Benign Breast Disease Research Articles

Personalized Breast Cancer Screening: A Risk Prediction Model Based on Women Attending BreastScreen Norway.

We aimed to develop and validate a model predicting breast cancer risk for women targeted by breast cancer screening. This retrospective cohort study included 57,411 women screened at least once in BreastScreen Norway during the period from 2007 to 2019. The prediction model included information about age, mammographic density, family history of breast cancer, body mass index, age at menarche, alcohol consumption, exercise, pregnancy, hormone replacement therapy, and benign breast disease. We calculated a 4-year absolute breast cancer risk estimates for women and in risk groups by quartiles. The Bootstrap resampling method was used for internal validation of the model (E/O ratio). The area under the curve (AUC) was estimated with a 95% confidence interval (CI). The 4-year predicted risk of breast cancer ranged from 0.22-7.33%, while 95% of the population had a risk of 0.55-2.31%. The thresholds for the quartiles of the risk groups, with 25% of the population in each group, were 0.82%, 1.10%, and 1.47%. Overall, the model slightly overestimated the risk with an E/O ratio of 1.10 (95% CI: 1.09-1.11) and the AUC was 62.6% (95% CI: 60.5-65.0%). This 4-year risk prediction model showed differences in the risk of breast cancer, supporting personalized screening for breast cancer in women aged 50-69 years.

Rotational gland dissection for refractory granulomatous mastitis: A single-center retrospective study

BackgroundRefractory granulomatous mastitis (RGM) is a chronic benign breast disease that commonly occurred in women of childbearing age and is usually treated with surgery, with numerous cases suffering from unsatisfied postoperative recovery of breast shape, high rates of surgical complications, and even high recurrence. This study tries to evaluate the efficacy of an innovative surgical procedure, the rotational gland dissection for the treatment of RGM. Methods129 patients with RGM who underwent surgical treatment at the Second Affiliated Hospital of Xi'an Jiaotong University between Apr. 2017 and May. 2021 were retrospectively included in this study. The article analyzed the age, local symptoms, lesion location, and size, days in hospital, recurrence rate, and satisfaction rate of the patients. ResultsPatients ranged in age from 19 to 58 years, with a median age of onset of 32 years. In 63 patients (48.84%), their lesions coverage exceeded two quadrants, and 52.71% of patients had lesions larger than 10 cm2. The average days in hospital of patients was 7.5 days, and 85.27% of them were satisfied with their post-surgery breast appearance. Within the median follow-up of 56 months, only 3.10% of patients experienced a recurrence of mastitis on the operation side. ConclusionThis novel surgical procedure we created is an effective treatment for RGM with a high success rate, high patient satisfaction, and low recurrence rate, and is significantly superior to other studies for it has the largest sample size and longest follow-up in this field.

Diagnostic value of shear wave elastography combined with super microvascular imaging for BI-RADS 3-5 nodules.

To investigate the diagnostic value of shear wave elastography (SWE) and super microvascular imaging (SMI) integrated with the traditional ultrasound breast imaging reporting and data system (BI-RADS) classification in differentiating between benign and malignant breast nodules. For analysis, 88 patients with 110 breast nodules assessed as BI-RADS 3-5 by conventional ultrasound were selected. SWE and SMI evaluations were conducted separately, and all nodules were verified as benign or malignant ones by pathology. Receiver operating characteristic (ROC) curves were plotted after obtaining quantitative parameters of different shear waves of nodules, including maximum (Emax), mean (Emean), minimum (Emin) Young's modulus, modulus standard deviation (SD), and modulus ratio (Eratio). The best cut-off value, specificity, sensitivity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) for diagnosing malignant nodules employing Emax were obtained, and the diagnostic value of combining Emax and BI-RADS classification was compared. SMI graded nodule based on the Alder blood flow grading standard, whereas the BI-RADS classification was based on microvascular morphology. We assessed the diagnostic value of SMI for breast nodules and investigated the diagnostic efficacy of SWE combined with SMI in differentiating benign and malignant breast nodules with BI-RADS classification 3-5. The adjusted the BI-RADS classification using SMI and SWE technologies promoted the sensitivity, specificity, and accuracy of discriminating benign and malignant breast nodules (P < 0.05). The combination of traditional ultrasound BI-RADS classification with SWE and SMI technologies offered high sensitivity, specificity, accuracy, PPV, and NPV for identifying benign and malignant breast lesions. Moreover, combining SWE and SMI technologies with the adjusted BI-RADS classificationhad the best diagnostic efficacy for distinguishing benign and malignant breast nodules with BI-RADS 3-5. The combination of SWE and SMI with the adjusted BI-RADS classification is a promising diagnostic method for differentiating benign and malignant breast nodules.

The value of multimodal magnetic resonance imaging in breast cancer and its correlation with pathological features and prognosis.

This study aimed to explore the diagnostic value of multimodal magnetic resonance imaging (MRI) in breast cancer (BC) and its correlation with pathological features of the disease. Clinical data of 85 BC patients (malignant group) and 85 patients with benign breast diseases (benign group), admitted to 3201 Hospital of Xi'an Jiaotong University Health Science Center from May 2020 to May 2022, were retrospectively collected. Both groups underwent multimodal MRI examinations. We compared the differences in multimodal MRI examination parameters between the groups, as well as between patients with different pathological characteristics and prognoses in the malignant group. The correlation between multimodal MRI examination parameters and pathological features of BC was analyzed. The apparent diffusion coefficient (ADC) of the malignant group was lower than that of the benign group, while the extravascular extracellular volume fraction (Ve), reaction rate constant (Kep), and volume transfer constant (Ktrans) of the malignant group were higher compared to the benign group (p<0.05). In the malignant group, patients with stage III+IV disease, lymph node metastases, and low differentiation had lower ADC values, and higher Ve, Ktrans, and Kep compared to patients with stage I+II disease, no lymph node metastasis, and medium to high differentiation (p<0.05). ADC value negatively correlated with the stage of the disease, lymph node metastases but positively correlated with the degree of differentiation (p<0.05). Ve, Ktrans, and Kep positively correlated with the stage of the disease and lymph node metastasis, and negatively correlated with the degree of differentiation (p<0.05). ADC value of patients with poor prognosis was lower, while Ve, Ktrans, and Kep were higher compared to patients with good prognosis (p<0.05). The Receiver operating characteristic (ROC) analysis showed that ADC, Ve, Ktrans, and Kep have certain predictive values for the poor prognosis of BC patients (p<0.05). Multimodal MRI examination detected obvious differences in the examination parameters of BC patients, and the increase or decrease in these parameters is closely correlated with the pathological characteristics and prognosis of the disease. Multimodal MRI examination can be used for pathological evaluation and prognosis prediction in BC patients.

Role of combined clinical-radiomics model based on contrast-enhanced MRI in predicting the malignancy of breast non-mass enhancements without an additional diffusion-weighted imaging sequence.

In our previous study, we developed a combined diagnostic model based on time-intensity curve (TIC) types and radiomics signature on contrast-enhanced magnetic resonance imaging (CE-MRI) for non-mass enhancement (NME). The model had a high diagnostic ability for differentiation without the additional diffusion-weighted imaging (DWI) sequence. In this study, we aimed to compare the diagnostic performance of the combined clinical-radiomics model based on CE-MRI and DWI in discriminating Breast Imaging-Reporting and Data System (BI-RADS) 4 NME breast lesions, ductal carcinoma in situ (DCIS), and invasive carcinoma. This retrospective study enrolled 364 NME lesions (343 patients). Of these, 183 malignant and 84 benign breast lesions classified as BI-RADS 4 NMEs by the initial diagnosis were reclassified based on the combined clinical-radiomics model and DWI, respectively. The nomogram score (NS) values for malignancy risk derived from the combined clinical-radiomics model and the minimal apparent diffusion coefficient (ADC) values from DWI were calculated and compared. The percentage of false positives were estimated in comparison with the original classification. Receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic value of the NS and minimal ADC values in distinguishing benign and malignant lesions, DCIS, and invasive breast carcinoma. An ablation experiment was used to test the value of the additional DWI sequence. The diagnostic value of the NS values [area under curve (AUC) =0.843; 95% CI: 0.789-0.896] for discriminating the 267 NME breast lesions categorized as BI-RADS 4 was significantly higher than the minimal ADC values (AUC =0.662; 95% CI: 0.590-0.735). The NS values showed higher sensitivity, specificity, and accuracy compared with the minimal ADC values (sensitivity: 80.3% vs. 65.6%; specificity: 79.8% vs. 65.5%; accuracy: 80.1% vs. 65.5%). The NS values and minimal ADC values did not achieve high diagnostic accuracy in discriminating between DCIS and invasive cancer. However, the diagnostic performance of the combined NS-ADC model (AUC =0.731; 95% CI: 0.655-0.806) was higher than that of the NS values alone (P=0.008) and comparable to that of the minimal ADC values (P=0.440). The combined clinical-radiomics model based on CE-MRI could improve the diagnostic performance in discriminating the BI-RADS 4 NME lesions without an additional DWI sequence. However, DWI may improve the diagnostic performance in discriminating DCIS from invasive cancer.

Clinicopathological profile of breast cancer from Chhattisgarh India: A single-center hospital-based study.

Global breast cancer incidence is increasing at an annual rate of 3·1%. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%). However, the data from different parts of India are still lacking and the study was conducted to assess the burden of disease at tertiary referral centers in central India. Retrospective record analysis (June 2013-June 2017) of data from outdoor clinics and pathology reports. The patients aged <15 yrs, nonresidence of Chhattisgarh, and diagnosed outside the study period were excluded. The triple assessment was used to diagnose all breast lumps (sensitivity 99%). Eighty patients were diagnosed having breast carcinoma. The mean age for breast cancer was 39 ± 3.028 years (ranged 31-50 years). Twenty patients had locally advanced breast carcinoma. The predominant religion was Hindu 55.00%. The referral pathway to seek medical care for breast cancer was via a gynecologist in 40% (32/80). Familial breast cancers were in 0.03% (3/80) of patients. None breast cancer patients have previous histology-proven benign breast disease. The mean size of the breast cancer lump was 3.56 cm (ranged 1.0-11.0 cm). Overlying skin ulceration (n = 2), skin infiltration/peau-d'- orange (n = 2), skin tethering (n = 4), and bloody nipple discharge were found in one patient. Breast cancer was diagnosed during lactation (postnatal period) in one patient. The maximum number of patients have tumor size >5 cm (72.6%). Immunohistochemistry and pathological analysis was done on core biopsy (n = 20) and surgical procedure (n = 60). Modified radical mastectomy was done in 52, breast conservative surgery with Sentinal Lymph node biopsy and axillary lymph node dissection in 6, and toilet mastectomy in two patients. The predominant tumors were solid (n = 79/80), with both solid and cystic types (1/80). The solid and cystic lesion on FNAC was of C3b type, and an excision biopsy revealed medullary carcinoma of the breast. Invasive ductal carcinoma-no special type (IDC-NST) was observed to be the most common histopathologic type (n = 70/80), followed by medullary carcinoma (n = 2), metaplastic carcinoma (n = 1), papillary carcinoma (n = 4), Paget disease with DCIS (n = 1), mucinous carcinoma (n = 1), invasive lobular carcinoma (n = 1). One male patient with breast cancer and two female patient having bilateral breast cancer also have IDC-NST.Scarff Bloom Richardson Grade was predominantly graded 2 in 46.25% (37/80) of breast cancer patients (Grade 1 = 9, Grade 2 = 37, Grade 3 = 34). Lymphovascular (LVI) and perineural invasion (PNI) were predominantly without LVI and PVI. (Lymphovascular present and perineural invasion present = 4, Lymphovascular present and perineural invasion absent = 32, Lymphovascular absent and perineural invasion absent = 42, Lymphovascular absent and perineural invasion present = 2). Histological examination of axillary lymph nodes showed the presence of malignant cells in all. Triple-negative breast carcinoma was 26.58% (21/79). Most breast cancer presented at stage II A = 37.5% (30/80) and II B = 28.7% (23/80) of the AJCC staging system. The clinico-epidemio and histological profile of breast cancer in Chhattisgarh is similar to other parts of India. Scarff Bloom Richardson Grade was predominantly grade 2 in 46.25% (37/80) contrary to Grade III (70%) in other series from India.

The association between fibrocystic breast and thyroid autoimmunity

Aims: The relationship between thyroid disorders and breast diseases including breast cancer and benign breast disease, has been a topic of interest and controversy. Our study aimed to investigate the potential association between autoimmune thyroid disease (ATD) and fibrocystic breast disease (FBD) Methods: This retrospective study investigated the relationship between atd an FBD. A total of 242 female patients aged 18 years or older were recruited for the study. Data were collected from medical records and patient interviews. The related parameters were recorded for each participant. Results: This study included two hundred forty-two age-matched (29.78±4.55) and body mass index (BMI)-matched (24±2.38) women. A Mann-Whitney test did not find a statistically significant association between the case and control in terms of Free thyroxine (FT4) (p-value&gt;0.05). There was a statistically significant difference between groups in terms of thyroid-stimulating hormone (TSH) (p-value&lt;0.001), antithyroid peroxidase (Anti-TPO) (p-value&lt;0.001), and anti-thyroglobulin (Anti-TG) (p-value&lt;0.001). A chi-square test did not find a statistically significant association between thyroid autoantibody and fibrocystic breast test results (p-value &gt; 0.05). Conclusion: While our study did not find a significant association between these two conditions, other studies have reported varying results. Hormonal alterations, autoimmune factors, and genetic predispositions are among the potential mechanisms that could explain the associations observed in some studies.

Mammary gland inflammation in rabbits does (Oryctolagus cuniculus): A systematic review.

Mastitis is a common condition in rabbit (Oryctolagus cuniculus) does, especially in rabbitries where mammae inflammations are the leading pathological expression of Staphylococcus aureus infections in reproductive females. It affects both the litter and the lactating female, including subsequent lactations. Non-septic (i.e. galactostasis and cystic mastitis) and septic (i.e. Mastitis acuta; including Mastitis gangrenosa; and Mastitis chronica) mammary inflammations are acknowledged; however, the literature is scarce regarding non-septic inflammations or subclinical mastitis. Thus, the present systematic review highlights and summarizes the existing data concerning mastitis in rabbit does, including diagnosis and treatment plans.

Распространенность доброкачественной дисплазии молочной железы в Уральском федеральном округе

Распространенность доброкачественной дисплазии молочной железы в Уральском федеральном округе

Evaluation of standard breast ultrasonography by adding two-dimensional and three-dimensional shear wave elastography: a prospective, multicenter trial

ObjectiveTo reduce the number of biopsies performed on benign breast lesions categorized as BI-RADS 4–5, we investigated the diagnostic performance of combined two-dimensional and three-dimensional shear wave elastography (2D + 3D SWE) with standard breast ultrasonography (US) for the BI-RADS assessment of breast lesions.MethodsA total of 897 breast lesions, categorized as BI-RADS 3–5, were subjected to standard breast US and supplemented by 2D SWE only and 2D + 3D SWE analysis. Based on the malignancy rate of less than 2% for BI-RADS 3, lesions assessed by standard breast US were reclassified with SWE assessment.ResultsAfter standard breast US evaluation, 268 (46.1%) participants underwent benign biopsies in BI-RADS 4–5 lesions. By using separated cutoffs for upstaging BI-RADS 3 at 120 kPa and downstaging BI-RADS 4a at 90 kPa in 2D + 3D SWE reclassification, 123 (21.2%) participants underwent benign biopsy, resulting in a 54.1% reduction (123 versus 268).ConclusionCombining 2D + 3D SWE with standard breast US for reclassification of BI-RADS lesions may achieve a reduction in benign biopsies in BI-RADS 4–5 lesions without sacrificing sensitivity unacceptably.Clinical relevance statementCombining 2D + 3D SWE with US effectively reduces benign biopsies in breast lesions with categories 4–5, potentially improving diagnostic accuracy of BI-RADS assessment for patients with breast lesions.Trial registrationChiCTR1900026556Key Points• Reduce benign biopsy is necessary in breast lesions with BI-RADS 4–5 category.• A reduction of 54.1% on benign biopsies in BI-RADS 4–5 lesions was achieved using 2D + 3D SWE reclassification.• Adding 2D + 3D SWE to standard breast US improved the diagnostic performance of BI-RADS assessment on breast lesions: specificity increased from 54 to 79%, and PPV increased from 54 to 71%, with slight loss in sensitivity (97.2% versus 98.7%) and NPV (98.1% versus 98.7%).

Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning.

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated great potential for breast cancer diagnosis in which real-time or near real-time diagnosis with high accuracy is desired. We aim to use US-guided DOT to achieve an automated, fast, and accurate classification of breast lesions. We propose a two-stage classification strategy with deep learning. In the first stage, US images and histograms created from DOT perturbation measurements are combined to predict benign lesions. Then the non-benign suspicious lesions are passed through to the second stage, which combine US image features, DOT histogram features, and 3D DOT reconstructed images for final diagnosis. The first stage alone identified 73.0% of benign cases without image reconstruction. In distinguishing between benign and malignant breast lesions in patient data, the two-stage classification approach achieved an area under the receiver operating characteristic curve of 0.946, outperforming the diagnoses of all single-modality models and of a single-stage classification model that combines all US images, DOT histogram, and imaging features. The proposed two-stage classification strategy achieves better classification accuracy than single-modality-only models and a single-stage classification model that combines all features. It can potentially distinguish breast cancers from benign lesions in near real-time.

BRCA1 Promoter Hypermethylation in Malignant Breast Tumors and in the Histologically Normal Adjacent Tissues to the Tumors: Exploring Its Potential as a Biomarker and Its Clinical Significance in a Translational Approach.

The hypermethylation status of the promoter region of the breast cancer 1 (BRCA1), a well-known tumor suppressor gene, has been extensively investigated in the last two decades as a potential biomarker for breast cancer. In this retrospective study, we investigated the prevalence of BRCA1 promoter methylation in 84 human breast tissues, and we correlated this epigenetic silencing with the clinical and histopathological parameters of breast cancer. We used methylation-specific PCR (MSP) to analyze BRCA1 promoter hypermethylation in 48 malignant breast tumors (MBTs), 15 normal adjacent tissues (NATs), and 21 benign breast lesions (BBLs). The results showed that BRCA1 promoter hypermethylation was higher in MBTs (20/48; 41.67%) and NATs (7/15; 46.67%) compared to BBLs (4/21; 19.05%). The high percentage of BRCA1 hypermethylation in the histologically normal adjacent tissues to the tumors (NATs) suggests the involvement of this epigenetic silencing as a potential biomarker of the early genomic instability in NATs surrounding the tumors. The detection of BRCA1 promoter hypermethylation in BBLs reinforces this suggestion, knowing that a non-negligible rate of benign breast lesions was reported to evolve into cancer. Moreover, our results indicated that the BRCA1 promoter hypermethylated group of MBTs exhibited higher rates of aggressive features, as indicated by the SBR III grade (14/19; 73.68%), elevated Ki67 levels (13/16; 81.25%), and Her2 receptor overexpression (5/20; 25%). Finally, we observed a concordance (60%) in BRCA1 promoter hypermethylation status between malignant breast tumors and their paired histologically normal adjacent tissues. This study highlights the role of BRCA1 promoter hypermethylation as a potential useful biomarker of aggressiveness in MBTs and as an early marker of genomic instability in both histological NATs and BBLs.

Role of sureness in evaluating AI/CADx: Lesion-based repeatability of machine learningclassification performance on breast MRI.

Artificial intelligence/computer-aided diagnosis (AI/CADx) and its use of radiomics have shown potential in diagnosis and prognosis of breast cancer. Performance metrics such as the area under the receiver operating characteristic (ROC) curve (AUC) are frequently used as figures of merit for the evaluation of CADx. Methods for evaluating lesion-based measures of performance may enhance the assessment of AI/CADx pipelines, particularly in the situation of comparing performances by classifier. The purpose of this study was to investigate the use case of two standard classifiers to (1) compare overall classification performance of the classifiers in the task of distinguishing between benign and malignant breast lesions using radiomic features extracted from dynamic contrast-enhanced magnetic resonance (DCE-MR) images, (2) define a new repeatability metric (termed sureness), and (3) use sureness to examine if one classifier provides an advantage in AI diagnostic performance by lesion when using radiomic features. Images of 1052 breast lesions (201 benign, 851 cancers) had been retrospectively collected under HIPAA/IRB compliance. The lesions had been segmented automatically using a fuzzy c-means method and thirty-two radiomic features had been extracted. Classification was investigated for the task of malignant lesions (81% of the dataset) versus benign lesions (19%). Two classifiers (linear discriminant analysis, LDA and support vector machines, SVM) were trained and tested within 0.632 bootstrap analyses (2000 iterations). Whole-set classification performance was evaluated at two levels: (1) the 0.632+ bias-corrected area under the ROC curve (AUC) and (2) performance metric curves which give variability in operating sensitivity and specificity at a target operating point (95% target sensitivity). Sureness was defined as 1-95% confidence interval of the classifier output for each lesion for each classifier. Lesion-based repeatability was evaluated at two levels: (1) repeatability profiles, which represent the distribution of sureness across the decision threshold and (2) sureness of each lesion. The latter was used to identify lesions with better sureness with one classifier over another while maintaining lesion-based performance across the bootstrap iterations. In classification performance assessment, the median and 95% CI of difference in AUC between the two classifiers did not show evidence of difference (ΔAUC=-0.003 [-0.031, 0.018]). Both classifiers achieved the target sensitivity. Sureness was more consistent across the classifier output range for the SVM classifier than the LDA classifier. The SVM resulted in a net gain of 33 benign lesions and 307 cancers with higher sureness and maintained lesion-based performance. However, with the LDA there was a notable percentage of benign lesions (42%) with better sureness but lower lesion-based performance. When there is no evidence for difference in performance between classifiers using AUC or other performance summary measures, a lesion-based sureness metric may provide additional insight into AI pipeline design. These findings present and emphasize the utility of lesion-based repeatability via sureness in AI/CADx as a complementary enhancement to other evaluation measures.

Evaluation of miRNA-200c and βIII tubulin as promising markers for clinical decision making in locally advanced breast cancer patients receiving taxanes therapy

BackgroundClinical management of locally advanced breast cancer (LABC) is a common clinical problem. Therefore, finding a good predictor for the efficacy of treatment is crucial. The miRNAs are small non-coding RNAs that play an important role in gene regulation of many biological processes. Class III tubulin (βIII tubulin) are dynamic fibrous cytoskeletal proteins that play a significant role in chromosome segregation during mitosis and meiosis and has been identified to participate in cell proliferation and cancer development. AimTo evaluate the role of miRNA-200c and βIII tubulin as promising markers for clinical decision making in locally advanced breast cancer patients receiving taxanes therapy. MethodsA retrospective study of paraffin sections of 143 female breast patients: 22 patients with benign breast diseases and 121 breast cancer patients receiving neoadjuvant taxane therapy divided to: 94 responder patients and 27 non responder patients. Patients were followed up from date of first diagnosis till end of the study. The expression level of miRNA 200c was evaluated using real time PCR and the expression level of βIII tubulin protein was immunohistochemically estimated. Results were correlated with response to neoadjuvant taxanes therapy. ResultsThe miRNA 200 c was significantly down regulated in patients responding to taxanes as compared with non-responders (p = 0.009). The βIII tubulin protein was significantly highly expressed in ER negative and PR negative breast cancer patients (p = 0.011, p = 0.053, respectively). It was significantly up regulated in triple negative and HER2 enriched (non luminal) cases and down regulated in luminal cases (p = 0.023). ConclusionThe results suggest miRNA 200c may be a predictive marker for response to neoadjuvant taxanes therapy in breast cancer patients, while βIII tubulin protein showed association with molecular subtypes of breast cancer namely luminal versus non luminal cases that may serve as an excellent marker for clinical decision making.

Sleep duration and mortality among breast cancer survivors in the Western New York Exposures and Breast Cancer (WEB) Study.

There is increasing evidence that sleep duration may affect breast cancer survival through effects on circadian function, influencing disease progression. However, further investigation of this association is needed. In a population-based, prospective cohort study of women from the Western New York Exposures and Breast Cancer Study, we examined mortality outcomes with invasive breast cancer identified using the National Death Index. Cox proportion hazards ratios with 95% confidence intervals were used to estimate risk of all-cause (AC) and breast cancer-specific (BC) mortality associated with self-reported usual sleep duration with adjustment for age, race/ethnicity, years of education, body mass index (BMI), menopausal status, pack-years ofsmoking, tumor stage, and estrogen-receptor (ER) status. We further examined associations within strata of BMI, tumor stage, menopausal status, and ER status. A sample of 817 patients with breast cancer were followed for a median of 18.7years, during which 339 deaths were reported, including 132 breast cancer-specific deaths. Those who reported shorter or longer sleep tended to have a slightly higher BMI, to be less proportionately non-Hispanic White, to report a previous history of benign breast disease, and to have consumed more alcohol during their lifetime. We found no significant associations between sleep duration and AC or BC mortality, including within stratified analyses. Sleep duration was not associated with either AC or BC mortality including within strata of BMI, tumor stage, menopausal status, or ER status.

Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions.

To propose a novel model-free data-driven approach based on the voxel-wise mapping of DCE-MRI time-intensity-curve (TIC) profiles for quantifying and visualizing hemodynamic heterogeneity and to validate its potential clinical applications. From December 2018 to July 2022, 259 patients with 325 pathologically confirmed breast lesions who underwent breast DCE-MRI were retrospectively enrolled. Based on the manually segmented breast lesions, the TIC of each voxel within the 3D whole lesion was classified into 19 subtypes based on wash-in rate (nonenhanced, slow, medium, and fast), wash-out enhancement (persistent, plateau, and decline), and wash-out stability (steady and unsteady), and the composition ratio of these 19 subtypes for each lesion was calculated as a new feature set (type-19). The three-type TIC classification, semiquantitative parameters, and type-19 features were used to build machine learning models for identifying lesion malignancy and classifying histologic grades, proliferation status, and molecular subtypes. The type-19 feature-based model significantly outperformed models based on the three-type TIC method and semiquantitative parameters both in distinguishing lesion malignancy (respectively; AUC = 0.875 vs. 0.831, p = 0.01 and 0.875vs. 0.804, p = 0.03), predicting tumor proliferation status (AUC = 0.890 vs. 0.548, p = 0.006 and 0.890 vs. 0.596, p = 0.020), but not in predicting histologic grades (p = 0.820 and 0.970). In addition to conventional methods, the proposed computational approach provides a novel, model-free, data-driven approach to quantify and visualize hemodynamic heterogeneity. Voxel-wise intra-lesion mapping of TIC profiles allows for visualization of hemodynamic heterogeneity and its composition ratio for differentiation of malignant and benign breast lesions. • Voxel-wise TIC profiles were mapped, and their composition ratio was compared between various breast lesions. • The model based on the composition ratio of voxel-wise TIC profiles significantly outperformed the three-type TIC classification model and the semiquantitative parameters model in lesion malignancy differentiation and tumor proliferation status prediction in breast lesions. • This novel, data-driven approach allows the intuitive visualization and quantification of the hemodynamic heterogeneity of breast lesions.

Surgeon and Radiologist Evaluation of Electromagnetic Chip Localization for Benign and Malignant Breast Lesions.

SmartClipTM is a food and drug administration-approved, electromagnetic chip (EMC) localization system that provides three-dimensional navigation for the excision of soft tissue lesions. The purpose of this study was to analyze the accuracy and feasibility of EMC radiologic and surgical localization for benign and malignant breast lesions. An institutional review board-approved, single institution, prospective study from October 2020 to September 2022 of 38 women undergoing breast conserving surgery with EMC localization of a single lesion > 5 mm on mammogram (MMG) or ultrasound (US) imaging. Surveys from performing breast radiologists and breast surgeons were collected after image-guided localization and surgical excision. Seventy-six survey responses from nine radiologists and four surgeons were received. The deployment needle and EMC were highly visible in 86.8% and 76.3% of procedures, respectively. There was no difficulty in deployment for 92.1% of procedures. The EMC was in the correct location on postdeployment MMG in 97.4% of cases. Three instances of EMC migration occurred, one 1 cm from target lesion. The targeted mass and EMC were within the surgical specimen in 97.4% of cases. On specimen radiograph, 39.5% of the EMCs were 0-1 mm from the center of the target lesion, 18.4% were within 2-4 mm, and 23.7% were within 5-10 mm. Mean operating room time for all cases was 65 min. One case required US to localize the target due to console malfunction. There was successful EMC deployment by radiologists with accurate visualization and successful surgical excision in most cases. The EnVisioTM SmartClipTM system is a reproducible and accurate localization method for benign and malignant breast lesions.

Triple assessment breast clinics: The value of clinical core biopsies

BackgroundTriple Assessment Breast Clinics are designed for rapid diagnosis of symptomatic patients. When there is no concordance between clinical and radiological assessment, clinicians perform clinical core biopsies. In patients with a clinically suspicious examination (S4, S5) and normal imaging, clinically guided core biopsy should be performed as per NCCP guidelines. However, substantial research does not exist on the diagnostic value or use of clinical core biopsies in non-suspicious palpable (S3) lesions and practices differ in each health system.AimsThe aim of this research was to assess the diagnostic value of clinical core biopsies in nonsuspicious, probably benign palpable breast lesions (S3) where image guided cores were not indicated (R1/R2).MethodsThe cohort consisted of patients undergoing clinical core biopsies at a Symptomatic Breast Unit from January 2014 to 2019. Data regarding patient demographics, outcome of triple-assessment and incidence of malignancy were obtained from a prospectively maintained database and results were analysed using Minitab 2018.ResultsThree hundred and sixty patients had a clinical core biopsy performed in this period. Clinical examination scores for these patients were S1/S2 (66), S3 (277), S4 (15), and S5 (2). Radiology Scores were R1/R2 (355) and R3(5). Two patients with clinical score S3 (0.6%) were diagnosed with breast cancer due to their clinical cores. Both patients had normal mass imaging. There was no association between uncertain palpable breast lesions (S3), and atypia or malignancy on biopsy results when breast imaging was normal (P = 0.43, χ2 test).ConclusionDespite clinical core biopsies being used in triple assessment, there is no certainty in their value except that there is high clinical suspicion. Imaging modalities are constantly improving and are already well established. When the patient is assigned a clinical score of S3 and has normal radiology, a clinical core biopsy is not required in most cases.

Clinical applications of shear wave dispersion imaging for breast lesions: a pictorial essay.

Shear wave dispersion (SWD) imaging is a newly developed ultrasound technology designed to evaluate the dispersion slope of shear waves, which is related to tissue viscosity. This advanced imaging technique holds potential for distinguishing malignant lesions from benign lesions and normal breast tissue. The SWD slope, as determined by shear wave elastography (SWE), could offer crucial insights into the characterization of breast lesions. This article presents SWE and SWD images of both malignant and benign breast lesions, in addition to normal breast tissue.

SaTransformer: Semantic‐aware transformer for breast cancer classification and segmentation

AbstractBreast cancer classification and segmentation play an important role in identifying and detecting benign and malignant breast lesions. However, segmentation and classification still face many challenges: 1) The characteristics of cancer itself, such as fuzzy edges, complex backgrounds, and significant changes in size, shape, and intensity distribution make accurate segment and classification challenges. 2) Existing methods ignore the potential relationship between classification and segmentation tasks, due to the classification and segmentation being treated as two separate tasks. To overcome these challenges, in this paper, a novel Semantic‐aware transformer (SaTransformer) for breast cancer classification and segmentation is proposed. Specifically, the SaTransformer enables doing the two takes simultaneously through one unified framework. Unlike existing well‐known methods, the segmentation and classification information are semantically interactive, reinforcing each other during feature representation learning and improving the ability of feature representation learning while consuming less memory and computational complexity. The SaTransformer is validated on two publicly available breast cancer datasets – BUSI and UDIAT. Experimental results and quantitative evaluations (accuracy: 97.97%, precision: 98.20%, DSC: 86.34%) demonstrate that the SaTransformer outperforms other state‐of‐the‐art methods.