scholarly journals Whole Breast Sound Speed Measurement from US Tomography Correlates Strongly with Volumetric Breast Density from Mammography

2020 ◽  
Vol 2 (5) ◽  
pp. 443-451
Author(s):  
Mark Sak ◽  
Peter Littrup ◽  
Rachel Brem ◽  
Neb Duric
Keyword(s):  
Breast Density ◽  
Sound Speed ◽  
Three Dimensional ◽  
Correlation Coefficients ◽  
Mammographic Breast Density ◽  
Data Sets ◽  
Imaging Data ◽  
Institutional Review ◽  
Dense Tissue ◽  
Screening Mammograms

Abstract Objective To assess the feasibility of using tissue sound speed as a quantitative marker of breast density. Methods This study was carried out under an Institutional Review Board–approved protocol (written consent required). Imaging data were selected retrospectively based on the availability of US tomography (UST) exams, screening mammograms with volumetric breast density data, patient age of 18 to 80 years, and weight less than 300 lbs. Sound speed images from the UST exams were used to measure the volume of dense tissue, the volume averaged sound speed (VASS), and the percent of high sound speed tissue (PHSST). The mammographic breast density and volume of dense tissue were estimated with three-dimensional (3D) software. Differences in volumes were assessed with paired t-tests. Spearman correlation coefficients were calculated to determine the strength of the correlations between the mammographic and UST assessments of breast density. Results A total of 100 UST and 3D mammographic data sets met the selection criteria. The resulting measurements showed that UST measured a more than 2-fold larger volume of dense tissue compared to mammography. The differences were statistically significant (P < 0.001). A strong correlation of rS = 0.85 (95% CI: 0.79–0.90) between 3D mammographic breast density (BD) and the VASS was noted. This correlation is significantly stronger than those reported in previous two-dimensional studies (rS = 0.85 vs rS = 0.71). A similar correlation was found for PHSST and mammographic BD with rS = 0.86 (95% CI: 0.80–0.90). Conclusion The strong correlations between UST parameters and 3D mammographic BD suggest that breast sound speed should be further studied as a potential new marker for inclusion in clinical risk models.

Human aorta: preliminary results with virtual endoscopy based on three-dimensional MR imaging data sets.

Radiology ◽  
1996 ◽  
Vol 199 (1) ◽  
pp. 37-40 ◽  
Author(s):  
C P Davis ◽  
M E Ladd ◽  
B J Romanowski ◽  
S Wildermuth ◽  
J F Knoplioch ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Three Dimensional ◽  
Virtual Endoscopy ◽  
Human Aorta ◽  
Data Sets ◽  
Imaging Data ◽  
Preliminary Results

Impact of a deep learning model to reduce variation and costs of federally mandated breast density legislation.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1550-1550
Author(s):  
Katherine Cavallo Hom ◽  
Brian Nicholas Dontchos ◽  
Sarah Mercaldo ◽  
Pragya Dang ◽  
Leslie Lamb ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Practice ◽  
Breast Density ◽  
Routine Clinical Practice ◽  
Dense Breast ◽  
Wilcoxon Test ◽  
Screening Tests ◽  
Mammographic Breast Density ◽  
Community Practice ◽  
Screening Mammograms

1550 Background: Dense breast tissue is an independent risk factor for malignancy and can mask cancers on mammography. Yet, radiologist-assessed mammographic breast density is subjective and varies widely between and within radiologists. Our deep learning (DL) model was implemented into routine clinical practice at an academic breast imaging center and was externally validated at a separate community practice, with both sites demonstrating high clinical acceptance of the model’s density predictions. The aim of this study is to demonstrate the influence our DL model has on prospective radiologist density assessments in routine clinical practice. Methods: This IRB-approved, HIPAA-compliant retrospective study identified consecutive screening mammograms without exclusion performed across three clinical sites, over two time periods: pre-DL model implementation (January 1, 2017 through September 30, 2017) and post-DL model implementation (January 1, 2019 through September 30, 2019). Clinical sites were as follows: Site A (the academic practice where the DL model was developed and was implemented in late 2017); Site B (an affiliated community practice which implemented the DL model in late 2017 and was used for external validation); and Site C (an affiliated community practice which was never exposed to the DL model). Patient demographics and radiologist-assessed mammographic breast densities were compared over time and across sites. Patient characteristics were evaluated using Wilcoxon test and Pearson’s chi-squared test. Multivariable logistic regression models evaluated the odds of a dense breast classification as a function of time period (pre-DL vs post-DL), race (White vs non-White) and site. Results: A total of 85,865 consecutive screening mammograms across the three clinical sites were identified. After controlling for age and race, adjusted odds ratios (aOR) of a mammogram being classified as dense at Site C compared to Site B before the DL model was implemented was 2.01 (95% CI 1.873, 2.157, p<0.001). This increased to 2.827 (95% CI 2.636, 3.032, p< 0.001) after DL implementation. The aOR of a mammogram being classified as dense at Site A after implementation compared to before implementation was 0.924 (95% CI 0.885, 0.964, p<0.001). Conclusions: Our findings suggest implementation of the DL model influences radiologist’s prospective density assessments in routine clinical practice by reducing the odds of a screening exam being categorized as dense. As a result, clinical use of our model could reduce downstream costs of supplemental screening tests and limit unnecessary high-risk clinic evaluations.[Table: see text]

scholarly journals Layered manufacturing for medical imaging data

2019 ◽  
Vol 11 (8) ◽  
pp. 168781401987139
Author(s):  
Shyh-Kuang Ueng ◽  
Hsin-Cheng Huang ◽  
Chieh-Shih Chou ◽  
Hsuan-Kai Huang
Keyword(s):  
Medical Image ◽  
Three Dimensional ◽  
Image Data ◽  
Target Object ◽  
Layered Manufacturing ◽  
Boundary Representation ◽  
Data Sets ◽  
Imaging Data ◽  
Distance Field ◽  
Dimensional Image

Layered manufacturing techniques have been successfully employed to construct scanned objects from three-dimensional medical image data sets. The printed physical models are useful tools for anatomical exploration, surgical planning, teaching, and related medical applications. Before fabricating scanned objects, we have to first build watertight geometrical representations of the target objects from medical image data sets. Many algorithms had been developed to fulfill this duty. However, some of these methods require extra efforts to resolve ambiguity problems and to fix broken surfaces. Other methods cannot generate legitimate models for layered manufacturing. To alleviate these problems, this article presents a modeling procedure to efficiently create geometrical representations of objects from computerized tomography scan and magnetic resonance imaging data sets. The proposed procedure extracts the iso-surface of the target object from the input data set at the first step. Then it converts the iso-surface into a three-dimensional image and filters this three-dimensional image using morphological operators to remove dangling parts and noises. At the next step, a distance field is computed in the three-dimensional image space to approximate the surface of the target object. Then the proposed procedure smooths the distance field to soothe sharp corners and edges of the target object. Finally, a boundary representation is built from the distance field to model the target object. Compared with conventional modeling techniques, the proposed method possesses the following advantages: (1) it reduces human efforts involved in the geometrical modeling process. (2) It can construct both solid and hollow models for the target object, and wall thickness of the hollow models is adjustable. (3) The resultant boundary representation guarantees to form a watertight solid geometry, which is printable using three-dimensional printers. (4) The proposed procedure allows users to tune the precision of the geometrical model to compromise with the available computational resources.

scholarly journals Development of a Quantitative Estimation of Mammographically Breast Density

2017 ◽  
Vol 18 (1) ◽  
pp. 16-20
Author(s):  
Meherun Nahar ◽  
Abdus Sattar Mollah ◽  
Mir Mohammad Akramuzzaman
Keyword(s):  
Breast Cancer ◽  
Mammographic Density ◽  
Breast Density ◽  
Quantitative Estimation ◽  
Mammographic Breast Density ◽  
Computer Assisted ◽  
Image Analyzer ◽  
Oblique View ◽  
Language Version ◽  
Dense Tissue

Objective: Increased mammographic breast density is a moderate independent risk factor for breast cancer. Assessment of breast density may become useful in risk assessment and prevention decisions. To evaluate the association between mammographic density and breast cancer risk, a simple observer-assisted technique called interactive thresholding was developed.Methods: For providing, a quantitative estimation of mammographically dense tissue, in this study computer assisted measurements were carried out using Adobe AIR software. For thresholding technique, software named ‘Xray Image Analyzer’ was programmed in Adobe AIR language version - Action script 3.0. runtime version- Flash player 9, AIR 1.0, and flash Lite-4. Interactive thresholding technique was applied to digitized film screen mammograms, which assesses the proportion of radio graphically dense tissue in the mammographic image representing mammographic density. The technique evaluated for 36 mammograms of 18 women who underwent referral mammography in a hospital at Dhaka city from October 2010 to October 2011.Results: The women in the selected group were in age range of 20 to 60 years, with a mean age of 44±9 and median age is 45 yrs. The technique was found to be very reliable with an intra-class correlation coefficient between observers typically R = 0.887. This technique may have a role in routine mammographic analysis for the purpose of assessing risk categories and as a tool in studies of the etiology of breast cancer, in particular for monitoring changes in breast parenchyma during potential preventive interventions. Conclusion: It is possible to use the interactive segmentation technique for other projections of the breast, such as the medio-lateral oblique view. In this case, however, it is necessary to perform a manual segmentation to remove the image of the pectoral muscle from the analysis. This technique can be employ as a tool in many clinical studies.Bangladesh J. Nuclear Med. 18(1): 16-20, January 2015

scholarly journals Three-Dimensional Printing Applications in Percutaneous Structural Heart Interventions

2019 ◽  
Vol 12 (10) ◽  
Author(s):  
Serge C. Harb ◽  
Leonardo L. Rodriguez ◽  
Marija Vukicevic ◽  
Samir R. Kapadia ◽  
Stephen H. Little
Keyword(s):  
Three Dimensional ◽  
Data Sets ◽  
Structural Interventions ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Volumetric Imaging ◽  
Three Dimensional Printing ◽  
3 Dimensional ◽  
Procedural Planning ◽  
Dimensional Printing

Cardiovascular 3-dimensional printing refers to the fabrication of patients’ specific cardiac anatomic replicas based on volumetric imaging data sets obtained by echocardiography, computed tomography, or magnetic resonance imaging. It enables advanced visualization and enhanced anatomic and sometimes hemodynamic understanding and also improves procedural planning and allows interventional simulation. Also, it is helpful in communication with patients and trainees. These key advantages have led to its broad use in the field of cardiology ranging from congenital to vascular and valvular disease, particularly in structural heart interventions, where many emerging technologies are being developed and tested. This review summarizes the process of 3-dimensional printing and the workflow from imaging acquisition to model generation and discusses the cardiac applications of 3-dimensional printing focusing on its use in percutaneous structural interventions, where procedural planning now commonly relies on 3-dimensional printed models.

Classification of normal screening mammograms is strongly influenced by perceived mammographic breast density

2017 ◽  
Vol 61 (4) ◽  
pp. 461-469 ◽  
Author(s):  
Zoey ZY Ang ◽  
Mohammad A Rawashdeh ◽  
Rob Heard ◽  
Patrick C Brennan ◽  
Warwick Lee ◽  
...  
Keyword(s):  
Breast Density ◽  
Mammographic Breast Density ◽  
Screening Mammograms

scholarly journals The Fat-glandular Interface and Breast Tumor Locations: Appearances on Ultrasound Tomography Are Supported by Quantitative Peritumoral Analyses

2021 ◽  
Author(s):  
Peter J Littrup ◽  
Nebojsa Duric ◽  
Mark Sak ◽  
Cuiping Li ◽  
Olivier Roy ◽  
...  
Keyword(s):  
Sound Speed ◽  
Tumor Location ◽  
Breast Cancers ◽  
Ultrasound Tomography ◽  
Breast Masses ◽  
Fibroglandular Tissue ◽  
Institutional Review ◽  
Chi Squared ◽  
Malignant Breast ◽  
Dense Tissue

Abstract Objective To analyze the preferred tissue locations of common breast masses in relation to anatomic quadrants and the fat-glandular interface (FGI) using ultrasound tomography (UST). Methods Ultrasound tomography scanning was performed in 206 consecutive women with 298 mammographically and/or sonographically visible, benign and malignant breast masses following written informed consent to participate in an 8-site multicenter, Institutional Review Board-approved cohort study. Mass locations were categorized by their anatomic breast quadrant and the FGI, which was defined by UST as the high-contrast circumferential junction of fat and fibroglandular tissue on coronal sound speed imaging. Quantitative UST mass comparisons were done for each tumor and peritumoral region using mean sound speed and percentage of fibroglandular tissue. Chi-squared and analysis of variance tests were used to assess differences. Results Cancers were noted at the FGI in 95% (74/78) compared to 51% (98/194) of fibroadenomas and cysts combined (P &lt; 0.001). No intra-quadrant differences between cancer and benign masses were noted for tumor location by anatomic quadrants (P = 0.66). Quantitative peritumoral sound speed properties showed that cancers were surrounded by lower mean sound speeds (1477 m/s) and percent fibroglandular tissue (47%), compared to fibroadenomas (1496 m/s; 65.3%) and cysts (1518 m/s; 84%) (P &lt; 0.001; P &lt; 0.001, respectively). Conclusion Breast cancers form adjacent to fat and UST localized the vast majority to the FGI, while cysts were most often completely surrounded by dense tissue. These observations were supported by quantitative peritumoral analyses of sound speed values for fat and fibroglandular tissue.

scholarly journals Mammographic Breast Density: it’s Role in Tumor Size Assessment with Imaging Techniques

2019 ◽  
Vol 4 (1) ◽  
Keyword(s):  
Linear Regression ◽  
Breast Density ◽  
Tumor Size ◽  
Breast Imaging ◽  
Imaging Techniques ◽  
Automatic Measurement ◽  
Mammographic Breast Density ◽  
Size Estimation ◽  
Imaging Data ◽  
Mean Size

Aims: To study the visual and automatic measurement of mammographic breast density (MBD) and its implications in tumor size assessment using distinct imaging techniques. Methods: Retrospective, observational study of the visual and automatic measurement of mammographic breast density according to the breast imaging data system (BI-RADS) in 212 patients with invasive unifocal breast cancer, excluding microinvasive lesions, who did not receive neoadjuvant chemotherapy. Tumor size assessment is compared using a linear regression according pathologic size with mammographic, US and MR size. The influence of MBD in each technique of pathologic size was seen by Bland-Altman plot. Results: Patient’s mean age was 55, 7±9.9 year-old. The mean size of the lesion stablished by mammography was 16.8± 10.4 (4 -70) mm, by US was 13.6±7.2 (5 – 55) mm and by MR 17.2 ±9.9 (5 – 66) mm. Mean pathologic size was 12.6 ±8.1 (0.3 – 55) mm. Automatic MBD mean was 25.2±16.78. BIRAD assessment with visual and automatic MBD measurements were correlated with a tendency of tumor size overestimation with visual method. Linear regression of tumor size according image techniques with pathologic size showed an adjusted r-square of 27.3% for mammography, 41.8% for US and 51.7% for MR. The best correlation was seen with MR although has a tendency to overestimate tumor size. Only tumor size assessed by mammography was influenced by MBD. With this technique, tumor size was best adjusted for those breasts with lower MBD. Conclusion: Visual measurement overestimates MBD versus automatic measurement according BIRADS categories. MR is the more accurate breast imaging technique for assessing tumor size independently of the BMD, which only influences in the mammographic tumor size estimation.

The effects of hormonal replacement therapy (HRT) on mammographic breast density and abnormal mammograms prompting further investigation.

2014 ◽  
Vol 32 (26_suppl) ◽  
pp. 8-8
Author(s):  
Liran Barda ◽  
Avinoam Nevler ◽  
Esther L Shabtai ◽  
Mordechai Gutman ◽  
Moshe Shabtai
Keyword(s):  
Breast Cancer ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Lower Sensitivity ◽  
Grade Scale ◽  
Risk Of Malignancy ◽  
History Of ◽  
Malignant Lesions ◽  
Screening Mammograms ◽  
The Relationship

8 Background: Mammographic density has been associated with higher risk of breast cancer and lower sensitivity. HRT has been implicated with increased density and is also a risk factor. The relationship between HRT, breast density, and mammographic findings requiring investigation has not been fully investigated. We aimed at analyzing this correlation. Methods: 2,758 consecutive, single-center screening mammograms performed during 1 year were analyzed. Mammograms were supplemented by ultrasound. Density was measured by a semiquantitative, 5-grade scale, and grouped into low (1-3) (LDM) and high density (4-5) (HDM). Demographic and obstetric data, personal and family history of breast cancer, and the use of HRT were entered into database. These parameters were correlated with breast density and any abnormality detected. Univariate and multivariate analysis as well as multivariate logistic regression were performed on SAS 9.2. Results: Mean overall age was 48 (SD = 10.8, range 27-78), mean ages of LDM and HDM groups were 59 ± 10.5 and 50.9 ± 9.3 respectively (p = 0.001). Of 2,758 tests, 2,094 (76%) were LDM and 664 (24%) were HDM. 1,962 women (71%) were postmenopausal and 592 (30%) were on HRT. A difference in density between pre- and postmenopausal women was observed (p = 0.0001). HRT was not associated with higher rate of HDM (18%, n = 105/582) vs.15% n = 211/1370 (p = n.s) without HRT. Abnormality was more likely in postmenopausal HRT-less (52% n = 711/1370) vs. (30% n = 226/582) HRT (p = 0.0001) including solid lump (p = 0.0001), tissue irregularity (p = 0.016) and calcifications (p = 0.0005). Menopause was associated with 48% of any finding vs. 41.4% in pre-menopause women (p = 0.0017). 104 malignant lesions were found in 267 with mammographic findings prompting histological assessment. HRT was associated with lower incidence (28%) of malignancy compared to 50% without HRT. Conclusions: HRT was not associated with increased density nor with higher risk of malignancy; moreover, a lower rate of mammographic abnormality was noted. Albeit further studies are required, the results of this study do not support the notion that HRT increases the likelihood of malignancy or affects breast density.

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