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Plastic and Reconstructive Surgery Dec 2021This study aims to present normative values for satisfaction with breasts among preoperative breast reconstruction patients as assessed using the BREAST-Q instrument and...
BACKGROUND
This study aims to present normative values for satisfaction with breasts among preoperative breast reconstruction patients as assessed using the BREAST-Q instrument and to delineate factors associated with preoperative breast satisfaction.
METHODS
A retrospective analysis of prospectively collected data was performed examining women undergoing postmastectomy breast reconstruction at a tertiary care center who preoperatively completed the BREAST-Q from 2010 to 2017. Because breast satisfaction scores were nonnormally distributed, scores were categorized into quartiles for analysis. Patient- and treatment-level variables were tested in a multivariable ordinal logistic regression model as predictors of breast satisfaction. Preoperative satisfaction was also tested for association with choice of reconstructive modality.
RESULTS
Among 1306 postmastectomy reconstruction patients included in the study, mean preoperative Satisfaction with Breasts score was 61.8 ± 21.5 and the median score was 58.0 (interquartile range, 48 to 70). Factors associated with significantly lower preoperative satisfaction included history of psychiatric diagnosis, preoperative radiotherapy, marital status (married), and higher body mass index. Factors associated with significantly higher scores were malignancy (localized tumor), medium bra size (B to C cup), and self-identification as black. Preoperative breast satisfaction was lower among patients who elected autologous reconstruction than among those with implant reconstruction (p < 0.001).
CONCLUSIONS
Preoperative breast satisfaction is influenced by multiple factors. Understanding these factors may improve preoperative counseling and expectation management for patients who undergo postmastectomy breast reconstruction.
CLINICAL QUESTION/LEVEL OF EVIDENCE
Risk, III.
Topics: Adult; Aged; Breast; Breast Neoplasms; Counseling; Family Characteristics; Female; Humans; Mammaplasty; Mastectomy; Middle Aged; Motivation; Neoadjuvant Therapy; Patient Reported Outcome Measures; Patient Satisfaction; Personal Satisfaction; Preoperative Period; Prospective Studies; Psychometrics; Radiotherapy, Adjuvant; Retrospective Studies; Surveys and Questionnaires; Treatment Outcome; Young Adult
PubMed: 34847108
DOI: 10.1097/PRS.0000000000008521 -
Clinical Obstetrics and Gynecology Dec 2016Percutaneous imaging-guided core needle biopsy (CNB) is a less invasive and less expensive alternative to surgical biopsy for the evaluation of breast lesions. After a... (Review)
Review
Percutaneous imaging-guided core needle biopsy (CNB) is a less invasive and less expensive alternative to surgical biopsy for the evaluation of breast lesions. After a CNB the radiologist determine if there is concordance between the pathology, imaging, and clinical findings. Patient management after CNB diagnosis of high-risk breast lesion varies. Surgical excision is warranted for lesions yielding a CNB diagnosis of ADH; however controversy exists regarding the need for surgical excision after CNB diagnosis of radial scar, papillary lesion, atypical lobular hyperplasia (ALH), or lobular carcinoma in situ (LCIS). Repeat CNB or surgical excision is warranted if histologic findings and imaging findings are discordant.
Topics: Biopsy, Large-Core Needle; Breast; Breast Diseases; Breast Neoplasms; Female; Humans; Magnetic Resonance Imaging; Precancerous Conditions; Risk Factors
PubMed: 27681693
DOI: 10.1097/GRF.0000000000000234 -
AJR. American Journal of Roentgenology May 2021Background parenchymal uptake (BPU) on molecular breast imaging (MBI) was identified in a case-control study as a breast cancer risk factor beyond mammographic density....
Background parenchymal uptake (BPU) on molecular breast imaging (MBI) was identified in a case-control study as a breast cancer risk factor beyond mammographic density. To our knowledge, this finding has not yet been confirmed in a cohort study. The objectives of this study were to examine the association of BPU with breast cancer and to estimate the absolute risk and discriminatory accuracy of BPU in a cohort study. A retrospective cohort was established that included women without a history of breast cancer who underwent MBI from 2004 to 2015. Radiologists who were blinded to future breast cancer diagnoses assessed BPU on baseline MBI examinations as low (photopenic or minimal) or elevated (mild, moderate, or marked). Associations of BPU with breast cancer were estimated using multivariable Cox proportional hazards models of the time to diagnosis. The 5-year absolute risk was calculated for study subgroups. The discriminatory accuracy of BPU was also assessed. Among 2992 women (mean age, 56.3 years; SD, 10.6 years) who underwent MBI, breast cancer events occurred in 144 women (median follow-up, 7.3 years). Median time to diagnosis after MBI was 4.2 years (range, 0.5-11.6 years). Elevated BPU was associated with a greater breast cancer risk (hazard ratio [HR], 2.39; 95% CI, 1.68-3.41; ≤ .001). This association remained in postmenopausal women (HR, 3.50; 95% CI, 2.31-5.31; < .001) but was not significant in premenopausal women (HR, 1.29; 95% CI, 0.72-2.32; = .39). The 5-year absolute risk of breast cancer was 4.3% (95% CI, 2.9-5.7%) for women with elevated BPU versus 2.5% (95% CI, 1.8-3.1%) for those with low BPU. Postmenopausal women with dense breasts and elevated BPU had a 5-year absolute risk of 8.1% (95% CI, 4.3-11.8%) versus 2.8% (1.8-3.8%) for those with low BPU. Among postmenopausal women, discriminatory accuracy for invasive cancer was improved with the addition of BPU versus use of the Gail risk score alone (C statistic, 65.1 vs 59.1; = .04) or use of the Breast Cancer Surveillance Consortium risk score alone (C statistic, 66.4 vs 60.4; = .04). BPU on MBI is an independent risk factor for breast cancer, with the strongest association observed among postmenopausal women with dense breasts. In postmenopausal women, BPU provides incremental discrimination in predicting breast cancer when combined with either the Gail model or the Breast Cancer Surveillance Consortium model. Observation of elevated BPU on MBI may identify a subset of women with dense breasts who would benefit most from supplemental screening or preventive options.
Topics: Adult; Aged; Aged, 80 and over; Breast; Breast Density; Breast Neoplasms; Cohort Studies; Female; Humans; Mammography; Middle Aged; Molecular Imaging; Parenchymal Tissue; Reproducibility of Results; Retrospective Studies
PubMed: 32755210
DOI: 10.2214/AJR.20.23854 -
Radiographics : a Review Publication of... 2019Full-field digital mammography (FFDM), the standard of care for breast cancer screening, has some limitations. With the advent of digital breast tomosynthesis (DBT),... (Review)
Review
Full-field digital mammography (FFDM), the standard of care for breast cancer screening, has some limitations. With the advent of digital breast tomosynthesis (DBT), improvements including decreased recall rates and increased cancer detection rates have been observed. The quasi-three-dimensional capability of DBT reduces breast tissue overlap, a significant limitation of FFDM. However, early studies demonstrate that a few cancers detected at FFDM may not be diagnosed at DBT-only screening, and lesions with calcifications as the dominant feature may look less suspicious at DBT or not be visible at all. These findings support the use of combined FFDM and DBT protocols to optimize screening performance. However, this combination would approximately double the patient's radiation exposure. The development of computer algorithms that generate two-dimensional synthesized mammography (SM) views from DBT has improved calcification conspicuity and sensitivity. Therefore, SM may substitute for FFDM in screening protocols, reducing radiation exposure. DBT plus SM demonstrates significantly better performance than that of FFDM alone, although there are reports of missed malignant calcifications. Thus, some centers continue to perform FFDM with DBT. Use of DBT in breast imaging has also necessitated the development of DBT-guided biopsy. DBT-guided biopsy may have a higher success rate than that of stereotactic biopsy, with a shorter procedure time. While DBT brings substantial improvements to breast cancer imaging, it is important to be aware of its strengths and limitations regarding detection of calcifications. This article reviews the imaging appearance of breast calcifications at DBT, discusses calcification biopsy techniques, and provides an overview of the current literature. Online supplemental material is available for this article. RSNA, 2019 An earlier incorrect version of this article appeared online. This article was corrected on February 13, 2019.
Topics: Biopsy; Breast; Breast Diseases; Breast Neoplasms; Calcinosis; Diagnosis, Differential; Early Detection of Cancer; Female; Humans; Image Interpretation, Computer-Assisted; Mammography; Sensitivity and Specificity
PubMed: 30681901
DOI: 10.1148/rg.2019180124 -
The Medical Clinics of North America Jul 2017The approach to breast cancer screening has changed over time from a general approach to a more personalized, risk-based approach. Women with dense breasts, one of the... (Review)
Review
The approach to breast cancer screening has changed over time from a general approach to a more personalized, risk-based approach. Women with dense breasts, one of the most prevalent risk factors, are now being informed that they are at increased risk of developing breast cancer and should consider supplemental screening beyond mammography. This article reviews the current evidence regarding the impact of breast density relative to other known risk factors, the evidence regarding supplemental screening for women with dense breasts, supplemental screening options, and recommendations for physicians having shared decision-making discussions with women who have dense breasts.
Topics: Age Factors; Breast; Breast Density; Breast Neoplasms; Early Detection of Cancer; Female; Humans; Magnetic Resonance Imaging; Mammography; Practice Guidelines as Topic; Risk Assessment; Risk Factors; Ultrasonography, Mammary
PubMed: 28577623
DOI: 10.1016/j.mcna.2017.03.005 -
Breast Cancer Research : BCR Aug 2017Accurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer...
BACKGROUND
Accurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors (age, race/ethnicity, family history of breast cancer, history of breast biopsy, and BI-RADS breast density) improves identifying women with dense breasts at high breast cancer risk.
METHODS
We conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume.
RESULTS
Compared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk (OR 2.87, 95% CI 1.84-4.47, and OR 2.56, 95% CI 1.87-3.52, respectively), whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk (OR 1.53, 95% CI 0.75-3.09, and OR 1.50, 95% CI 0.82-2.73, respectively). Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623-0.654, vs. C-statistic 0.614, 95% CI 0.598-0.630, respectively; P < 0.001). Women with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%.
CONCLUSIONS
Risk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone.
Topics: Aged; Breast; Breast Density; Breast Neoplasms; Case-Control Studies; Early Detection of Cancer; Female; Humans; Middle Aged; Neoplasm Staging; Odds Ratio; Public Health Surveillance; Risk Assessment; Risk Factors
PubMed: 28830497
DOI: 10.1186/s13058-017-0887-5 -
Nigerian Journal of Clinical Practice Oct 2023Different imaging techniques are used in the diagnosis of breast cancer. The low sensitivity of mammography to detect cancer in the dense breast parenchyma and the lack...
BACKGROUND
Different imaging techniques are used in the diagnosis of breast cancer. The low sensitivity of mammography to detect cancer in the dense breast parenchyma and the lack of standard application of digital breast tomosynthesis (DBT) are some of the problems. Therefore, breast cancer imaging techniques should be compared in terms of conspicuity and characterization of lesions.
AIM
Full-field digital mammography (DM) and synthetic mammography (SM) which are obtained from the slices of digital breast tomosynthesis (DBT) give similar results in terms of conspicuity and characterization of the lesions in detecting breast cancer.
PATIENTS AND METHODS
In this retrospective study, 47 women diagnosed with breast cancer were included in the study. DM, SM, and DBT images were evaluated by scoring the conspicuity of the index lesion in the parenchyma and its characterization in terms of contour and shape with a 4-point scale. In addition, the conspicuity of the lesions in relation to lesion size and breast density was examined with these three techniques.
RESULTS
There is no significant difference between DM and SM techniques for index lesion conspicuity and characterization; however, the imaging score of DBT is significantly higher than other techniques for the conspicuity and characterization of the lesions. In terms of the conspicuity of the lesions in relation to lesion size, DM and SM techniques show significant difference according to the size of the lesion, whereas the DBT technique did not show significant difference. While mammography type is a determinant of lesion conspicuity in only DM and SM techniques, conspicuity findings do not differ significantly in the DBT technique.
CONCLUSION
In conclusion, it was shown that standard images and SM images obtained from DBT did not differ significantly in terms of conspicuity and characterization of lesions. Thus, DBT is significantly superior to the DM and SM images. While the DM and SM images are more successful in showing large lesions and lesion detection in nondense breasts, DBT images were not affected by lesion size and breast density.
Topics: Humans; Female; Breast Neoplasms; Mammography; Breast Density; Retrospective Studies; Breast
PubMed: 37929519
DOI: 10.4103/njcp.njcp_532_22 -
Archives of Pathology & Laboratory... Aug 2022This review article is a result of the breast pathology lectures given at the Sixth Chinese American Pathologists Association annual diagnostic pathology course in... (Review)
Review
CONTEXT.—
This review article is a result of the breast pathology lectures given at the Sixth Chinese American Pathologists Association annual diagnostic pathology course in October 2020 (held virtually due to COVID-19).
OBJECTIVE.—
To update recent developments, in this review article, the authors wrote minireviews in the following 4 areas: lobular neoplasm, adenomyoepithelial lesions, papillary lesions, and fibroepithelial lesions.
DATA SOURCES.—
The sources include extensive literature review, personal research, and experience.
CONCLUSIONS.—
With the wide practice of screening mammography, these lesions are not uncommon in image-guided core biopsies and excisional specimens. Many recent developments have emerged in understanding these lesions. We aim to provide readers with concise updates for each of these lesions with a focus on recent updates in definitions, diagnostic criteria, management, and molecular profiles that are most relevant to the daily practice of pathology and patient management.
Topics: Breast; Breast Neoplasms; COVID-19; Early Detection of Cancer; Female; Humans; Mammography; Precancerous Conditions
PubMed: 34270716
DOI: 10.5858/arpa.2021-0091-RA -
Tomography (Ann Arbor, Mich.) Jan 2023Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer...
Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing radiation exposure issues, a novel device (i.e. MammoWave) based on low-power radio-frequency signals has been developed for breast lesion detection. The MammoWave is a microwave device and is under clinical validation phase in several hospitals across Europe. The device transmits non-invasive microwave signals through the breast and accumulates the backscattered (returned) signatures, commonly denoted as the S21 signals in engineering terminology. Backscattered (complex) S21 signals exploit the contrast in dielectric properties of breasts with and without lesions. The proposed research is aimed to automatically segregate these two types of signal responses by applying appropriate supervised machine learning (ML) algorithm for the data emerging from this research. The support vector machine with radial basis function has been employed here. The proposed algorithm has been trained and tested using microwave breast response data collected at one of the clinical validation centres. Statistical evaluation indicates that the proposed ML model can recognise the MammoWave breasts signal with no radiological finding (NF) and with radiological findings (WF), i.e., may be the presence of benign or malignant lesions. A sensitivity of 84.40% and a specificity of 95.50% have been achieved in NF/WF recognition using the proposed ML model.
Topics: Humans; Female; Microwaves; Breast; Breast Neoplasms; Supervised Machine Learning; Technology
PubMed: 36648997
DOI: 10.3390/tomography9010010 -
Tomography (Ann Arbor, Mich.) Dec 2022Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely,... (Review)
Review
Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in a mammogram, or a region is changing rapidly, it is more likely to be suspicious, compared to a lesion that remains unchanged and it is usually benign. However, visual evaluation of mammograms is challenging even for expert radiologists. For this reason, various Computer-Aided Diagnosis (CAD) algorithms are being developed to assist in the diagnosis of abnormal breast findings using mammograms. Most of the current CAD systems do so using only the most recent mammogram. This paper provides a review of the development of methods to emulate the radiological approach and perform automatic segmentation and/or classification of breast abnormalities using sequential mammogram pairs. It begins with demonstrating the importance of utilizing prior views in mammography, through the review of studies where the performance of expert and less-trained radiologists was compared. Following, image registration techniques and their application to mammography are presented. Subsequently, studies that implemented temporal analysis or subtraction of temporally sequential mammograms are summarized. Finally, a description of the open access mammography datasets is provided. This comprehensive review can serve as a thorough introduction to the use of prior information in breast cancer CAD systems but also provides indicative directions to guide future applications.
Topics: Humans; Female; Breast Neoplasms; Mammography; Diagnosis, Computer-Assisted; Breast; Computers
PubMed: 36548533
DOI: 10.3390/tomography8060241