Breast care considerations for transgender and gender-diverse patients.

Transgender and gender-diverse (TGD) persons represent a small but growing population in the United States. Accessing inclusive, equitable, and evidence-based healthcare remains a challenge for this patient population. Many TGD persons seek gender-affirming care, including gender-affirming hormonal therapy (GAHT) and gender-affirming surgery (GAS), to help ameliorate the physical and mental aspects of their gender incongruence. Both GAHT and GAS induce clinically important histopathologic and anatomic changes in breast tissue. Consequently, breast care in TGD persons has become an increasingly recognized topic of importance in gender-affirming care. However, there remains a scarce but growing base of literature specifically addressing the unique healthcare needs of breast care in TGD patients. This article will review how to establish trusting patient-provider relationships for TGD patients, gender inclusivity in breast clinics and imaging centers, the influence of GAHT and GAS on breast tissue, breast cancer screening recommendations and barriers, and breast cancer risk and treatment considerations in TGD persons.

Differentiation Between Phyllodes Tumor and Fibroadenoma of the Breast: A Radiomics Prediction Model Based on Full-Field Digital Mammography & Digital Tomosynthesis.

OBJECTIVE: To assess the diagnostic performance of FFDM-based and DBT-based radiomics models to differentiate breast phyllodes tumors from fibroadenomas. METHODS: 192 patients (93 phyllodes tumors and 99 fibroadenomas) who underwent mammography were retrospectively enrolled. Radiomic features were respectively extracted from FFDM and the clearest slice of DBT images. A least absolute shrinkage and selection operator (LASSO) regression was used to select radiomics features. A combined model was constructed by radiomics and radiological signatures. Machine learning classification was done using logistic regression based on radiomics or radiological signatures (clinical model). Four radiologists were tested on phyllodes tumors and fibroadenomas with and without optimal model assistance. The area under the receiver operating characteristic (ROC) curve (AUC) was computed to assess the performance of each model or radiologist. The Delong test and McNemar's test were performed to compare the performance. RESULTS: The combined model yielded the highest performance with an AUC of 0.948 (95%CI: 0.889-1.000) in the testing set, slightly higher than the FFDM-radiomics model (AUC of 0.937, 95%CI: 0.841-0.984) and the DBT-radiomics model (AUC of 0.860, 95%CI: 0.742-0.936) and significantly superior to the clinical model (AUC of 0.719, 95%CI: 0.585-0.829). With the combined model aid, the AUCs of four radiologists were improved from 0.808 to 0.914 (p=0.079), 0.759 to 0.888 (p=0.015), 0.717 to 0.846 (p=0.004), and 0.629 to 0.803 (p=0.001). CONCLUSION: Radiomics analysis based on FFDM and DBT shows promise in differentiating phyllodes tumors from fibroadenomas.

Diagnostic Accuracy of Screening Contrast-enhanced Mammography for Women with Extremely Dense Breasts at Increased Risk of Breast Cancer.

Background Mammogram interpretation is challenging in female patients with extremely dense breasts (Breast Imaging Reporting and Data System [BI-RADS] category D), who have a higher breast cancer risk. Contrast-enhanced mammography (CEM) has recently emerged as a potential alternative; however, data regarding CEM utility in this subpopulation are limited. Purpose To evaluate the diagnostic performance of CEM for breast cancer screening in female patients with extremely dense breasts. Materials and Methods This retrospective single-institution study included consecutive CEM examinations in asymptomatic female patients with extremely dense breasts performed from December 2012 to March 2022. From CEM examinations, low-energy (LE) images were the equivalent of a two-dimensional full-field digital mammogram. Recombined images highlighting areas of contrast enhancement were constructed using a postprocessing algorithm. The sensitivity and specificity of LE images and CEM images (ie, including both LE and recombined images) were calculated and compared using the McNemar test. Results This study included 1299 screening CEM examinations (609 female patients; mean age, 50 years ± 9 [SD]). Sixteen screen-detected cancers were diagnosed, and two interval cancers occured. Five cancers were depicted at LE imaging and an additional 11 cancers were depicted at CEM (incremental cancer detection rate, 8.7 cancers per 1000 examinations). CEM sensitivity was 88.9% (16 of 18; 95% CI: 65.3, 98.6), which was higher than the LE examination sensitivity of 27.8% (five of 18; 95% CI: 9.7, 53.5) (P = .003). However, there was decreased CEM specificity (88.9%; 1108 of 1246; 95% CI: 87.0, 90.6) compared with LE imaging (specificity, 96.2%; 1199 of 1246; 95% CI: 95.0, 97.2) (P < .001). Compared with specificity at baseline, CEM specificity at follow-up improved to 90.7% (705 of 777; 95% CI: 88.5, 92.7; P = .01). Conclusion Compared with LE imaging, CEM showed higher sensitivity but lower specificity in female patients with extremely dense breasts, although specificity improved at follow-up. © RSNA, 2024 See also the editorial by Lobbes in this issue.

A comprehensive numerical procedure for high-intensity focused ultrasound ablation of breast tumour on an anatomically realistic breast phantom.

High-Intensity Focused Ultrasound (HIFU) as a promising and impactful modality for breast tumor ablation, entails the precise focalization of high-intensity ultrasonic waves onto the tumor site, culminating in the generation of extreme heat, thus ablation of malignant tissues. In this paper, a comprehensive three-dimensional (3D) Finite Element Method (FEM)-based numerical procedure is introduced, which provides exceptional capacity for simulating the intricate multiphysics phenomena associated with HIFU. Furthermore, the application of numerical procedures to an anatomically realistic breast phantom (ARBP) has not been explored before. The integrity of the present numerical procedure has been established through rigorous validation, incorporating comparative assessments with previous two-dimensional (2D) simulations and empirical data. For ARBP ablation, the administration of a 0.1 MPa pressure input pulse at a frequency of 1.5 MHz, sustained at the focal point for 10 seconds, manifests an ensuing temperature elevation to 80°C. It is noteworthy that, in contrast, the prior 2D simulation using a 2D phantom geometry reached just 72°C temperature under the identical treatment regimen, underscoring the insufficiency of 2D models, ascribed to their inherent limitations in spatially representing acoustic energy, which compromises their overall effectiveness. To underscore the versatility of this numerical platform, a simulation of a more clinically relevant HIFU therapy procedure has been conducted. This scenario involves the repositioning of the ultrasound focal point to three separate lesions, each spaced at 3 mm intervals, with ultrasound exposure durations of 6 seconds each and a 5-second interval for movement between focal points. This approach resulted in a more uniform high-temperature distribution at different areas of the tumour, leading to the ablation of almost all parts of the tumour, including its verges. In the end, the effects of different abnormal tissue shapes are investigated briefly as well. For solid mass tumors, 67.67% was successfully ablated with one lesion, while rim-enhancing tumors showed only 34.48% ablation and non-mass enhancement tumors exhibited 20.32% ablation, underscoring the need for multiple lesions and tailored treatment plans for more complex cases.

Role of inter- and extra-lesion tissue, transfer learning, and fine-tuning in the robust classification of breast lesions.

Accurate and unbiased classification of breast lesions is pivotal for early diagnosis and treatment, and a deep learning approach can effectively represent and utilize the digital content of images for more precise medical image analysis. Breast ultrasound imaging is useful for detecting and distinguishing benign masses from malignant masses. Based on the different ways in which benign and malignant tumors affect neighboring tissues, i.e., the pattern of growth and border irregularities, the penetration degree of the adjacent tissue, and tissue-level changes, we investigated the relationship between breast cancer imaging features and the roles of inter- and extra-lesional tissues and their impact on refining the performance of deep learning classification. The novelty of the proposed approach lies in considering the features extracted from the tissue inside the tumor (by performing an erosion operation) and from the lesion and surrounding tissue (by performing a dilation operation) for classification. This study uses these new features and three pre-trained deep neuronal networks to address the challenge of breast lesion classification in ultrasound images. To improve the classification accuracy and interpretability of the model, the proposed model leverages transfer learning to accelerate the training process. Three modern pre-trained CNN architectures (MobileNetV2, VGG16, and EfficientNetB7) are used for transfer learning and fine-tuning for optimization. There are concerns related to the neuronal networks producing erroneous outputs in the presence of noisy images, variations in input data, or adversarial attacks; thus, the proposed system uses the BUS-BRA database (two classes/benign and malignant) for training and testing and the unseen BUSI database (two classes/benign and malignant) for testing. Extensive experiments have recorded accuracy and AUC as performance parameters. The results indicate that the proposed system outperforms the existing breast cancer detection algorithms reported in the literature. AUC values of 1.00 are calculated for VGG16 and EfficientNet-B7 in the dilation cases. The proposed approach will facilitate this challenging and time-consuming classification task.

Reduction Mammaplasty: Experience in Context of Bangladesh.

There are many women in Bangladesh who suffer from chronic shoulder pain, back and neck pain, nerve pain, and other difficulties due to large breasts but most of them are not keen to get rid of their problems. Most women in this country are afraid from surgery and they are not even aware about their body shape and images. Recently, very few of them are coming to the door of surgeons with enlarged breast difficulties. A study was conducted on reduction mammaplasty regarding experience in the context of our country. Few of the patients were motivated among many who have problems of the huge breast and perform reduction mammaplasty. The paper addresses the experience of reduction mammaplasty. The study among 8 patients was performed in, Anower Khan Modern Medical College and Hospital and Care Medical College and Hospital during the period of Octy 2018 to January 2021. The patients underwent reduction mammaplasty over a 2.5 years period were identified and reviewed for patients satisfaction rate, religious issues, shyness, Family restrictions, socio economic condition, lack of awareness, risk factor, symptom relief, limitation and complication rate. Rate of complications was from 6.5% to 22% for reduction mammaplasty, whereas reported patient satisfaction rates range from 85.0% to 95.0%. In the study, reported rates of symptom improvement range from 80.8% to 94.6%, religious issues about 90.0% to 95.0%, Shyness 80.0% to 87.5%, family restrictions 80% to 87.5%, socio economic condition (High Class n=5, Upper Middle Class n=3), risk factor 70.0% to 80.0%, but in regard to psychological well-being there are tremendous outcomes. Reduction mammaplasty has had excellent patient satisfaction levels. However, a very few complications may occur even in the most suitable candidate. Skilled and experienced surgeons, enriched healthcare infrastructures, meticulous pre-operative planning, gentle tissue handling and anticipatory post-operative care will reduce the incidence of adverse results.

Retrospective analysis of core-needle and vacuum-assisted breast biopsies of B3 fibroepithelial lesions and correlation with results in subsequent surgical specimens.

BACKGROUND: Fibroepithelial lesions (FEL) are a heterogeneous group of biphasic tumours that include fibroadenomas (FA) and the rare entity of benign phyllodes tumors (PT) as well as cases where distinction between these two entities is not possible. The histologic distinction between benign PT and cellular FA is still a diagnostic challenge, especially in core-needle biopsy (CNB) or vacuum-assisted biopsy (VAB). Guidelines are not clearly established regarding the management of FEL in CNB or VAB. In this study, we addressed the frequency of B3 FEL diagnosed in CNB or VAB and compared the final histopathological findings in the excision specimens to evaluate up- or downgrading. METHODS: We identified 117 female patients with the preoperative diagnosis of FEL (B3), PT, or FEL in combination of pure epithelial B3 lesions in CNB or VAB. Clinico-pathological information as well as data on subsequent surgical excision were available for all patients. RESULTS: PT was diagnosed in 9 (14.8%) and FEL (B3) in 52 (85.2%) cases. Additionally, 56 patients with FA in combination with an additional B3 lesion were identified. Most FEL (B3)/PT initial diagnoses were made in CNB (55.6% of PT; 84.6% of FEL). After the initial biopsy, 7 of 9 (77.8%) patients with initial diagnosis of benign or borderline PT in CNB/VAB and 40 of 52 (77.0%) patients with initial diagnosis of FEL (B3) in CNB/VAB underwent open excision (OE). 4 of 9 cases (44.4%) initially diagnosed as PT were verified, whereas 2 of 9 (22.2%) were downgraded to FA. 20 of 52 cases (38.5%) initially diagnosed as FEL (B3) were downgraded to FA, whereas 11 of 52 cases (21.2%) were diagnosed as benign or borderline PT. One FEL (B3) case was upgraded to malignant PT. CONCLUSION: Most PT and FEL (B3) diagnoses on CNB/VAB underwent surgical removal. In the final pathological findings of cases classified primarily as FEL (B3), the majority were downgraded to FA, one quarter were upgraded to PT, and a small subset remained as combined FA/PT. In clinical daily practice, we recommend individualized decision-making considering different options (clinical follow-up or removal of the lesion depending on the whole context) in a multidisciplinary preoperative conference.

Macrophage uptake rate of Sonazoid in breast lymphosonography is highly conserved in healthy controls.

Subcutaneous microbubble administration in connection with contrast enhanced ultrasound (CEUS) imaging is showing promise as a noninvasive and sensitive way to detect tumor draining sentinel lymph nodes (SLNs) in patients with breast cancer. Moreover, there is potential to harness the results from these approaches to directly estimate cancer burden, since some microbubble formulas, such as the Sonazoid used in this study, are rapidly phagocytosed by macrophages, and the macrophage concentration in a lymph node is inversely related to the cancer burden. This work presents a mathematical model that can approximate a rate constant governing macrophage uptake of Sonazoid,ki, given dynamic CEUS Sonazoid imaging data. Twelve healthy women were injected with 1.0 ml of Sonazoid in an upper-outer quadrant of one of their breasts and SLNs were imaged in each patient immediately after injection, and then at 0.25, 0.5, 1, 2, 4, 6, and 24 h after injection. The mathematical model developed was fit to the dynamic CEUS data from each subject resulting in a mean ± sd of 0.006 ± 0.005 h-1and 0.4 ± 0.1 h-1for relative lymphatic flow (EFl) andki, respectively. Furthermore, the roughly 25% sd of thekimeasurement was similar to the sd that would be expected from realistic noise simulations for a stable 0.4 h-1value ofki, suggesting that macrophage concentration is highly consistent among cancer-free SLNs. These results, along with the significantly smaller variance inkimeasurement observed compared to relative lymphatic flow suggest thatkimay be a more precise and promising approach of estimating macrophage abundance, and inversely cancer burden. Future studies comparing tumor-free to tumor-bearing nodes are planned to verify this hypothesis.

Assessment of parenteral estradiol and dihydroxyprogesterone use among other feminizing regimens for transgender women: insights on satisfaction with breast development from community-based healthcare services.

The practice of hormone therapy is crucial in aligning secondary sex characteristics with the gender identity of transgender adults. This study examines the effects of a commonly used injectable hormone combination, specifically estradiol enanthate with dihydroxyprogesterone acetophenide (EEn/DHPA), on serum hormonal levels and self-reported satisfaction with breast development in transwomen. Our research focused on a retrospective longitudinal study involving a large cohort of transwomen evaluated between 2020 and 2022, comprising 101 participants. We assessed serum levels of estradiol (E2), testosterone (T), luteinizing hormone (LH), and follicle-stimulating hormone (FSH), comparing the EEn/DHPA hormonal regimen with other combined estrogen-progestogen (CEP) therapies. Additionally, a subset of 43 transwomen completed a 5-question survey to evaluate self-reported satisfaction with breast development using Tanner scales. Our findings indicated that participants using the EEn/DHPA regimen exhibited significantly higher serum E2 levels (mean: 186 pg/mL ± 32 pg/mL) than those using other therapies (62 ± 7 pg/mL), along with lower FSH levels, but no significant differences in T and LH levels. Concerning satisfaction with breast development, 76% reported increased fulfillment with breast augmentation while using EEn/DHPA. These results suggest that an injectable, low-cost EEn/DHPA administered every three weeks could serve as an alternative feminizing regimen, particularly considering the extensive long-term experience of the local transgender community. Further longitudinal studies on the efficacy of feminizing-body effects and endovascular risks of various parenteral CEP types are warranted to improve primary healthcare provision for transgender persons.

Detection of breast cancer in digital breast tomosynthesis with vision transformers.

Digital Breast Tomosynthesis (DBT) has revolutionized more traditional breast imaging through its three-dimensional (3D) visualization capability that significantly enhances lesion discernibility, reduces tissue overlap, and improves diagnostic precision as compared to conventional two-dimensional (2D) mammography. In this study, we propose an advanced Computer-Aided Detection (CAD) system that harnesses the power of vision transformers to augment DBT's diagnostic efficiency. This scheme uses a neural network to glean attributes from the 2D slices of DBT followed by post-processing that considers features from neighboring slices to categorize the entire 3D scan. By leveraging a transfer learning technique, we trained and validated our CAD framework on a unique dataset consisting of 3,831 DBT scans and subsequently tested it on 685 scans. Of the architectures tested, the Swin Transformer outperformed the ResNet101 and vanilla Vision Transformer. It achieved an impressive AUC score of 0.934 ± 0.026 at a resolution of 384 × 384. Increasing the image resolution from 224 to 384 not only maintained vital image attributes but also led to a marked improvement in performance (p-value = 0.0003). The Mean Teacher algorithm, a semi-supervised method using both labeled and unlabeled DBT slices, showed no significant improvement over the supervised approach. Comprehensive analyses across different lesion types, sizes, and patient ages revealed consistent performance. The integration of attention mechanisms yielded a visual narrative of the model's decision-making process that highlighted the prioritized regions during assessments. These findings should significantly propel the methodologies employed in DBT image analysis by setting a new benchmark for breast cancer diagnostic precision.

Implications of breast density for breast cancer screening.

ABSTRACT: Extremely dense breasts can be an independent risk factor for breast cancer. A new FDA rule requires that patients be notified of their breast density and the possible benefits of additional imaging to screen for breast cancer. Clinicians should be cognizant of the data about breast cancer risk, breast density, and recommendations to change screening techniques if patients, particularly premenopausal females, have extremely dense breasts but no other known risk factors.

Deep learning assessment of senescence-associated nuclear morphologies in mammary tissue from healthy female donors to predict future risk of breast cancer: a retrospective cohort study.

BACKGROUND: Cellular senescence has been associated with cancer as either a barrier mechanism restricting autonomous cell proliferation or a tumour-promoting microenvironmental mechanism that secretes proinflammatory paracrine factors. With most work done in non-human models and the heterogeneous nature of senescence, the precise role of senescent cells in the development of cancer in humans is not well understood. Furthermore, more than 1 million non-malignant breast biopsies are taken every year that could be a major resource for risk stratification. We aimed to explore the clinical relevance for breast cancer development of markers of senescence in mammary tissue from healthy female donors. METHODS: In this retrospective cohort study, we applied single-cell deep learning senescence predictors, based on nuclear morphology, to histological images of haematoxylin and eosin-stained breast biopsy samples from healthy female donors at the Komen Tissue Bank (KTB) at the Indiana University Simon Cancer Center (Indianapolis, IN, USA). All KTB participants (aged ≥18 years) who underwent core biopsies for research purposes between 2009 and 2019 were eligible for the study. Senescence was predicted in the epithelial (terminal duct lobular units [TDLUs] and non-TDLU epithelium), stromal, and adipose tissue compartments using validated models, previously trained on cells induced to senescence by ionising radiation (IR), replicative exhaustion (or replicative senescence; RS), or antimycin A, atazanavir-ritonavir, and doxorubicin (AAD) exposures. To benchmark our senescence-based cancer prediction results, we generated 5-year Gail scores-the current clinical gold standard for breast cancer risk prediction-for participants aged 35 years and older on the basis of characteristics at the time of tissue donation. The primary outcome was estimated odds of breast cancer via logistic modelling for each tissue compartment based on predicted senescence scores in cases (participants who had been diagnosed with breast cancer as of data cutoff, July 31, 2022) and controls (those who had not been diagnosed with breast cancer). FINDINGS: 4382 female donors (median age at donation 45 years [IQR 34-57]) were eligible for the study. As of data cutoff (median follow-up of 10 years [7-11]), 86 (2·0%) had developed breast cancer a mean of 4·8 years (SD 2·84) after date of donation and 4296 (98·0%) had not received a breast cancer diagnosis. Among the 86 cases, we found significant differences in adipose-specific IR and AAD senescence prediction scores compared with controls. Risk analysis showed that individuals in the upper half (above the median) of scores for the adipose tissue IR model had higher odds of developing breast cancer (odds ratio [OR] 1·71 [95% CI 1·10-2·68]; p=0·019), whereas the adipose AAD model revealed a reduced odds of developing breast cancer (OR 0·57 [0·36-0·88]; p=0·013). For the other tissue compartments and the RS model, no significant associations were found (except for stromal tissue via the IR model, had higher odds of developing breast cancer [OR 1·59, 1·03-2·49]). Individuals with both of the adipose risk factors had an OR of 3·32 (1·68-7·03; p=0·0009). Participants with 5-year Gail scores above the median had an OR for development of cancer of 2·33 (1·46-3·82; p=0·0012) compared with those with scores below the median. When combining Gail scores with our adipose AAD risk model, we found that individuals with both of these predictors had an OR of 4·70 (2·29-10·90; p<0·0001). When combining the Gail score with our adipose IR model, we found that individuals with both predictors had an OR of 3·45 (1·77-7·24; p=0·0002). INTERPRETATION: Assessment of senescence-associated nuclear morphologies with deep learning allows prediction of future cancer risk from normal breast biopsy samples. The combination of multiple models improved prediction of future breast cancer compared with the current clinical benchmark, the Gail model. Our results suggest an important role for microscope image-based deep learning models in predicting future cancer development. Such models could be incorporated into current breast cancer risk assessment and screening protocols. FUNDING: Novo Nordisk Foundation, Danish Cancer Society, and the US National Institutes of Health.

Penetrating cardiac injury after percutaneous breast core-needle biopsy, unusual life-threatening complication: a case report.

BACKGROUND: Complications after percutaneous breast biopsy are infrequent but may include hematoma, pseudoaneurysm formation, persistent pain, infection, delayed wound healing, vasovagal reaction, hemothorax, pneumothorax, and neoplastic seeding. The risk factors include tumor factors (size, location, vascularity), procedure-related factors (needle diameter, number of biopsies), and interventionist experience. There has been no previous report of a fatal complication resulting from percutaneous breast biopsy. CASE PRESENTATION: We report a 54-year-old Asian woman with a 3 cm BI-RADS® 4B left breast mass in the lower-inner quadrant who was biopsied by a 16 G needle under ultrasound guidance at a province hospital. She experienced dizziness and near-syncope afterward. The initial evaluation showed evidence of cardiac tamponade with hemodynamic instability. She underwent urgent subxiphoid pericardial window and was transferred to our facility. We brought her directly to the operating room to perform an explorative median sternotomy and found a 0.2 cm hole in the right ventricle. The injured site was successfully repaired without cardiopulmonary bypass. Postoperative echocardiography demonstrated mild right ventricular dysfunction without evidence of septal or valvular injury. She survived with no significant complications. DISCUSSION: This case might be the first report of a life-threatening complication related to percutaneous breast core-needle biopsy. The rapid pericardial release is key to the survival of cardiac tamponade. The patient subsequently required cardiac repair and monitoring to avoid long-term complications. In this report, we suggested a safe biopsy method, complications recognition, and appropriate management of penetrating cardiac injury. CONCLUSION: Penetrating cardiac injury resulting from percutaneous breast biopsy is extremely rare but can occur. A biopsy must be done cautiously, and worst-case management should promptly be considered.

MR Imaging-Guided Focused Ultrasound for Breast Tumors.

Breast tumors remain a complex and prevalent health burden impacting millions of individuals worldwide. Challenges in treatment arise from the invasive nature of traditional surgery and, in malignancies, the complexity of treating metastatic disease. The development of noninvasive treatment alternatives is critical for improving patient outcomes and quality of life. This review aims to explore the advancements and applications of focused ultrasound (FUS) technology over the past 2 decades. FUS offers a promising noninvasive, nonionizing intervention strategy in breast tumors including primary breast cancer, fibroadenomas, and metastatic breast cancer.

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.

Comparison of the Ultrasonography Features of the Breast in Women with Fibroadenoma and Those with Other Breast Lumps.

BACKGROUND: Fibroadenoma (FA) is documented as the most common benign breast disease typically presenting as a lump. A wide variety of other diseases including breast cancer can similarly present as lumps hence the need for further differentiation. Ultrasonography plays a vital role in the evaluation and treatment of breast lumps with histological analysis as the gold standard. OBJECTIVE: This study compared the physical and sonographic features of the breast in women with FA and women with breast lumps due to other diseases. MATERIALS AND METHODS: This is a single-centre comparative study. Clinical and sonographic breast evaluations of the recruited patients with lumps were done and reported using the American College of Radiology Breast Imaging Reporting and Data System score. The lumps were biopsied, and histological diagnosis was documented. Clinical and imaging features of the breasts of women with FA were then compared with those of women with lumps from other breast diseases, and collated data were analysed using SPSS Statistical version 23.0. RESULTS: Data from 118 subjects (59 in each group) were used for this study. There was a significant difference in the physical and sonographic appearance of FA concerning the patient's age, parity, change in lesion size, perilesional architecture, echogenicity, borders, capsule and background breast density. No FA was found in women with less dense breasts. CONCLUSION: The sonographic features of breasts showed some differences from the corresponding features of FA and other breast lesions. This has the potential to increase the efficiency of pre-operative diagnosis of FA and could be further applied in developing diagnostic criteria for FA in our environment.

Breast Cancers Detected during a Decade of Screening with Digital Breast Tomosynthesis: Comparison with Digital Mammography.

Background Digital breast tomosynthesis (DBT) has been shown to help increase cancer detection compared with two-dimensional digital mammography (DM). However, it is unclear whether additional tumor detection will improve outcomes or lead to overdiagnosis of breast cancer. Purpose This study aimed to compare cancer types and stages over 3 years of DM screening and 10 years of DBT screening to determine the effect of DBT. Materials and Methods A retrospective search identified breast cancers detected by using screening mammography from August 2008 through July 2021. Data collected included demographic, imaging, and pathologic information. Invasive cancers 2 cm or larger, human epidermal growth factor 2-positive or triple-negative tumors greater than 10 mm, axillary nodes positive for cancer, and distant organ spread were considered advanced cancers. The DBT and DM cohorts were compared and further analyzed by prevalent versus incident examinations. False-negative findings were also assessed. Results A total of 1407 breast cancers were analyzed (142 with DM, 1265 with DBT). DBT showed a higher rate of cancer depiction than DM (5.3 vs four cancers per 1000, respectively; P = .001), with a similar ratio of invasive cancers to ductal carcinomas in situ (76.5%:23.5% [968 and 297 of 1265, respectively] vs 71.1%:28.9% [101 and 41 of 142, respectively]). Mean invasive cancer size did not differ between DM and DBT (1.44 cm ± 0.93 [SD] vs 1.36 cm ± 1.14, respectively; P = .49), but incident DBT cases were smaller than prevalent cases (1.2 cm ± 1.0 vs 1.6 cm ± 1.4, respectively; P < .001). DBT and DM had similar rates of invasive cancer subtypes: low grade (26.5% [243 of 912] vs 29% [28 of 96], respectively), moderate grade (57.2% [522 of 912] vs 51% [49 of 96], respectively), and high grade (16.1% [147 of 912] vs 20% [19 of 96], respectively) (P = .65). The proportion of advanced cancers was lower with DBT than DM (32.6% [316 of 968] vs 43.6% [44 of 101], respectively; P = .04) and between DBT prevalent and incident screening (39.1% [133 of 340] vs 29.1% [183 of 628], respectively; P = .003). There was no difference in interval cancer rates (0.14 per 1000 with DM and 0.2 per 1000 with DBT; P = .42) for both groups. Conclusion DBT helped to increase breast cancer detection rate and depicted invasive cancers with a lower rate of advanced cancers compared with DM, with further improvement observed at incident rounds of screening. © RSNA, 2024 See also the editorial by Kim and Woo in this issue.

Detection of invasive ductal carcinoma by electrical impedance spectroscopy implementing gaussian relaxation-time distribution (EIS-GRTD).

Breast cancer detection and differentiation of breast tissues are critical for accurate diagnosis and treatment planning. This study addresses the challenge of distinguishing between invasive ductal carcinoma (IDC), normal glandular breast tissues (nGBT), and adipose tissue using electrical impedance spectroscopy combined with Gaussian relaxation-time distribution (EIS-GRTD). The primary objective is to investigate the relaxation-time characteristics of these tissues and their potential to differentiate between normal and abnormal breast tissues. We applied a single-point EIS-GRTD measurement to ten mastectomy specimens across a frequency rangef= 4 Hz to 5 MHz. The method calculates the differential ratio of the relaxation-time distribution functionΔγbetween IDC and nGBT, which is denoted byΔγIDC-nGBT,andΔγbetween IDC and adipose tissues, which is denoted byΔγIDC-adipose.As a result, the differential ratio ofΔγbetween IDC and nGBTΔγIDC-nGBTis 0.36, and between IDC and adiposeΔγIDC-adiposeis 0.27, which included in theα-dispersion atτpeak1=0.033±0.001s.In all specimens, the relaxation-time distribution functionγof IDCγIDCis higher, and there is no intersection withγof nGBTγnGBTand adiposeγadipose.The difference inγsuggests potential variations in relaxation properties at the molecular or structural level within each breast tissue that contribute to the overall relaxation response. The average mean percentage errorδfor IDC, nGBT, and adipose tissues are 5.90%, 6.33%, and 8.07%, respectively, demonstrating the model's accuracy and reliability. This study provides novel insights into the use of relaxation-time characteristic for differentiating breast tissue types, offering potential advancements in diagnosis methods. Future research will focus on correlating EIS-GRTD finding with pathological results from the same test sites to further validate the method's efficacy.