomics and Autoencoders to Distinguish Benign and Malignant Breast Tumors on US Images.

Background US is clinically established for breast imaging, but its diagnostic performance depends on operator experience. Computer-assisted (real-time) image analysis may help in overcoming this limitation. Purpose To develop precise real-time-capable US-based breast tumor categorization by combining classic radiomics and autoencoder-based features from automatically localized lesions. Materials and Methods A total of 1619 B-mode US images of breast tumors were retrospectively analyzed between April 2018 and January 2024. nnU-Net was trained for lesion segmentation. Features were extracted from tumor segments, bounding boxes, and whole images using either classic radiomics, autoencoder, or both. Feature selection was performed to generate radiomics signatures, which were used to train machine learning algorithms for tumor categorization. Models were evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity and were statistically compared with histopathologically or follow-up-confirmed diagnosis. Results The model was developed on 1191 (mean age, 61 years ± 14 [SD]) female patients and externally validated on 50 (mean age, 55 years ± 15]). The development data set was divided into two parts: testing and training lesion segmentation (419 and 179 examinations) and lesion categorization (503 and 90 examinations). nnU-Net demonstrated precision and reproducibility in lesion segmentation in test set of data set 1 (median Dice score [DS]: 0.90 [IQR, 0.84-0.93]; P = .01) and data set 2 (median DS: 0.89 [IQR, 0.80-0.92]; P = .001). The best model, trained with 23 mixed features from tumor bounding boxes, achieved an AUC of 0.90 (95% CI: 0.83, 0.97), sensitivity of 81% (46 of 57; 95% CI: 70, 91), and specificity of 87% (39 of 45; 95% CI: 77, 87). No evidence of difference was found between model and human readers (AUC = 0.90 [95% CI: 0.83, 0.97] vs 0.83 [95% CI: 0.76, 0.90]; P = .55 and 0.90 vs 0.82 [95% CI: 0.75, 0.90]; P = .45) in tumor classification or between model and histopathologically or follow-up-confirmed diagnosis (AUC = 0.90 [95% CI: 0.83, 0.97] vs 1.00 [95% CI: 1.00,1.00]; P = .10). Conclusion Precise real-time US-based breast tumor categorization was developed by mixing classic radiomics and autoencoder-based features from tumor bounding boxes. ClinicalTrials.gov identifier: NCT04976257 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Bahl in this issue.

Background parenchymal enhancement on contrast-enhanced mammography: associations with breast density and patient's characteristics.

To evaluate if background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM), graded according to the2022 CEM-dedicated Breast Imaging Reporting and Data System (BI-RADS) lexicon, is associated with breast density, menopausal status, and age. This bicentric retrospective analysis included CEM examinations performed for thework-up of suspicious mammographic findings. Three readers independently and blindly evaluated BPE on recombined CEM images and breast density on low-energy CEM images. Inter-reader reliability was estimated using Fleiss κ. Multivariable binary logistic regression was performed, dichotomising breast density and BPE as low (a/b BI-RADS categories, minimal/mild BPE) and high (c/d BI-RADS categories, moderate/marked BPE). A total of 200 women (median age 56.8years, interquartile range 50.5-65.6, 140/200 in menopause) were included. Breast density was classified as a in 27/200 patients (13.5%), as b in 110/200 (55.0%), as c in 52/200 (26.0%), and as d in 11/200 (5.5%), with moderate inter-reader reliability (κ = 0.536; 95% confidence interval [CI] 0.482-0.590). BPE was minimal in 95/200 patients (47.5%), mild in 64/200 (32.0%), moderate in 25/200 (12.5%), marked in 16/200 (8.0%), with substantial inter-reader reliability (κ = 0.634; 95% CI 0.581-0.686). At multivariable logistic regression, premenopausal status and breast density were significant positive predictors of high BPE, with adjusted odds ratios of 6.120 (95% CI 1.847-20.281, p = 0.003) and 2.416 (95% CI 1.095-5.332, p = 0.029) respectively. BPE on CEM is associated with well-established breast cancer risk factors, being higher in women with higher breast density and premenopausal status.

Evolution of a local adolescent breast imaging service

The pathway for paediatric patients (<18 years) with breast-related symptoms from primary care has evolved in recent years within our region. Historically patients were managed at their local site within the region with varied management approaches, including young patients undergoing potentially unnecessary breast biopsies, and timelines adopted. In 2020, the establishment of a dedicated paediatric breast clinic enabled a smoother referral process from primary care, an appropriate clinical setting for the patient population and a standardised management pathway. A retrospective review was performed of patients aged between 9 and 18 years referred to the breast service between 2017 and 2022. We reviewed the trends in referral numbers over the years, referral indications, ultrasound findings and clinical outcomes. There has been a yearly increment in the number of patients referred, primarily driven by the 15-18 age group. The primary referral indication was a breast lump (52%), gynaecomastia (14%), abscess (9%), and breast pain (6%). The most prevalent ultrasound findings were a normal breast (41%) and a fibroadenoma (25%). Of patients presenting with a breast lump, 40% demonstrated a normal ultrasound and 46% fibroadenomas. Conservative management was opted for the majority of patients with simple fibroadenomas and gynaecomastia. An unusual case of dermatofibrosarcoma protruberans was identified, stressing the importance of prompt surgical review for rapidly growing masses. The new clinic structure has optimized under-18 patient care, reducing unnecessary interventions and alleviating pressure on adult breast units. We propose a management pathway that can be adopted at a local/regional level.

Preliminary experiments on interpretable ChatGPT-assisted diagnosis for breast ultrasound radiologists.

Ultrasound is essential for detecting breast lesions. The American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) classification system is widely used, but its subjectivity can lead to inconsistency in diagnostic outcomes. Artificial intelligence (AI) models, such as ChatGPT-3.5, may potentially enhance diagnostic accuracy and efficiency in medical settings. This study aimed to assess the utility of the ChatGPT-3.5 model in generating BI-RADS classifications for breast ultrasound reports and its ability to replicate the "chain of thought" (CoT) in clinical decision-making to improve model interpretability. Breast ultrasound reports were collected, and ChatGPT-3.5 was used to generate diagnoses and treatment plans. We evaluated GPT-4's performance by comparing its generated reports to those from doctors with various levels of experience. We also conducted a Turing test and a consistency analysis. To enhance the interpretability of the model, we applied the CoT method to deconstruct the decision-making chain of the GPT model. A total of 131 patients were evaluated, with 57 doctors participating in the experiment. ChatGPT-3.5 showed promising performance in structure and organization (S&O), professional terminology and expression (PTE), treatment recommendations (TR), and clarity and comprehensibility (C&C). However, improvements are needed in BI-RADS classification, malignancy diagnosis (MD), likelihood of being written by a physician (LWBP), and ultrasound doctor artificial intelligence acceptance (UDAIA). Turing test results indicated that AI-generated reports convincingly resembled human-authored reports. Reproducibility experiments displayed consistent performance. Erroneous report analysis revealed issues related to incorrect diagnosis, inconsistencies, and overdiagnosis. The CoT investigation supports the potential of ChatGPT to replicate the clinical decision-making process and offers insights into AI interpretability. The ChatGPT-3.5 model holds potential as a valuable tool for assisting in the efficient determination of BI-RADS classifications and enhancing diagnostic performance.

SANAS-Net: spatial attention neural architecture search for breast cancer detection

&lt;p&gt;The utilization of mammography images plays a vital role in the prompt detection and treatment of breast cancer. Breast imaging techniques aid medical professionals in assessing the dimensions, morphology, and spatial orientation of breast lesions, facilitating the differentiation between benign and malignant conditions. Breast tissue can vary widely in terms of density, composition, and structure, leading to complexities in distinguishing between benign and malignant conditions. The primary contribution of this paper is the proposal of a spatial attention-based neural architecture search network (SANAS-Net) technique that incorporates a spatial attention mechanism, enabling the model to learn and prioritize key regions within mammograms (MMs). Multi-head attention is employed within the transformer blocks to effectively capture a wide range of spatial relations and feature interactions. Global contextual information was integrated into the transformer blocks by means of introducing positional embeddings. Several practical studies have been undertaken to verify the effectiveness of our methodology in identifying fully attentive networks that exhibit good performance in distinguishing between malignant and benign breast cancer cases. The experimental study reached a test accuracy of 89.95%, which is way higher than previously proposed algorithms for mammography imagebased breast cancer detection.&lt;/p&gt;

Maximizing Breast Cancer Detection Through Screening: A Comparative Analysis of Imaging-Based Approaches

Introduction/BackgroundThis study estimates the percentage of detectable breast cancers in the screening population that could be found with primary and supplemental screening, and provides cost estimates for population wide supplemental screening in the U.S. Materials and MethodsPublished estimates on cancer detection rates of 2D mammography, tomosynthesis (DBT), whole breast ultrasound (US), molecular breast imaging (MBI), contrast-enhanced mammography (CEM), and MRI, the number of mammograms conducted in the United States in 2023, and the proportion of dense breast tissue, were utilized. The maximum number of detectable cancers was projected from incremental cancer detection rates of the most sensitive supplemental screening method. The proportion of cancers detectable for each modality was calculated. In 2023, Medicare reimbursement rates were used to estimate supplemental screening costs. ResultsOut of 469,437 detectable cancers, 2D mammography could detect 190,531 (41%), leaving 278,906 undetected. Adding supplemental screening could detect a combined 220,165 cancers (47%) with DBT, 237,596 (51%) with US, 331,727 (71%) with MBI, 377,049 (80%) with CEM and 469,437 (100%) with MRI. The imaging cost in US dollars to provide supplemental screening to all individuals with dense breasts in 2023 was $933M for tomosynthesis, $1.84B for US, $3.87B for CEM, $4.16B for MBI, and $6.36B for MRI. ConclusionThe study highlights potential benefits from supplemental breast cancer screening, suggesting the combination of mammography and breast MRI offers the most effective detection method though at the highest imaging cost. These findings provide valuable insights to guide future research and inform decision-making in supplemental breast cancer screening strategies.

Male breast cancer and sexual aspects research and analysis (MASARA study): preliminary results

PurposeTo evaluate the impact of male breast cancer and its treatment on patient’s sexual function, including changes in sexual desire, performance, and satisfaction. To investigate changes in the body image experienced by male breast cancer patients and their perception in accessing healthcare in hospital facilities.MethodsWe administered three online questionnaires using Google Forms (Google LLC) to male breast cancer patients treated at our Institution from 2015 to 2023. The surveys were provided in Italian and distributed from March 1st to April 1st, 2024. A panel of one breast radiologist, one oncologist and one clinical psychologist designed a 22-item tailored survey to investigate imaging follow-up routine, sexual habits before and after treatment and perceived male breast cancer stigma. The International Index of Erectile Function and Italian version of the Body Image Scale were also administered. Replies to the three questionnaires were analyzed using descriptive statistical analysis.ResultsAlmost half of the patients completed the three questionnaires (33/70, 47%). The median age was 62 (IR 56–72). Only 27.8% were aware of male breast cancer prior to diagnosis. 86.1% (31/36) underwent regular breast imaging follow-up, with sexual activity declining during treatment. Endocrine treatment was reported by 63.9%. Discomfort was experienced, notably during interactions and communication about their condition. Erectile dysfunction was prevalent, with 18.2% reporting severe dysfunction. The average body image score was 9.1.ConclusionsOur findings emphasize the importance of comprehensive care that attends to the full spectrum of emotional and social well-being beyond medical requirements in male breast cancer patients.

A Novel Deep Learning Model for Breast Tumor Ultrasound Image Classification with Lesion Region Perception.

Multi-task learning (MTL) methods are widely applied in breast imaging for lesion area perception and classification to assist in breast cancer diagnosis and personalized treatment. A typical paradigm of MTL is the shared-backbone network architecture, which can lead to information sharing conflicts and result in the decline or even failure of the main task's performance. Therefore, extracting richer lesion features and alleviating information-sharing conflicts has become a significant challenge for breast cancer classification. This study proposes a novel Multi-Feature Fusion Multi-Task (MFFMT) model to effectively address this issue. Firstly, in order to better capture the local and global feature relationships of lesion areas, a Contextual Lesion Enhancement Perception (CLEP) module is designed, which integrates channel attention mechanisms with detailed spatial positional information to extract more comprehensive lesion feature information. Secondly, a novel Multi-Feature Fusion (MFF) module is presented. The MFF module effectively extracts differential features that distinguish between lesion-specific characteristics and the semantic features used for tumor classification, and enhances the common feature information of them as well. Experimental results on two public breast ultrasound imaging datasets validate the effectiveness of our proposed method. Additionally, a comprehensive study on the impact of various factors on the model's performance is conducted to gain a deeper understanding of the working mechanism of the proposed framework.

Tumor size measurements predicted by digital mammography, ultrasonography, and magnetic resonance imaging in primary invasive breast cancer disease

Background and objective: The size of the tumor significantly influences the prognosis and treatment approach for breast cancer patients. The aim of this study was to find the most accurate imaging method for estimating pre-treatment tumor size in women with newly diagnosed primary invasive breast cancer by comparing the predicted tumor size obtained from mammography, ultrasonography, and magnetic resonance imaging with the pathologic size obtained from the surgical specimens. Methods: This cross-sectional study included 181 primary invasive breast cancer patients from September 2021 to March 2023. The difference in tumor size was evaluated based on imaging and pathological reports. Variables such as age, breast density, and tumor characteristics like histologic type, grade, location, and side were recorded and analyzed. The American College of Radiology Breast Imaging Reporting and Data System was used for reporting. Data analysis, performed using SPSS Statistics software, included descriptive statistics, Spearman's correlation coefficient, and Lin's index. The statistical significance level was set at P &lt;0.05. Results: The mean tumor size was 29.68, 29.07, 28.37, and 27.7mm by mammography, ultrasonography, magnetic resonance imaging, and pathology, respectively. All diagnostic procedures revealed a statistically significant correlation with pathologic tumor size with the Spearman correlation test, P = 0.000. MRI had the highest Lin’s concordance correlation coefficient (0.93). Conclusion: The study determined that all imaging modalities were accurate in estimating tumor size when compared to the gold standard of pathological specimens and that magnetic resonance imaging outperformed digital mammography and ultrasonography.

Integrated and Interoperable Platform for Detecting Masses on Mammograms.

The screening and diagnosis of breast cancer is a major public health issue. Although deep learning models are proving highly effective in breast imaging, these models are not yet readily accessible to a wide audience. In order to promote the widespread dissemination of such models, this article introduces a free and open-source, integrated platform for the automated detection of masses on mammograms. A state-of-the-art RetinaNet model is trained on this task and the results of the inference are encoded using the DICOM-SR interoperable format. These contributions present a significant step towards overcoming the accessibility gap in deep learning for breast imaging.

Characterization of Commercial and Custom-Made Printing Filament Materials for Computed Tomography Imaging of Radiological Phantoms

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.

MRI features of breast cancer immunophenotypes with a focus on luminal estrogen receptor low positive invasive carcinomas

To compare the magnetic resonance imaging (MRI) features of different immunophenotypes of breast carcinoma of no special type (NST), with special attention to estrogen receptor (ER)-low-positive breast cancer. This retrospective, single-centre, Institutional Review Board (IRB)-approved study included 398 patients with invasive breast carcinoma. Breast carcinomas were classified as ER-low-positive when there was ER staining in 1–10% of tumour cells. Pretreatment MRI was reviewed to assess the tumour imaging features according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS) lexicon. Of the 398 cases, 50 (12.6%) were luminal A, 191 (48.0%) were luminal B, 26 (6.5%) were luminal ER-low positive, 64 (16.1%) were HER2-overexpressing, and 67 (16.8%) were triple negative. Correlation analysis between MRI features and tumour immunophenotype showed statistically significant differences in mass shape, margins, internal enhancement and the delayed phase of the kinetic curve. An oval or round shape and rim enhancement were most frequently observed in triple-negative and luminal ER-low-positive tumours. Spiculated margins were most common in luminal A and luminal B tumours. A persistent kinetic curve was more frequent in luminal A tumours, while a washout curve was more common in the triple-negative, HER2-overexpressing and luminal ER-low-positive immunophenotypes. Multinomial regression analysis showed that luminal ER-low-positive tumours had similar results to triple-negative tumours for almost all variables. Luminal ER-low-positive tumours present with similar MRI findings to triple-negative tumours, which suggests that MRI can play a fundamental role in adequate radiopathological correlation and therapeutic planning in these patients.

Survey on Current Utilization and Perception of Synthesized Mammography.

To assess utilization and perceptions of 2D synthesized mammography (SM) for digital breast tomosynthesis (DBT) among practicing U.S. breast radiologists. An IRB-exempt 23-question anonymized survey was developed by the Society of Breast Imaging (SBI) Patient Care and Delivery Committee and emailed to practicing U.S. radiologist SBI members on October 9, 2023. Questions assessed respondents' demographics, current mammographic screening protocol, confidence interpreting SM for mammographic findings, and perceived advantages and disadvantages of SM. Response rate was 13.4% (371/2771). Of 371 respondents, 208 were currently screening with DBT/SM (56.1%), 98 with DBT/SM/digital mammography (DM) (26.4%), 61 with DBT/DM (16.4%), and 4 with DM (1.1%). Most respondents felt confident using DBT/SM to evaluate masses (254/319, 79.6%), asymmetries (247/319, 77.4%), and distortions (265/318, 83.3%); however, confidence was mixed for calcifications (agreement 130/320, 40.6%; disagreement 156/320, 48.8%; neutral 34/320, 10.6%). The most frequently cited disadvantage and advantage of SM were reconstruction algorithm false-positive results (199/347, 57.4%) and lower radiation dose (281/346, 81.2%), respectively. Higher confidence and fewer disadvantages were reported by radiologists who had more SM experience, screened with DBT/SM, or exclusively used Hologic vendor (all P <.05). For most survey respondents (56.1%), SM has replaced DM in DBT screening. Radiologists currently screening with DBT/SM or with more SM experience reported greater confidence in SM with fewer perceived disadvantages.

A reliable breast cancer diagnosis approach using an optimized deep learning and conformal prediction

Accuracy of breast cancer detection using histopathological images is mainly subject to the expertise of physicians. For this reason, there are certain levels of uncertainty during the decision making of breast cancer diagnosis. To increase the certainty of physicians’ decisions, computer-aided medical systems can be used as additional knowledge for diagnosis from histopathological images. Early detection of breast cancer contributes greatly to the survival rate of the patients, which is essentially dependent on the analysis of breast images. In the literature review, few studies have been proposed for breast cancer classification based on histopathological images. However, the performance of current methods is still inefficient and they are incapable for extracting important features from the images due to ineffective hyper-parameters optimization. In this paper, a reliable deep learning approach for breast cancer diagnosis is proposed using a random search algorithm and DenseNet-121-based transfer-learning model. For the transfer learning technique, the pre-trained DenseNet-121 model is adopted due to its capability to strengthen the feature representation and reduce the number of trainable parameters. The use of random search optimization finds the best combinations of hyper-parameters values to improve the accuracy of the classification approach. Several experiments are conducted on a benchmark public dataset of histopathological images with different magnification factors: 40×, 100×, 200×, and 400×. The experiments showed that the proposed approach achieved 98.96 %, 97.62 %, 97.08 %, and 96.42 % of accuracy for 40×, 100×, 200×, and 400× test images, respectively, demonstrating its effectiveness compared to the state-of-the-art models and methods. The reliability of proposed approach is achieved by quantifying the uncertainty of model outcomes using conformal prediction method, guarantying user-chosen levels of confidence.

Patients' perceptions of targeted breast ultrasound and digital breast tomosynthesis in the diagnostic setting: A mixed methods study.

Although DBT is the standard initial imaging modality for women with focal breast symptoms, the importance of ultrasound has grown rapidly in the past decades. Therefore, the Breast UltraSound Trial (BUST) focused on assessing the diagnostic value of ultrasound and digital breast tomosynthesis (DBT) for the evaluation of breast symptoms by reversing the order of breast imaging; first performing ultrasound followed by DBT. This side-study of the BUST evaluates patients' perceptions of ultrasound and DBT in a reversed setting. After imaging, 1181/1276 BUST participants completed a survey consisting of open and closed questions regarding both exams (mean age 47.2, ±11.74). Additionally, a different subset of BUST participants (n = 29) participated in six focus group interviews 18-24 months after imaging to analyze their imaging experiences in depth. A total of 55.3% of women reported reluctance to undergoing DBT, primarily due of pain, while the vast majority also find bilateral DBT reassuring (87.3%). Thematic analysis identified themes related to 1) imaging reluctance (pain/burden, result, and breast harm) and 2) ultrasound and DBT perceptions. Regarding the latter, the theme comfort underscores DBT as burdensome and painful, while ultrasound is largely perceived as non-burdensome. Ultrasound is also particularly valued for its interactive nature, as highlighted in the theme interaction. Perceived effectiveness reflects women's interest in bilateral breast evaluation with DBT and the visibility of lesions, while they express more uncertainty about the reliability of ultrasound. Emotional impact portrays DBT as reassuring for many women, whereas opinions on the reassurance provided by ultrasound are more diverse. Additional themes include costs, protocols and privacy. Ultrasound is highly tolerated, and particularly valued is the interaction with the radiologist. Nearly half of women express reluctance towards DBT; nevertheless, a large portion report feeling more confident after undergoing bilateral DBT, reassuring them of the absence of abnormalities. Understanding patients' perceptions of breast imaging examinations is of great value when optimizing diagnostic pathways.