Diagnostic value of contrast-enhanced ultrasound and shear-wave elastography for small breast nodules.

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

Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study.

INTRODUCTION: Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs. MATERIAL AND METHODS: Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations. RESULTS: The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images. CONCLUSION: Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources. RELEVANCE STATEMENT: Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs. KEY POINTS: • We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms. • VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts. • Visual explanations using Grad-CAM++ highlighted VGG16's superior performance in localizing BAC.

Evaluation of cosmetic outcomes in breast reconstruction patients undergoing radiotherapy using an anomaly generative adversarial network model.

Considering the rising prevalence of breast reconstruction followed by radiotherapy (RT), evaluating the cosmetic impact of RT is crucial. Currently, there are limited tools for objectively assessing cosmetic outcomes in patients who have undergone reconstruction. Therefore, we validated the cosmetic outcome using a previously developed anomaly Generative Adversarial Network (GAN)-based model and evaluated its utility. Between January 2016 and December 2020, we collected computed tomography (CT) images from 82 breast cancer patients who underwent immediate reconstruction surgery followed by radiotherapy. Among these patients, 38 received immediate implant insertion, while 44 underwent autologous breast reconstruction. Anomaly scores (AS) were estimated using an anomaly GAN model at pre-RT, 1st follow-up, 1-year (Post-1Y) and 2-year (Post-2Y) after RT. Subsequently, the scores were analyzed in a time-series manner, considering reconstruction types (implant versus autologous), RT techniques, and the incidence of major complications. The median age of the patients was 46 years (range 29-62). The AS between Post-1Y and Post-2Y demonstrated a positive relationship (coefficient 0.515, P < 0.001). The AS was significantly associated with objective cosmetic indices, namely Breast Contour Difference (P = 0.009) and Breast Area Difference (P = 0.004), at both Post-1Y and Post-2Y. Subgroup analysis stratified by type of breast reconstruction revealed significantly higher AS values in patients who underwent prosthetic implant insertion compared to those with autologous reconstruction at all follow-up time points (1st follow-up, P = 0.001; Post-1Y, P < 0.001; and Post-2Y, P < 0.001). A threshold AS of ≥ 1.9 was associated with a 10% predicted risk of developing major complications. The feasibility of an AS generated by a GAN model for predicting both cosmetic outcomes and the likelihood of complications following RT has been successfully validated. Further investigation involving a larger patient cohort is warranted.

Effect of testosterone therapy on breast tissue composition and mammographic breast density in trans masculine individuals.

BACKGROUND: The effect of gender-affirming testosterone therapy (TT) on breast cancer risk is unclear. This study investigated the association between TT and breast tissue composition and breast tissue density in trans masculine individuals (TMIs). METHODS: Of the 444 TMIs who underwent chest-contouring surgeries between 2013 and 2019, breast tissue composition was assessed in 425 TMIs by the pathologists (categories of lobular atrophy and stromal composition) and using our automated deep-learning algorithm (% epithelium, % fibrous stroma, and % fat). Forty-two out of 444 TMIs had mammography prior to surgery and their breast tissue density was read by a radiologist. Mammography digital files, available for 25/42 TMIs, were analyzed using the LIBRA software to obtain percent density, absolute dense area, and absolute non-dense area. Linear regression was used to describe the associations between duration of TT use and breast tissue composition or breast tissue density measures, while adjusting for potential confounders. Analyses stratified by body mass index were also conducted. RESULTS: Longer duration of TT use was associated with increasing degrees of lobular atrophy (p < 0.001) but not fibrous content (p = 0.82). Every 6 months of TT was associated with decreasing amounts of epithelium (exp(ß) = 0.97, 95% CI 0.95,0.98, adj p = 0.005) and fibrous stroma (exp(ß) = 0.99, 95% CI 0.98,1.00, adj p = 0.05), but not fat (exp(ß) = 1.01, 95%CI 0.98,1.05, adj p = 0.39). The effect of TT on breast epithelium was attenuated in overweight/obese TMIs (exp(ß) = 0.98, 95% CI 0.95,1.01, adj p = 0.14). When comparing TT users versus non-users, TT users had 28% less epithelium (exp(ß) = 0.72, 95% CI 0.58,0.90, adj p = 0.003). There was no association between TT and radiologist's breast density assessment (p = 0.58) or LIBRA measurements (p > 0.05). CONCLUSIONS: TT decreases breast epithelium, but this effect is attenuated in overweight/obese TMIs. TT has the potential to affect the breast cancer risk of TMIs. Further studies are warranted to elucidate the effect of TT on breast density and breast cancer risk.

[Examination of breast density in breast cancer discordant twin pairs]. / Emlodenzitás vizsgálata emlorákdiszkordáns ikerpárok esetében.

Previous twin studies show that genetic factors are responsible for 63% of the variability in breast density. We analyzed the mammographic images of 9 discordant twin pairs for breast cancer from the population-based Hungarian Twin Registry. We measured breast density using 3D Slicer software. Genetic variants predisposing to breast cancer were also examined. One of the examined twin pairs had a BRCA2 mutation in both members. There was no significant difference between the mean values of breast density in the tumor and non-tumor groups (p=0.323). In terms of parity and the presence of menopause, we found mostly no significant difference between the members of the twin pair. In our cohort of identical twins discordant for breast cancer, the average breast density showed no significant difference, which can be explained by the common genetic basis of breast cancer and breast density.

Enlarged Breast Size (Macromastia) and Associated Neurologic Risks: A Scoping Review.

BACKGROUND AND OBJECTIVES: Symptomatic macromastia (enlarged breasts) is a syndrome of persistent headache, neck and shoulder pain, thoracic kyphosis, painful shoulder grooving from bra straps, inframammary rash, backache, and upper extremity paresthesias. Up to 89% of the 100,000 US women undergoing breast reduction surgery (reduction mammoplasty) annually report headache preoperatively with many endorsing postoperative headache improvement. Headache is one insurance indication to cover surgical reduction, and peak prevalence of migraine matches the average age of women with macromastia at time of surgery. Little is known about the influence of macromastia on headache. The goal of our narrative review is to understand the evidence for and potential mechanisms underlying macromastia-related headache. METHODS: A literature search was performed in PubMed Medline using concepts "breast hypertrophy," "macromastia," "headache," "migraine," "breast reduction," and "reduction mammoplasty" excluding limits on age, language, publication date, or study type. Supplemental literature searches were performed to provide a comprehensive understanding of potential mechanisms underlying macromastia-related headache. RESULTS: We identified 25 studies describing macromastia-associated headache in the setting of reduction mammoplasty, with 23 original research studies (retrospective, n = 12, prospective, n = 11) totaling 3,799 patients, 1 systematic review, and 1 meta-analysis. Most (24/25) were published in Plastic Surgery, one in Internal Medicine, and none in Neurology. Wide ranges were identified for preoperative headache prevalence (2%-89%) and postoperative headache improvement (34%-100%). Studies described headache as "myofascial" or "tension-type" without detailing headache definitions, chronicity, headache screening method, or neurologic examination. Potential pathophysiologic mechanisms of macromastia-associated headache include structural, mechanical, psychosocial, and hormonal. DISCUSSION: No studies on macromastia-associated headache and reduction mammoplasty are published in Neurology. This important women's health topic remains unexplored in fields outside Plastic Surgery. Many questions remain unanswered that are important for neurologists to understand, including which headache type(s) women with macromastia experience and which headache type(s) respond to surgical intervention.

Associations between quantitative measures of mammographic density and terminal ductal lobular unit involution in Chinese breast cancer patients.

BACKGROUND: Higher mammographic density (MD), a radiological measure of the proportion of fibroglandular tissue in the breast, and lower terminal duct lobular unit (TDLU) involution, a histological measure of the amount of epithelial tissue in the breast, are independent breast cancer risk factors. Previous studies among predominantly white women have associated reduced TDLU involution with higher MD. METHODS: In this cohort of 611 invasive breast cancer patients (ages 23-91 years [58.4% ≥ 50 years]) from China, where breast cancer incidence rates are lower and the prevalence of dense breasts is higher compared with Western countries, we examined the associations between TDLU involution assessed in tumor-adjacent normal breast tissue and quantitative MD assessed in the contralateral breast obtained from the VolparaDensity software. Associations were estimated using generalized linear models with MD measures as the outcome variables (log-transformed), TDLU measures as explanatory variables (categorized into quartiles or tertiles), and adjusted for age, body mass index, parity, age at menarche and breast cancer subtype. RESULTS: We found that, among all women, percent dense volume (PDV) was positively associated with TDLU count (highest tertile vs. zero: Expbeta = 1.28, 95% confidence interval [CI] 1.08-1.51, ptrend = < .0001), TDLU span (highest vs. lowest tertile: Expbeta = 1.23, 95% CI 1.11-1.37, ptrend = < .0001) and acini count/TDLU (highest vs. lowest tertile: Expbeta = 1.22, 95% CI 1.09-1.37, ptrend = 0.0005), while non-dense volume (NDV) was inversely associated with these measures. Similar trend was observed for absolute dense volume (ADV) after the adjustment of total breast volume, although the associations for ADV were in general weaker than those for PDV. The MD-TDLU associations were generally more pronounced among breast cancer patients ≥ 50 years and those with luminal A tumors compared with patients < 50 years and with luminal B tumors. CONCLUSIONS: Our findings based on quantitative MD and TDLU involution measures among Chinese breast cancer patients are largely consistent with those reported in Western populations and may provide additional insights into the complexity of the relationship, which varies by age, and possibly breast cancer subtype.

An investigation of tomosynthesis on the diagnostic efficacy of spot compression mammography.

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

Characterizing Sentinel Lymph Node Status in Breast Cancer Patients Using a Deep-Learning Model Compared With Radiologists' Analysis of Grayscale Ultrasound and Lymphosonography.

ABSTRACT: The objective of the study was to use a deep learning model to differentiate between benign and malignant sentinel lymph nodes (SLNs) in patients with breast cancer compared to radiologists' assessments.Seventy-nine women with breast cancer were enrolled and underwent lymphosonography and contrast-enhanced ultrasound (CEUS) examination after subcutaneous injection of ultrasound contrast agent around their tumor to identify SLNs. Google AutoML was used to develop image classification model. Grayscale and CEUS images acquired during the ultrasound examination were uploaded with a data distribution of 80% for training/20% for testing. The performance metric used was area under precision/recall curve (AuPRC). In addition, 3 radiologists assessed SLNs as normal or abnormal based on a clinical established classification. Two-hundred seventeen SLNs were divided in 2 for model development; model 1 included all SLNs and model 2 had an equal number of benign and malignant SLNs. Validation results model 1 AuPRC 0.84 (grayscale)/0.91 (CEUS) and model 2 AuPRC 0.91 (grayscale)/0.87 (CEUS). The comparison between artificial intelligence (AI) and readers' showed statistical significant differences between all models and ultrasound modes; model 1 grayscale AI versus readers, P = 0.047, and model 1 CEUS AI versus readers, P < 0.001. Model 2 r grayscale AI versus readers, P = 0.032, and model 2 CEUS AI versus readers, P = 0.041.The interreader agreement overall result showed κ values of 0.20 for grayscale and 0.17 for CEUS.In conclusion, AutoML showed improved diagnostic performance in balance volume datasets. Radiologist performance was not influenced by the dataset's distribution.

Breast volume in non-obese females is related to breast adipose cell hypertrophy, inflammation, and COX2 expression.

BACKGROUND: Breast hypertrophy seems to be a risk factor for breast cancer and the amount and characteristics of breast adipose tissue may play important roles. The main aim of this study was to investigate associations between breast volume in normal weight women and hypertrophic adipose tissue and inflammation. METHODS: Fifteen non-obese women undergoing breast reduction surgery were examined. Breast volume was measured with plastic cups and surgery was indicated if the breast was 800 ml or larger according to Swedish guidelines. We isolated adipose cells from the breasts and ambient subcutaneous tissue to measure cell size, cell inflammation and other known markers of risk of developing breast cancer including COX2 gene activation and MAPK, a cell proliferation regulator. RESULTS: Breast adipose cell size was characterized by cell hypertrophy and closely related to breast volume. The breast adipose cells were also characterized by being pro-inflammatory with increased IL-6, IL-8, IL-1ß, CCL-2, TNF-a and an increased marker of cell senescence GLB1/ß-galactosidase, commonly increased in hypertrophic adipose tissue. The prostaglandin synthetic marker COX2 was also increased in the hypertrophic cells and COX2 has previously been shown to be an important marker of risk of developing breast cancer. Interestingly, the phosphorylation of the proliferation marker MAPK was also increased in the hypertrophic adipose cells. CONCLUSION: Taken together, these findings show that increased breast volume in non-obese women is associated with adipose cell hypertrophy and dysfunction and characterized by increased inflammation and other markers of increased risk for developing breast cancer. TRIAL REGISTRATION: Projektdatabasen FoU i VGR, project number: 249191 (https://www.researchweb.org/is/vgr/project/249191).

AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human Performance.

Purpose To compare two deep learning-based commercially available artificial intelligence (AI) systems for mammography with digital breast tomosynthesis (DBT) and benchmark them against the performance of radiologists. Materials and Methods This retrospective study included consecutive asymptomatic patients who underwent mammography with DBT (2019-2020). Two AI systems (Transpara 1.7.0 and ProFound AI 3.0) were used to evaluate the DBT examinations. The systems were compared using receiver operating characteristic (ROC) analysis to calculate the area under the ROC curve (AUC) for detecting malignancy overall and within subgroups based on mammographic breast density. Breast Imaging Reporting and Data System results obtained from standard-of-care human double-reading were compared against AI results with use of the DeLong test. Results Of 419 female patients (median age, 60 years [IQR, 52-70 years]) included, 58 had histologically proven breast cancer. The AUC was 0.86 (95% CI: 0.85, 0.91), 0.93 (95% CI: 0.90, 0.95), and 0.98 (95% CI: 0.96, 0.99) for Transpara, ProFound AI, and human double-reading, respectively. For Transpara, a rule-out criterion of score 7 or lower yielded 100% (95% CI: 94.2, 100.0) sensitivity and 60.9% (95% CI: 55.7, 66.0) specificity. The rule-in criterion of higher than score 9 yielded 96.6% sensitivity (95% CI: 88.1, 99.6) and 78.1% specificity (95% CI: 73.8, 82.5). For ProFound AI, a rule-out criterion of lower than score 51 yielded 100% sensitivity (95% CI: 93.8, 100) and 67.0% specificity (95% CI: 62.2, 72.1). The rule-in criterion of higher than score 69 yielded 93.1% (95% CI: 83.3, 98.1) sensitivity and 82.0% (95% CI: 77.9, 86.1) specificity. Conclusion Both AI systems showed high performance in breast cancer detection but lower performance compared with human double-reading. Keywords: Mammography, Breast, Oncology, Artificial Intelligence, Deep Learning, Digital Breast Tomosynthesis © RSNA, 2024.

Detection of invasive ductal carcinoma in quadrant breast areas by electrical impedance tomography implemented with gaussian relaxation-time distribution (EIT-GRTD).

Invasive ductal carcinoma (IDC) in breast specimens has been detected in the quadrant breast area: (I) upper outer, (II) upper inner, (III) lower inner, and (IV) lower outer areas by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT-GRTD). The EIT-GRTD consists of two steps which are (1) the optimum frequencyfoptselection and (2) the time constant enhancement of breast imaging reconstruction.foptis characterized by a peak in the majority measurement pair of the relaxation-time distribution functionγ,which indicates the presence of IDC.γrepresents the inverse of conductivity and indicates the response of breast tissues to electrical currents across varying frequencies based on the Voigt circuit model. The EIT-GRTD is quantitatively evaluated by multi-physics simulations using a hemisphere container of mimic breast, consisting of IDC and adipose tissues as normal breast tissue under one condition with known IDC in quadrant breast area II. The simulation results show that EIT-GRTD is able to detect the IDC in four layers atfopt= 30, 170 Hz. EIT-GRTD is applied in the real breast by employed six mastectomy specimens from IDC patients. The placement of the mastectomy specimens in a hemisphere container is an important factor in the success of quadrant breast area reconstruction. In order to perform the evaluation, EIT-GRTD reconstruction images are compared to the CT scan images. The experimental results demonstrate that EIS-GRTD exhibits proficiency in the detection of the IDC in quadrant breast areas while compared qualitatively to CT scan images.

Evaluating spectral performance for quantitative contrast-enhanced breast CT with a GaAs based photon counting detector: a simulation approach.

Quantitative contrast-enhanced breast computed tomography (CT) has the potential to improve the diagnosis and management of breast cancer. Traditional CT methods using energy-integrated detectors and dual-exposure images with different incident spectra for material discrimination can increase patient radiation dose and be susceptible to motion artifacts and spectral resolution loss. Photon Counting Detectors (PCDs) offer a promising alternative approach, enabling acquisition of multiple energy levels in a single exposure and potentially better energy resolution. Gallium arsenide (GaAs) is particularly promising for breast PCD-CT due to its high quantum efficiency and reduction of fluorescence x-rays escaping the pixel within the breast imaging energy range. In this study, the spectral performance of a GaAs PCD for quantitative iodine contrast-enhanced breast CT was evaluated. A GaAs detector with a pixel size of 100µm, a thickness of 500µm was simulated. Simulations were performed using cylindrical phantoms of varying diameters (10 cm, 12 cm, and 16 cm) with different concentrations and locations of iodine inserts, using incident spectra of 50, 55, and 60 kVp with 2 mm of added aluminum filtration and and a mean glandular dose of 10 mGy. We accounted for the effects of beam hardening and energy detector response using TIGRE CT open-source software and the publicly available Photon Counting Toolkit (PcTK). Material-specific images of the breast phantom were produced using both projection and image-based material decomposition methods, and iodine component images were used to estimate iodine intake. Accuracy and precision of the proposed methods for estimating iodine concentration in breast CT images were assessed for different material decomposition methods, incident spectra, and breast phantom thicknesses. The results showed that both the beam hardening effect and imperfection in the detector response had a significant impact on performance in terms of Root Mean Squared Error (RMSE), precision, and accuracy of estimating iodine intake in the breast. Furthermore, the study demonstrated the effectiveness of both material decomposition methods in making accurate and precise iodine concentration predictions using a GaAs-based photon counting breast CT system, with better performance when applying the projection-based material decomposition approach. The study highlights the potential of GaAs-based photon counting breast CT systems as viable alternatives to traditional imaging methods in terms of material decomposition and iodine concentration estimation, and proposes phantoms and figures of merit to assess their performance.

Risk factors for residual fibroglandular breast tissue following a mastectomy - an overview and retrospective cohort study.

BACKGROUND: Residual fibroglandular breast tissue (RFGT) following a mastectomy is associated with the remaining of occult breast cancer at the time of mastectomy as well as an increased local recurrence risk thereafter. Despite its oncologic implications, data on measures to prevent RFGT are lacking. Therefore, in a first step knowledge of risk factors for RFGT is of uttermost importance in order to allow identification of patients at risk and subsequently adaption of the surgical treatment and potentially prevention of RFGT a priori. METHODS: We performed a systematic literature review in PubMed using the MESH terms [residual fibroglandular breast tissue], [residual breast tissue], [mastectomy] and [risk factor] followed by a retrospective data analysis including all patients with a mastectomy treated at the Department of Obstetrics and Gynecology of the Medical University of Vienna, Austria, between 01.01.2015 and 26.02.2020 in order to identify risk factors of RFGT following a mastectomy. The primary aim of the study was to assess a potential difference in RFGT volume between the different types of mastectomy. The secondary objectives of the study were to identify other potential risk factors for RFGT as well as to compare the skin and subcutaneous fat tissue thickness pre- to postoperatively. RESULTS: Significantly higher RFGT volumes were observed following a nipple-sparing mastectomy (NSM) compared to a skin-sparing mastectomy (SSM) and radical mastectomy (RME) (p < .001). Furthermore, RFGT volume was significantly associated with the variables: reconstruction (p = .012), acellular dermal matrix (ADM) or mesh (p = .031), patient age (p = .022), preoperative fibroglandular tissue (FGT) volume (p = .012) and preoperative whole breast volume (including the skin envelope and nipple-areola-complex) (p = .030). The reduction in the postoperative compared to preoperative skin envelope thickness measured medially and laterally reached statistical significance in the NSM-cohort (medial p < .001, lateral p = .001) and showed a numerical difference in the RME and SSM-cohort. CONCLUSION: Mastectomy type, reconstruction, ADM or mesh, patient age, preoperative FGT volume and whole breast volume were identified as risk factors for RFGT in univariable analysis. The observed reduction in the post- compared to preoperative skin envelope thickness should be avoided considering the known associated increase in risk for ischemic complications.

Intraindividual Comparison of Different Methods for Automated BPE Assessment at Breast MRI: A Call for Standardization.

Background The level of background parenchymal enhancement (BPE) at breast MRI provides predictive and prognostic information and can have diagnostic implications. However, there is a lack of standardization regarding BPE assessment. Purpose To investigate how well results of quantitative BPE assessment methods correlate among themselves and with assessments made by radiologists experienced in breast MRI. Materials and Methods In this pseudoprospective analysis of 5773 breast MRI examinations from 3207 patients (mean age, 60 years ± 10 [SD]), the level of BPE was prospectively categorized according to the Breast Imaging Reporting and Data System by radiologists experienced in breast MRI. For automated extraction of BPE, fibroglandular tissue (FGT) was segmented in an automated pipeline. Four different published methods for automated quantitative BPE extractions were used: two methods (A and B) based on enhancement intensity and two methods (C and D) based on the volume of enhanced FGT. The results from all methods were correlated, and agreement was investigated in comparison with the respective radiologist-based categorization. For surrogate validation of BPE assessment, how accurately the methods distinguished premenopausal women with (n = 50) versus without (n = 896) antihormonal treatment was determined. Results Intensity-based methods (A and B) exhibited a correlation with radiologist-based categorization of 0.56 ± 0.01 and 0.55 ± 0.01, respectively, and volume-based methods (C and D) had a correlation of 0.52 ± 0.01 and 0.50 ± 0.01 (P < .001). There were notable correlation differences (P < .001) between the BPE determined with the four methods. Among the four quantitation methods, method D offered the highest accuracy for distinguishing women with versus without antihormonal therapy (P = .01). Conclusion Results of different methods for quantitative BPE assessment agree only moderately among themselves or with visual categories reported by experienced radiologists; intensity-based methods correlate more closely with radiologists' ratings than volume-based methods. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Mann in this issue.

[Implementation and development of endoscopic benign breast lump resection and breast-conserving surgery for cancer].

Objective: To review the development of endoscopic techniques in breast surgery, focusing on their use in benign breast lump resection and breast-conserving surgery for cancer, and also summarize the development and application of these techniques in China, highlighting promotion and homogenization challenges and future directions. Methods: A systematic review of relevant literature was conducted to trace the historical evolution, clinical applications, and related research of endoscopic techniques in breast surgery, emphasizing their advantages and disadvantages of endoscopic benign breast lump resection and breast-conserving surgery for cancer. Results: Endoscopic benign breast lump resection and breast-conserving surgery for cancer have improved patients' postoperative psychological health and quality of life, particularly in scar-free surgery. However, challenges such as limited intraoperative visibility and prolonged surgery time lead to controversy in clinical practice. Conclusion: Despite significant advancements, endoscopic techniques in breast surgery also face challenges. Future efforts should focus on technological improvements and clinical research to address these issues, promoting widespread application and standardization. The key to future development lies in the promotion and homogenization of these technologies.

[Past and present of endoscopic surgery and robotic surgery in treatment of breast diseases].

Objective: To review the research progress related to endoscopic surgery and robotic surgery for breast diseases, aiming to provide references for clinical practice. Methods: The recent domestic and international literature on endoscopic surgery and robotic surgery for breast diseases was reviewed, then the challenges in their development, the innovative evolution of endoscopic surgery combined with clinical practice by our team, and its clinical applications were summarized. Results: Traditional endoscopic surgery, despite its advantages such as minimal invasiveness, good cosmetic outcomes, and high patient's satisfaction, has been limited in its development due to specific difficulties in establishing the operative field. Our team innovatively proposed the "reverse sequence method" and the Huaxi Hole 1 theory and methods, cleverly altering the surgical procedure sequence, adding small operative orifices to transform single-port operations into multi-port ones, effectively overcoming the challenges restricting the advancement of endoscopic surgery in the field of breast diseases, thereby enabling further proliferation of endoscopic procedures. In terms of breast endoscopic reconstruction surgery, the parachute patch technique has broadened the indications for reconstruction surgery, benefiting patients with a certain degree of breast ptosis; and the postoperative adjustment concept, through early intervention in the post-reconstruction breast shape, has further refined the reconstruction procedure. Robot-assisted surgery derived from endoscopic surgery theory has further enhanced the precision and stability of surgeries, reducing surgical risks; however, excessive time and economic costs are urgent issues that must be addressed. Conclusion: Through theoretical innovations, endoscopic surgery has been applied in the excision and reconstruction of breast lesions, while robotic surgery shows promising applications in autologous breast reconstruction, especially in the latissimus dorsi reconstruction field. Nevertheless, the lack of high-level large-sample, multi-center randomized controlled clinical trials to confirm its surgical safety, oncological safety, and postoperative cosmetic outcomes is an important direction for future research.

Clinical application and effect evaluation of acupoint thread embedding therapy and traditional Chinese medicine treatment based on menstrual cycle characteristics in the management of breast hyperplasia: An observational study.

To evaluate the effectiveness of the combination of acupoint embedding therapy and traditional Chinese medicine dialectical treatment regimen in improving clinical symptoms, promoting tumor regression, controlling adverse reactions and complications, and enhancing patient satisfaction by comparing and analyzing the clinical data of 120 breast tumor patients. One hundred twenty patients with breast cancer were divided into a treatment group (60 cases) and a control group (60 cases) according to different treatment plans. Patients in the treatment group received a combination of acupoint embedding therapy and traditional Chinese medicine dialectical treatment based on different time points of the menstrual cycle. Including the proportion of reduction in the number of breast masses, the proportion of reduction in mass size, changes in pain severity scores, tumor regression rate, regression time, incidence of adverse reactions and complications, and patient satisfaction. Statistical software was used to analyze the data to evaluate differences between the 2 groups. In terms of clinical symptoms, the proportion of reduction in the number of breast masses in the treatment group averaged 50%, significantly higher than the 25% in the control group; the proportion of reduction in mass size averaged 40%, also higher than the 15% in the control group; and the improvement in pain severity scores was also superior to the control group. Regarding tumor regression, the tumor regression rate in the treatment group reached 85%, with an average regression time of 6.2 weeks, both significantly better than the 55% and 9.8 weeks in the control group. In terms of adverse reactions and complications, the incidence rate in the treatment group was relatively low, and no serious adverse events occurred. Patient satisfaction surveys showed that the treatment group had significantly higher satisfaction with treatment effectiveness, treatment process, and physician service attitude compared to the control group. Based on clinical data from 120 breast tumor patients, the results of this study indicate that breast tumor patients treated with a specific treatment regimen have significant advantages in improving clinical symptoms, tumor regression, controlling adverse reactions and complications, and patient satisfaction. This treatment regimen has high clinical application value and deserves further promotion.