Fibroglandular Tissue Research Articles

The Effect of Breast Parenchymal Density on Breast Cancer Subtypes and Prognostic Factors

Objective: Mammographic breast parenchymal density is the radiological composition of radiodense fibroglandular tissue and radiolucent adipose tissue. Dense breasts restrict the imaging of lesions detected by mammography and reduce the sensitivity of mammography. In this study, we investigated correlation between breast parenchymal density and prognosis, tumor biology, reoperation rates and prognostic factors.
 Methods: Patients who were operated for breast cancer between January 2017-December 2019 were included. The craniocaudal and mediolateral images obtained during conventional mammograms were collected in a database. The appearance of radiologically dense and lucent areas on mammography was taken as the mammographic percentage consistent with the current literatüre
 Results: According to breast parenchymal density, there were 21 patients (14.7%) with type 1 breast density, 29 (20.3%) with type 2, 47 (32.9%) with type 3 and 46 (32.1%) with type 4. Mean survival time due to breast cancer was 27.45±17.85 months (1-63), and 139 patients (97.2%) lived, while 4 (2.8%) were exitus.
 Conclusion: Increased breast parenchymal density is a crucial risk factor for breast cancer. The effects of density on tumor biology and prognosis are still controversial. Although increased breast density seems to have an effect on indicators of poor prognosis. Additional examinations should be taken into consideration in order not to cause delay or skipping in diagnosis due to the decreased sensitivity of mammography in dense breasts

MRI background parenchymal enhancement, fibroglandular tissue, and mammographic breast density in patients with invasive lobular breast cancer on adjuvant endocrine hormonal treatment: associations with survival

BackgroundTo investigate if baseline and/or changes in contralateral background parenchymal enhancement (BPE) and fibroglandular tissue (FGT) measured on magnetic resonance imaging (MRI) and mammographic breast density (MD) can be used as imaging biomarkers for overall and recurrence-free survival in patients with invasive lobular carcinomas (ILCs) undergoing adjuvant endocrine treatment.MethodsWomen who fulfilled the following inclusion criteria were included in this retrospective HIPAA-compliant IRB-approved study: unilateral ILC, pre-treatment breast MRI and/or mammography from 2000 to 2010, adjuvant endocrine treatment, follow-up MRI, and/or mammography 1–2 years after treatment onset. BPE, FGT, and mammographic MD of the contralateral breast were independently graded by four dedicated breast radiologists according to BI-RADS. Associations between the baseline levels and change in levels of BPE, FGT, and MD with overall survival and recurrence-free survival were assessed using Kaplan–Meier survival curves and Cox regression analysis.ResultsTwo hundred ninety-eight patients (average age = 54.1 years, range = 31–79) fulfilled the inclusion criteria. The average follow-up duration was 11.8 years (range = 2–19). Baseline and change in levels of BPE, FGT, and MD were not significantly associated with recurrence-free or overall survival. Recurrence-free and overall survival were affected by histological subtype (p < 0.0001), number of metastatic axillary lymph nodes (p < 0.0001), age (p = 0.01), and adjuvant endocrine treatment duration (p < 0.001).ConclusionsQualitative evaluation of BPE, FGT, and mammographic MD changes cannot predict which patients are more likely to benefit from adjuvant endocrine treatment.

A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation

Background and ObjectiveBreast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer.It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard.This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation. MethodsA multi-center study, composed of 1785 women whose “for presentation” mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. ResultsThe results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. ConclusionsAn automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.

Fully automatic classification of breast MRI background parenchymal enhancement using a transfer learning approach.

Marked enhancement of the fibroglandular tissue on contrast-enhanced breast magnetic resonance imaging (MRI) may affect lesion detection and classification and is suggested to be associated with higher risk of developing breast cancer. The background parenchymal enhancement (BPE) is qualitatively classified according to the BI-RADS atlas into the categories “minimal,” “mild,” “moderate,” and “marked.” The purpose of this study was to train a deep convolutional neural network (dCNN) for standardized and automatic classification of BPE categories.This IRB-approved retrospective study included 11,769 single MR images from 149 patients. The MR images were derived from the subtraction between the first post-contrast volume and the native T1-weighted images. A hierarchic approach was implemented relying on 2 dCNN models for detection of MR-slices imaging breast tissue and for BPE classification, respectively. Data annotation was performed by 2 board-certified radiologists. The consensus of the 2 radiologists was chosen as reference for BPE classification. The clinical performances of the single readers and of the dCNN were statistically compared using the quadratic Cohen's kappa.Slices depicting the breast were classified with training, validation, and real-world (test) accuracies of 98%, 96%, and 97%, respectively. Over the 4 classes, the BPE classification was reached with mean accuracies of 74% for training, 75% for the validation, and 75% for the real word dataset. As compared to the reference, the inter-reader reliabilities for the radiologists were 0.780 (reader 1) and 0.679 (reader 2). On the other hand, the reliability for the dCNN model was 0.815.Automatic classification of BPE can be performed with high accuracy and support the standardization of tissue classification in MRI.

Does mammographic density remain a radiological challenge in the digital era?

The low subject contrast between cancerous and fibroglandular tissue could obscure breast abnormalities. To investigate radiologists' performance for detection of breast cancer in low and high mammographic density (MD) when cases are digitally acquired. A test set of 60 digital mammography cases, of which 20 were cancerous, were examined by 17 radiologists. Mammograms were categorized as low (≤50%) or high (>50%) MD and rated for suspicion of malignancy using the Royal Australian and New Zealand College of Radiology (RANZCR) classification system. Radiologist demographics including cases read per year, age, subspecialty, and years of reporting were recorded. Radiologist performance was analyzed by the following metrics: sensitivity; specificity; area under the receiver operating characteristic (ROC) curve (AUC), location sensitivity, and jackknife free-response ROC (JAFROC) figure of merit (FOM). Comparing high to low MD cases, radiologists showed a significantly higher sensitivity (P = 0.015), AUC (P = 0.003), location sensitivity (P = 0.002), and JAFROC FOM (P = 0.001). In high compared to low MD cases, radiologists with <1000 annual reads and radiologists with no mammographic subspecialty had significantly higher AUC, location sensitivity, and JAFROC FOM. Radiologists with ≥1000 annual reads and radiologists with mammography subspecialty demonstrated a significant increase in location sensitivity in high compared to low MD cases. In this experimental situation, radiologists' performance was higher when reading cases with high compared to low MD. Experienced radiologists were able to precisely localize lesions in breasts with higher MD. Further studies in unselected screening materials are needed to verify the results.

A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms.

Mammograms are commonly employed in the large scale screening of breast cancer which is primarily characterized by the presence of malignant masses. However, automated image-level detection of malignancy is a challenging task given the small size of the mass regions and difficulty in discriminating between malignant, benign mass and healthy dense fibro-glandular tissue. To address these issues, we explore a two-stage Multiple Instance Learning (MIL) framework. A Convolutional Neural Network (CNN) is trained in the first stage to extract local candidate patches in the mammograms that may contain either a benign or malignant mass. The second stage employs a MIL strategy for an image level benign vs. malignant classification. A global image-level feature is computed as a weighted average of patch-level features learned using a CNN. Our method performed well on the task of localization of masses with an average Precision/Recall of 0.76/0.80 and achieved an average AUC of 0.91 on the image-level classification task using a five-fold cross-validation on the INbreast dataset. Restricting the MIL only to the candidate patches extracted in Stage 1 led to a significant improvement in classification performance in comparison to a dense extraction of patches from the entire mammogram.

Surgical Management of High Grade Gynecomastia: Combination of Liposuction, Glandular Resection and a Circumareolar Skin Reduction

Background: Gynecomastia is defined as an excessivelydeveloped male breast to the extent it resembles the femaleone. Emotional discomfort and social embracement motivatethe patients to seek a medical advice, to restore the masculinebreast shape. In high grade gynecomastia, the contributingfactors are excess redundant skin, fibro-glandular tissue andabundant adiposity, each component should be consideredseparately in the patient's surgical plan. Various surgicalprocedures were succeeded to correct this deformity but withdrawbacks such as residual unsightly scar, or contour irregularities.Objectives: Were to treat high grades of gynecomastiawith a multifaceted procedure; by combining liposuction,glandular excision together with excess skin resection throughcircumareolar concentric approach.Aim: This study has evaluated that procedure as regardtechnical refinements, early and late complications and aestheticoutcome.Method: In this case series, eighteen healthy male patientswho had bilateral gynecomastia, had undergone correction oftheir breast deformity with that multifaceted procedure betweenJanuary 2016 and June 2017, their ages ranged from [17-61years old, with mean ± 30.94].Results: All patients were satisfied with the results exceptone patient who had undergone reoperation. The early complicationrate was 27.7%; it was consistent with other series.The follow-up aesthetic evaluation scale showed that 65% ofpatients with very good result, 22% of patients with goodresult, 17% patients with average result, and only one patienthad poor result.Conclusion: This combination had succeeded to addressthe contributing causes of severe gynecomastia. The problemsof excess and displaced tissues were properly dealt withthrough circumareolar approach to achieve a pleasant scarconfined to the areola with flat male chest

Relationship Between Breast Ultrasound Background Echotexture and Magnetic Resonance Imaging Background Parenchymal Enhancement and the Effect of Hormonal Status Thereon.

We studied the relationship between breast ultrasound background echotexture (BET) and magnetic resonance imaging (MRI) background parenchymal enhancement (BPE) and whether this relationship varied with hormonal status and amount of fibroglandular tissue (FGT) on MRI. Two hundred eighty-three Korean women (52.1 years; range = 27-79 years) with newly diagnosed primary breast cancer who underwent preoperative breast ultrasound and MRI were retrospectively studied. Background echotexture, BPE, and FGT were classified into 4 categories, and age, menopausal status, menstrual cycle regularity, and menstrual cycle stage at MRI were recorded. Background echotexture and BPE relationship was assessed overall, and in menopausal, FGT, menstrual cycle regularity, and menstrual cycle stage subgroups. Background echotexture and BPE correlated in women overall, and menopausal, FGT, and menstrual cycle subgroups and those in the first half of the cycle (all P < 0.001). Background echotexture reflects BPE, regardless of menopausal status, menstrual cycle regularity, and FGT and may be a biomarker of breast cancer risk.

Development of patient-specific 3D-printed breast phantom using silicone and peanut oils for magnetic resonance imaging.

Despite increasing reports of 3D printing in medical applications, the use of 3D printing in breast imaging is limited, thus, personalized 3D-printed breast model could be a novel approach to overcome current limitations in utilizing breast magnetic resonance imaging (MRI) for quantitative assessment of breast density. The aim of this study is to develop a patient-specific 3D-printed breast phantom and to identify the most appropriate materials for simulating the MR imaging characteristics of fibroglandular and adipose tissues. A patient-specific 3D-printed breast model was generated using 3D-printing techniques for the construction of the hollow skin and fibroglandular region shells. Then, the T1 relaxation times of the five selected materials (agarose gel, silicone rubber with/without fish oil, silicone oil, and peanut oil) were measured on a 3T MRI system to determine the appropriate ones to represent the MR imaging characteristics of fibroglandular and adipose tissues. Results were then compared to the reference values of T1 relaxation times of the corresponding tissues: 1,324.42±167.63 and 449.27±26.09 ms, respectively. Finally, the materials that matched the T1 relaxation times of the respective tissues were used to fill the 3D-printed hollow breast shells. The silicone and peanut oils were found to closely resemble the T1 relaxation times and imaging characteristics of these two tissues, which are 1,515.8±105.5 and 405.4±15.1 ms, respectively. The agarose gel with different concentrations, ranging from 0.5 to 2.5 wt%, was found to have the longest T1 relaxation times. A patient-specific 3D-printed breast phantom was successfully designed and constructed using silicone and peanut oils to simulate the MR imaging characteristics of fibroglandular and adipose tissues. The phantom can be used to investigate different MR breast imaging protocols for the quantitative assessment of breast density.

Comparison of Segmentation Methods in Assessing Background Parenchymal Enhancement as a Biomarker for Response to Neoadjuvant Therapy.

Breast parenchymal enhancement (BPE) has shown association with breast cancer risk and response to neoadjuvant treatment. However, BPE quantification is challenging, and there is no standardized segmentation method for measurement. We investigated the use of a fully automated breast fibroglandular tissue segmentation method to calculate BPE from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for use as a predictor of pathologic complete response (pCR) following neoadjuvant treatment in the I-SPY 2 TRIAL. In this trial, patients had DCE-MRI at baseline (T0), after 3 weeks of treatment (T1), after 12 weeks of treatment and between drug regimens (T2), and after completion of treatment (T3). A retrospective analysis of 2 cohorts was performed: one with 735 patients and another with a final cohort of 340 patients, meeting a high-quality benchmark for segmentation. We evaluated 3 subvolumes of interest segmented from bilateral T1-weighted axial breast DCE-MRI: full stack (all axial slices), half stack (center 50% of slices), and center 5 slices. The differences between methods were assessed, and a univariate logistic regression model was implemented to determine the predictive performance of each segmentation method. The results showed that the half stack method provided the best compromise between sampling error from too little tissue and inclusion of incorrectly segmented tissues from extreme superior and inferior regions. Our results indicate that BPE calculated using the half stack segmentation approach has potential as an early biomarker for response to treatment in the hormone receptor–negative and human epidermal growth factor receptor 2–positive subtype.

Relaxation-compensated CEST (chemical exchange saturation transfer) imaging in breast cancer diagnostics at 7T

PurposeTo investigate whether fat-corrected and relaxation-compensated amide proton transfer (APT) and guanidyl CEST-MRI enables the detection of signal intensity differences between breast tumors and normal-appearing fibroglandular tissue in patients with newly-diagnosed breast cancer. MethodTen patients with newly-diagnosed breast cancer and seven healthy volunteers were included in this prospective IRB-approved study. CEST-MRI was performed on a 7 T-whole-body scanner followed by a multi-Lorentzian fit analysis. APT and guanidyl CEST signal intensities were quantified in the tumor and in healthy fibroglandular tissue after correction of B0/B1-field inhomogeneities, fat signal contribution, T1- and T2-relaxation; signal intensity differences of APT and guanidyl resonances were compared using Mann–Whitney-U-tests. Pearson correlations between tumor CEST signal intensities and the proliferation index Ki-67 were performed. ResultsAPT CEST signal in tumor tissue (6.70 ± 1.38%Hz) was increased compared to normal-appearing fibroglandular tissue of patients (3.56 ± 0.54%Hz, p = 0.001) and healthy volunteers (3.70 ± 0.68%Hz, p = 0.001). Further, a moderate positive correlation was found between the APT signal and the proliferation index Ki-67 (R2 = 0.367, r = 0.606, p = 0.11). Guanidyl CEST signal was also increased in tumor tissue (5.24 ± 1.85%Hz) compared to patients’ (2.42 ± 0.45%Hz, p = 0.006) and volunteers’ (2.36 ± 0.54%Hz, p < 0.001) normal-appearing fibroglandular tissue and a positive correlation with the Ki-67 level was observed (R2 = 0.365, r = 0.604, p = 0.11). APT and guanidyl CEST signal in normal-appearing fibroglandular tissue was not different between patients and healthy volunteers (p = 0.88; p = 0.93). ConclusionRelaxation-compensated and fat-corrected CEST-MRI allowed a non-invasive differentiation of breast cancer and normal-appearing breast tissue. Thus, this approach represents a contrast agent-free method that may help to increase diagnostic accuracy in MR-mammography.

Non-Palpable Invasive Lobular Carcinoma Initially Diagnosed by Inguinal Lymph Node Biopsy

Metastasis from breast cancer to the inguinal lymph nodes or the vulva is a rare event with very few cases reported in literature. A 38-year-old lady presented complaining of progressive abdominal enlargement. Physical examination was unremarkable apart from abdominal enlargement and enlarged right inguinal lymph node. Radiology showed marked ascites, omental thickening, bilateral small ovarian masses, suspicious right inguinal lymph node (LN) as well as breast fibrocystic disease. Cytological examination of the ascitic fluid revealed malignant cells. Core needle biopsy from the inguinal LN suggested metastatic breast carcinoma. Breast MRI showed heterogeneous fibro-glandular tissue and moderate parenchymal enhancement. Random core needle biopsies from the breast led to the diagnosis of invasive lobular carcinoma. The patient received 8 cycles of docetaxel/carboplatin protocol, with partial response. Next, she was maintained on hormonal treatment (Anastrazole) till the ascites re- accumulated. Vulvar nodules appeared whose biopsy revealed infiltration by the same tumoral tissue. She received 6 cycles of Endoxan and Pharmarubicin till regression was achieved, and then she was subjected to Exemestane. Thorough clinical assessment including of all lymph node basins and gynecological assessment are crucial in all breast cancer patients, particularly those with invasive lobular subtypes.&#x0D; Keywords: Breast neoplasms, Lobular, Vulva, Vulvar metastasis, Neoplasm metastasis

3,3'-Diindolylmethane (DIM): A nutritional intervention and its impact on breast density in healthy BRCA carriers compared to non-treated carriers—A prospective clinical trial.

1556 Background: Women who carry the BRCA mutation are at high lifetime risk of breast cancer, but there is no consensus regarding an effective and safe chemoprevention strategy. A large body of evidence suggests that 3,3-diindolylmethane (DIM), a dimer of indole-3-carbinol (I3C) found in cruciferous vegetables, can potentially prevent carcinogenesis and tumor development. The primary aim of this prospective study was to investigate the effect of DIM supplementation on breast density, a recognized predictive factor of breast-cancer risk. Methods: Participants were 23 healthy female BRCA carriers (median age 47 years; 78% postmenopausal) who were treated with oral DIM 100 mgx1/d for one year. The amount of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE) on magnetic resonance imaging (MRI) performed before and after the intervention were scored by two independent expert radiologists using the Breast Imaging and Reporting Data System (BI-RADS). Each woman in the cohort was matched by age (within 3 years) and menopausal status to a woman attending the clinic who was not participating in the study and who underwent breast MRI in parallel year. Results: A decrease in the average score for FGT amount from 2.8±0.8 at onset to 2.65±0.842.8 after one year (p = 0.031), with no significant change in BPE (p = 0.429). A group of DIM-untreated age- and menopausal-status-matched clinic patients did not show a significant change in FGT amount (p = 0.33) or BPE (p = 0.814) in a parallel year. Mean estradiol level decreased from 159 to 102 pmol/L (p = 0.01), and mean testosterone level, from 0.42 to 0.31 pmol/L (p = 0.007). Side effects were grade 1. Conclusions: One year’s supplementation with DIM 100 mgX1/d in BRCA carriers was associated with a significant decline in FGT amount on MRI. Larger randomized studies are warranted to corroborate these findings. Clinical trial information: NCT02197000.

Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks

Fibroglandular tissue (FGT) segmentation is a crucial step for quantitative analysis of background parenchymal enhancement (BPE) in magnetic resonance imaging (MRI), which is useful for breast cancer risk assessment. In this study, we develop an automated deep learning method based on a generative adversarial network (GAN) to identify the FGT region in MRI volumes and evaluate its impact on a specific clinical application. The GAN consists of an improved U-Net as a generator to generate FGT candidate areas and a patch deep convolutional neural network (DCNN) as a discriminator to evaluate the authenticity of the synthetic FGT region. The proposed method has two improvements compared to the classical U-Net: (1) the improved U-Net is designed to extract more features of the FGT region for a more accurate description of the FGT region; (2) a patch DCNN is designed for discriminating the authenticity of the FGT region generated by the improved U-Net, which makes the segmentation result more stable and accurate. A dataset of 100 three-dimensional (3D) bilateral breast MRI scans from 100 patients (aged 22–78 years) was used in this study with Institutional Review Board (IRB) approval. 3D hand-segmented FGT areas for all breasts were provided as a reference standard. Five-fold cross-validation was used in training and testing of the models. The Dice similarity coefficient (DSC) and Jaccard index (JI) values were evaluated to measure the segmentation accuracy. The previous method using classical U-Net was used as a baseline in this study. In the five partitions of the cross-validation set, the GAN achieved DSC and JI values of 87.0 ± 7.0% and 77.6 ± 10.1%, respectively, while the corresponding values obtained through by the baseline method were 81.1 ± 8.7% and 69.0 ± 11.3%, respectively. The proposed method is significantly superior to the previous method using U-Net. The FGT segmentation impacted the BPE quantification application in the following manner: the correlation coefficients between the quantified BPE value and BI-RADS BPE categories provided by the radiologist were 0.46 ± 0.15 (best: 0.63) based on GAN segmented FGT areas, while the corresponding correlation coefficients were 0.41 ± 0.16 (best: 0.60) based on baseline U-Net segmented FGT areas. BPE can be quantified better using the FGT areas segmented by the proposed GAN model than using the FGT areas segmented by the baseline U-Net.

Current and Emerging Magnetic Resonance-Based Techniques for Breast Cancer.

Breast cancer is the most commonly diagnosed cancer among women worldwide, and early detection remains a principal factor for improved patient outcomes and reduced mortality. Clinically, magnetic resonance imaging (MRI) techniques are routinely used in determining benign and malignant tumor phenotypes and for monitoring treatment outcomes. Static MRI techniques enable superior structural contrast between adipose and fibroglandular tissues, while dynamic MRI techniques can elucidate functional characteristics of malignant tumors. The preferred clinical procedure—dynamic contrast-enhanced MRI—illuminates the hypervascularity of breast tumors through a gadolinium-based contrast agent; however, accumulation of the potentially toxic contrast agent remains a major limitation of the technique, propelling MRI research toward finding an alternative, noninvasive method. Three such techniques are magnetic resonance spectroscopy, chemical exchange saturation transfer, and non-contrast diffusion weighted imaging. These methods shed light on underlying chemical composition, provide snapshots of tissue metabolism, and more pronouncedly characterize microstructural heterogeneity. This review article outlines the present state of clinical MRI for breast cancer and examines several research techniques that demonstrate capacity for clinical translation. Ultimately, multi-parametric MRI—incorporating one or more of these emerging methods—presently holds the best potential to afford improved specificity and deliver excellent accuracy to clinics for the prediction, detection, and monitoring of breast cancer.

Imaging radiation dose in breast radiotherapy by X-ray CT calibration of Cherenkov light

Imaging Cherenkov emission during radiation therapy cancer treatments can provide a real-time, non-contact sampling of the entire dose field. The emitted Cherenkov signal generated is proportional to deposited dose, however, it is affected by attenuation from the intrinsic tissue optical properties of the patient, which in breast, ranges from primarily adipose to fibroglandular tissue. Patients being treated with whole-breast X-ray radiotherapy (n = 13) were imaged for 108 total fractions, to establish correction factors from the linear relationships between Cherenkov light and CT number (HU). This study elucidates this relationship in vivo, and a correction factor approach is used to scale each image to improve the linear correlation between Cherenkov emission intensity and dose (R_{6X}^2 = 0.85,,R_{10X}^2 = 0.95). This study provides a major step towards direct quantitative radiation dose imaging in humans by utilizing non-contact camera sensing of Cherenkov emission during the radiation therapy treatment.

High-Spatial-Resolution Multishot Multiplexed Sensitivity-encoding Diffusion-weighted Imaging for Improved Quality of Breast Images and Differentiation of Breast Lesions: A Feasibility Study.

Multishot multiplexed sensitivity-encoding diffusion-weighted imaging is a feasible and easily implementable routine breast MRI protocol that yields high-quality diffusion-weighted breast images.Purpose: To compare multiplexed sensitivity-encoding (MUSE) diffusion-weighted imaging (DWI) and single-shot DWI for lesion visibility and differentiation of malignant and benign lesions within the breast.Materials and Methods: In this prospective institutional review board-approved study, both MUSE DWI and single-shot DWI sequences were first optimized in breast phantoms and then performed in a group of patients. Thirty women (mean age, 51.1 years ± 10.1 [standard deviation]; age range, 27-70 years) with 37 lesions were included in this study and underwent scanning using both techniques. Visual qualitative analysis of diffusion-weighted images was accomplished by two independent readers; images were assessed for lesion visibility, adequate fat suppression, and the presence of artifacts. Quantitative analysis was performed by calculating apparent diffusion coefficient (ADC) values and image quality parameters (signal-to-noise ratio [SNR] for lesions and fibroglandular tissue; contrast-to-noise ratio) by manually drawing regions of interest within the phantoms and breast tumor tissue. Interreader variability was determined using the Cohen κ coefficient, and quantitative differences between MUSE DWI and single-shot DWI were assessed using the Mann-Whitney U test; significance was defined at P < .05.Results: MUSE DWI yielded significantly improved image quality compared with single-shot DWI in phantoms (SNR, P = .001) and participants (lesion SNR, P = .009; fibroglandular tissue SNR, P = .05; contrast-to-noise ratio, P = .008). MUSE DWI ADC values showed a significant difference between malignant and benign lesions (P < .001). No significant differences were found between MUSE DWI and single-shot DWI in the mean, maximum, and minimum ADC values (P = .96, P = .28, and P = .49, respectively). Visual qualitative analysis resulted in better lesion visibility for MUSE DWI over single-shot DWI (κ = 0.70).Conclusion: MUSE DWI is a promising high-spatial-resolution technique that may enhance breast MRI protocols without the need for contrast material administration in breast screening.Keywords: Breast, MR-Diffusion Weighted Imaging, OncologySupplemental material is available for this article.© RSNA, 2020.

Alcohol and Tobacco Use in Relation to Mammographic Density in 23,456 Women.

Percent density (PD) is a strong risk factor for breast cancer that is potentially modifiable by lifestyle factors. PD is a composite of the dense (DA) and nondense (NDA) areas of a mammogram, representing predominantly fibroglandular or fatty tissues, respectively. Alcohol and tobacco use have been associated with increased breast cancer risk. However, their effects on mammographic density (MD) phenotypes are poorly understood. We examined associations of alcohol and tobacco use with PD, DA, and NDA in a population-based cohort of 23,456 women screened using full-field digital mammography machines manufactured by Hologic or General Electric. MD was measured using Cumulus. Machine-specific effects were estimated using linear regression, and combined using random effects meta-analysis. Alcohol use was positively associated with PD (P trend = 0.01), unassociated with DA (P trend = 0.23), and inversely associated with NDA (P trend = 0.02) adjusting for age, body mass index, reproductive factors, physical activity, and family history of breast cancer. In contrast, tobacco use was inversely associated with PD (P trend = 0.0008), unassociated with DA (P trend = 0.93), and positively associated with NDA (P trend<0.0001). These trends were stronger in normal and overweight women than in obese women. These findings suggest that associations of alcohol and tobacco use with PD result more from their associations with NDA than DA. PD and NDA may mediate the association of alcohol drinking, but not tobacco smoking, with increased breast cancer risk. Further studies are needed to elucidate the modifiable lifestyle factors that influence breast tissue composition, and the important role of the fatty tissues on breast health.

Cone-beam breast computed tomography using ultra-fast image reconstruction with constrained, total-variation minimization for suppression of artifacts

Compressed sensing based iterative reconstruction algorithms for computed tomography such as adaptive steepest descent-projection on convex sets (ASD-POCS) are attractive due to their applicability in incomplete datasets such as sparse-view data and can reduce radiation dose to the patients while preserving image quality. Although IR algorithms reduce image noise compared to analytical Feldkamp-Davis-Kress (FDK) algorithm, they may generate artifacts, particularly along the periphery of the object. One popular solution is to use finer image-grid followed by down-sampling. This approach is computationally intensive but may be compensated by reducing the field of view. Our proposed solution is to replace the algebraic reconstruction technique within the original ASD-POCS by ordered subsets-simultaneous algebraic reconstruction technique (OS-SART) and with initialization using FDK image. We refer to this method as Fast, Iterative, TV-Regularized, Statistical reconstruction Technique (FIRST). In this study, we investigate FIRST for cone-beam dedicated breast CT with large image matrix. The signal-difference to noise ratio (SDNR), the difference of the mean value and the variance of adipose and fibroglandular tissues for both FDK and FIRST reconstructions were determined. With FDK serving as the reference, the root-mean-square error (RMSE), bias, and the full-width at half-maximum (FWHM) of microcalcifications in two orthogonal directions were also computed. Our results suggest that FIRST is competitive to the finer image-grid method with shorter reconstruction time. Images reconstructed using the FIRST do not exhibit artifacts and outperformed FDK in terms of image noise. This suggests the potential of this approach for radiation dose reduction in cone-beam breast CT.

Multimodal Breast Phantoms for Microwave, Ultrasound, Mammography, Magnetic Resonance and Computed Tomography Imaging.

The aim of this work was to develop multimodal anthropomorphic breast phantoms suitable for evaluating the imaging performance of a recently-introduced Microwave Imaging (MWI) technique in comparison to the established diagnostic imaging modalities of Magnetic Resonance Imaging (MRI), Ultrasound (US), mammography and Computed Tomography (CT). MWI is an emerging technique with significant potential to supplement established imaging techniques to improve diagnostic confidence for breast cancer detection. To date, numerical simulations have been used to assess the different MWI scanning and image reconstruction algorithms in current use, while only a few clinical trials have been conducted. To bridge the gap between the numerical simulation environment and a more realistic diagnostic scenario, anthropomorphic phantoms which mimic breast tissues in terms of their heterogeneity, anatomy, morphology, and mechanical and dielectric characteristics, may be used. Key in this regard is achieving realism in the imaging appearance of the different healthy and pathologic tissue types for each of the modalities, taking into consideration the differing imaging and contrast mechanisms for each modality. Suitable phantoms can thus be used by radiologists to correlate image findings between the emerging MWI technique and the more familiar images generated by the conventional modalities. Two phantoms were developed in this study, representing difficult-to-image and easy-to-image patients: the former contained a complex boundary between the mammary fat and fibroglandular tissues, extracted from real patient MRI datasets, while the latter contained a simpler and less morphologically accurate interface. Both phantoms were otherwise identical, with tissue-mimicking materials (TMMs) developed to mimic skin, subcutaneous fat, fibroglandular tissue, tumor and pectoral muscle. The phantoms’ construction used non-toxic materials, and they were inexpensive and relatively easy to manufacture. Both phantoms were scanned using conventional modalities (MRI, US, mammography and CT) and a recently introduced MWI radar detection procedure called in-coherent Multiple Signal Classification (I-MUSIC). Clinically realistic artifact-free images of the anthropomorphic breast phantoms were obtained using the conventional imaging techniques as well as the emerging technique of MWI.