Dataset Information

The compressed breast during mammography and breast tomosynthesis: in vivo shape characterization and modeling.

ABSTRACT: To characterize and develop a patient-based 3D model of the compressed breast undergoing mammography and breast tomosynthesis. During this IRB-approved, HIPAA-compliant study, 50 women were recruited to undergo 3D breast surface imaging with structured light (SL) during breast compression, along with simultaneous acquisition of a tomosynthesis image. A pair of SL systems were used to acquire 3D surface images by projecting 24 different patterns onto the compressed breast and capturing their reflection off the breast surface in approximately 12-16?s. The 3D surface was characterized and modeled via principal component analysis. The resulting surface model was combined with a previously developed 2D model of projected compressed breast shapes to generate a full 3D model. Data from ten patients were discarded due to technical problems during image acquisition. The maximum breast thickness (found at the chest-wall) had an average value of 56?mm, and decreased 13% towards the nipple (breast tilt angle of 5.2°). The portion of the breast not in contact with the compression paddle or the support table extended on average 17?mm, 18% of the chest-wall to nipple distance. The outermost point along the breast surface lies below the midline of the total thickness. A complete 3D model of compressed breast shapes was created and implemented as a software application available for download, capable of generating new random realistic 3D shapes of breasts undergoing compression. Accurate characterization and modeling of the breast curvature and shape was achieved and will be used for various image processing and clinical tasks.

SUBMITTER: Rodriguez-Ruiz A

PROVIDER: S-EPMC5727349 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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The compressed breast during mammography and breast tomosynthesis: in vivo shape characterization and modeling.

Rodríguez-Ruiz Alejandro A Agasthya Greeshma A GA Sechopoulos Ioannis I

Physics in medicine and biology 20170807 17

To characterize and develop a patient-based 3D model of the compressed breast undergoing mammography and breast tomosynthesis. During this IRB-approved, HIPAA-compliant study, 50 women were recruited to undergo 3D breast surface imaging with structured light (SL) during breast compression, along with simultaneous acquisition of a tomosynthesis image. A pair of SL systems were used to acquire 3D surface images by projecting 24 different patterns onto the compressed breast and capturing their refl ...[more]

PMID: 28665291

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Project description:To develop a set of accurate 2D models of compressed breasts undergoing mammography or breast tomosynthesis, based on objective analysis, to accurately characterize mammograms with few linearly independent parameters, and to generate novel clinically realistic paired cranio-caudal (CC) and medio-lateral oblique (MLO) views of the breast.We seek to improve on an existing model of compressed breasts by overcoming detector size bias, removing the nipple and non-mammary tissue, pairing the CC and MLO views from a single breast, and incorporating the pectoralis major muscle contour into the model. The outer breast shapes in 931 paired CC and MLO mammograms were automatically detected with an in-house developed segmentation algorithm. From these shapes three generic models (CC-only, MLO-only, and joint CC/MLO) with linearly independent components were constructed via principal component analysis (PCA). The ability of the models to represent mammograms not used for PCA was tested via leave-one-out cross-validation, by measuring the average distance error (ADE).The individual models based on six components were found to depict breast shapes with accuracy (mean ADE-CC = 0.81 mm, ADE-MLO = 1.64 mm, ADE-Pectoralis = 1.61 mm), outperforming the joint CC/MLO model (P ? 0.001). The joint model based on 12 principal components contains 99.5% of the total variance of the data, and can be used to generate new clinically realistic paired CC and MLO breast shapes. This is achieved by generating random sets of 12 principal components, following the Gaussian distributions of the histograms of each component, which were obtained from the component values determined from the images in the mammography database used.Our joint CC/MLO model can successfully generate paired CC and MLO view shapes of the same simulated breast, while the individual models can be used to represent with high accuracy clinical acquired mammograms with a small set of parameters. This is the first step toward objective 3D compressed breast models, useful for dosimetry and scatter correction research, among other applications.

Project description:BackgroundOur objective was to perform a systematic review and meta-analysis comparing the breast cancer detection rate (CDR), invasive CDR, recall rate, and positive predictive value 1 (PPV1) of digital mammography (DM) alone, combined digital breast tomosynthesis (DBT) and DM, combined DBT and synthetic 2-dimensional mammography (S2D), and DBT alone.MethodsMEDLINE and Embase were searched until April 2020 to identify comparative design studies reporting on patients undergoing routine breast cancer screening. Random effects model proportional meta-analyses estimated CDR, invasive CDR, recall rate, and PPV1. Meta-regression modeling was used to compare imaging modalities. All statistical tests were 2-sided.ResultsForty-two studies reporting on 2 606 296 patients (13 003 breast cancer cases) were included. CDR was highest in combined DBT and DM (6.36 per 1000 screened, 95% confidence interval [CI] = 5.62 to 7.14, P < .001), and combined DBT and S2D (7.40 per 1000 screened, 95% CI = 6.49 to 8.37, P < .001) compared with DM alone (4.68 per 1000 screened, 95% CI = 4.28 to 5.11). Invasive CDR was highest in combined DBT and DM (4.53 per 1000 screened, 95% CI = 3.97 to 5.12, P = .003) and combined DBT and S2D (5.68 per 1000 screened, 95% CI = 4.43 to 7.09, P < .001) compared with DM alone (3.42 per 1000 screened, 95% CI = 3.02 to 3.83). Recall rate was lowest in combined DBT and S2D (42.3 per 1000 screened, 95% CI = 37.4 to 60.4, P<.001). PPV1 was highest in combined DBT and DM (10.0%, 95% CI = 8.0% to 12.0%, P = .004), and combined DBT and S2D (16.0%, 95% CI = 10.0% to 23.0%, P < .001), whereas no difference was detected for DBT alone (7.0%, 95% CI = 6.0% to 8.0%, P = .75) compared with DM alone (7.0%, 95.0% CI = 5.0% to 8.0%).ConclusionsOur findings provide evidence on key performance metrics for DM, DBT alone, combined DBT and DM, and combined DBT and S2D, which may inform optimal application of these modalities for breast cancer screening.

Project description:To develop models of compressed breasts undergoing mammography based on objective analysis, that are capable of accurately representing breast shapes in acquired clinical images and generating new, clinically realistic shapes.An automated edge detection algorithm was used to catalogue the breast shapes of clinically acquired cranio-caudal (CC) and medio-lateral oblique (MLO) view mammograms from a large database of digital mammography images. Principal component analysis (PCA) was performed on these shapes to reduce the information contained within the shapes to a small number of linearly independent variables. The breast shape models, one of each view, were developed from the identified principal components, and their ability to reproduce the shape of breasts from an independent set of mammograms not used in the PCA, was assessed both visually and quantitatively by calculating the average distance error (ADE).The PCA breast shape models of the CC and MLO mammographic views based on six principal components, in which 99.2% and 98.0%, respectively, of the total variance of the dataset is contained, were found to be able to reproduce breast shapes with strong fidelity (CC view mean ADE = 0.90 mm, MLO view mean ADE = 1.43 mm) and to generate new clinically realistic shapes. The PCA models based on fewer principal components were also successful, but to a lesser degree, as the two-component model exhibited a mean ADE = 2.99 mm for the CC view, and a mean ADE = 4.63 mm for the MLO view. The four-component models exhibited a mean ADE = 1.47 mm for the CC view and a mean ADE = 2.14 mm for the MLO view. Paired t-tests of the ADE values of each image between models showed that these differences were statistically significant (max p-value = 0.0247). Visual examination of modeled breast shapes confirmed these results. Histograms of the PCA parameters associated with the six principal components were fitted with Gaussian distributions. The six-component model was also used to generate CC and MLO view mammogram breast shapes, using the mean PCA parameter values of these distributions and randomly generated values based on the fitted Gaussian distributions, which resemble clinically encountered breasts. A spreadsheet with the data necessary to apply this model is provided as the supplementary material.Our PCA models of breast shapes in both mammographic views successfully reproduce analyzed breast shapes and generate new clinically relevant shapes. This work can aid in research applications which incorporate breast shape modeling, such as x-ray scatter correction, dosimetry, and image registration.

Project description:Background Breast Imaging Reporting and Data System (BI-RADS) breast density categories assigned by interpreting radiologists often influence decisions surrounding supplemental breast cancer screening and risk assessment. The landscape of mammographic screening continuously evolves, and different mammographic screening modalities may result in different perception of density, reflected in different assignment of BI-RADS density categories. Purpose To investigate the effect of screening mammography modality on BI-RADS breast density assessments. Materials and Methods Data were retrospectively analyzed from 24 736 individual women (42.3% [10 455 of 24 736] white women, 57.7% [14 281 of 24 736] black women; mean age, 56.3 years; age range, 40.0-74.9 years) who underwent from one to seven mammographic screening examinations from September 2010 through February 2017 (60 766 examinations). Three screening modalities were used: digital mammography alone (8935 examinations); digital mammography with digital breast tomosynthesis (DBT; 30 779 examinations); and synthetic mammography with DBT (21 052 examinations). Random-effects logistic regression analysis was performed to estimate the likelihood of assignment to high versus low BI-RADS density category according to each modality, adjusted for ethnicity, age, body mass index (BMI), and radiologist. The interactions of modality with ethnicity and BMI on density categorization were also tested with the model. Results Women screened with DBT versus digital mammography alone had lower likelihood regarding categorization of high density breasts (digital mammography and DBT vs digital mammography: odds ratio, 0.69 [95% confidence interval: 0.61, 0.80], P < .001; synthetic mammography and DBT vs digital mammography: odds ratio, 0.43 [95% confidence interval: 0.37, 0.50], P < .001). Lower likelihood of high density was also observed at synthetic mammography and DBT compared with digital mammography and DBT (odds ratio, 0.62; 95% confidence interval: 0.56, 0.69; P < .001). There were interactions of modality with ethnicity (P = .007) and BMI (P = .003) on breast density assessment, with greater differences in density categorization according to modality observed for black women than for white women and groups with higher BMI. Conclusion Breast density categorization may vary by screening mammographic modality, and this effect appears to vary by ethnicity and body mass index. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

Project description:ImportanceMany US radiologists have screening mammography recall rates above the expert-recommended threshold of 12%. The influence of digital breast tomosynthesis (DBT) on the distribution of radiologist recall rates is uncertain.ObjectiveTo evaluate radiologists' recall and cancer detection rates before and after beginning interpretation of DBT examinations.Design, setting, and participantsThis cohort study included 198 radiologists from 104 radiology facilities in the Breast Cancer Surveillance Consortium who interpreted 251?384 DBT and 2?000?681 digital mammography (DM) screening examinations from 2009 to 2017, including 126 radiologists (63.6%) who interpreted DBT examinations during the study period and 72 (36.4%) who exclusively interpreted DM examinations (to adjust for secular trends). Data were analyzed from April 2018 to July 2019.ExposuresDigital breast tomosynthesis and DM screening examinations.Main outcomes and measuresRecall rate and cancer detection rate.ResultsA total of 198 radiologists interpreted 2?252?065 DM and DBT examinations (2?000?681 [88.8%] DM examinations; 251?384 [11.2%] DBT examinations; 710?934 patients [31.6%] aged 50-59 years; 1?448?981 [64.3%] non-Hispanic white). Among the 126 radiologists (63.6%) who interpreted DBT examinations, 83 (65.9%) had unadjusted DM recall rates of no more than 12% before using DBT, with a median (interquartile range) recall rate of 10.0% (7.5%-13.0%). On DBT examinations, 96 (76.2%) had an unadjusted recall rate of no more than 12%, with a median (interquartile range) recall rate of 8.8% (6.3%-11.3%). A secular trend in recall rate was observed, with the multivariable-adjusted risk of recall on screening examinations declining by 1.2% (95% CI, 0.9%-1.5%) per year. After adjusting for examination characteristics and secular trends, recall rates were 15% lower on DBT examinations compared with DM examinations interpreted before DBT use (relative risk, 0.85; 95% CI, 0.83-0.87). Adjusted recall rates were significantly lower on DBT examinations compared with DM examinations interpreted before DBT use for 45 radiologists (35.7%) and significantly higher for 18 (14.3%); 63 (50.0%) had no statistically significant change. The unadjusted cancer detection rate on DBT was 5.3 per 1000 examinations (95% CI, 5.0-5.7 per 1000 examinations) compared with 4.7 per 1000 examinations (95% CI, 4.6-4.8 per 1000 examinations) on DM examinations interpreted before DM use (multivariable-adjusted risk ratio, 1.21; 95% CI, 1.11-1.33).Conclusions and relevanceIn this study, DBT was associated with an overall decrease in recall rate and an increase in cancer detection rate. However, our results indicated that there is wide variability among radiologists, including a subset of radiologists who experienced increased recall rates on DBT examinations. Radiology practices should audit radiologist DBT screening performance and consider additional DBT training for radiologists whose performance does not improve as expected.

Project description:BackgroundDigital breast tomosynthesis (DBT) combined with digital mammography (DM) is increasingly used in the United States instead of DM alone for breast cancer screening. Early screening outcomes incorporating synthetic mammography (SM) with DBT have suggested that SM is an acceptable non-radiation dose alternative to DM.PurposeTo compare multicenter outcomes from breast cancer screening with SM/DBT versus DM/DBT.Materials and MethodsThis was a retrospective study of consecutive screening mammograms obtained at two institutions. Eligible studies consisted of 34 106 DM/DBT examinations between October 3, 2011, and October 31, 2014, and 34 180 SM/DBT examinations between January 7, 2015, and February 2, 2018, at the University of Pennsylvania and 51 148 DM/DBT examinations between January 1, 2012, and May 31, 2016, and 31 929 SM/DBT examinations between June 1, 2016, and March 30, 2018, at the University of Vermont. Demographics of women who attended screening and results from screening were recorded. Recall rate, biopsy rate, false-negative rate, cancer detection rate, positive predictive value, sensitivity, and specificity were calculated according to modality and institution. Descriptive statistics, χ2 tests, and logistic regression were used in analysis.ResultsThe study included 151 363 screening examinations among 151 363 women (mean age, 58.1 years ± 10.9 [standard deviation]). The unadjusted recall rate was lower with SM/DBT than with DM/DBT (7.0% [4630 of 66 109 examinations] for SM/DBT vs 7.9% [6742 of 85 254 examinations] for DM/DBT; P < .01). However, after multivariable adjustment, SM/DBT was associated with a slightly higher recall rate compared with DM/DBT (adjusted odds ratio [OR], 1.06; adjusted 95% CI: 1.01, 1.11; P = .02). Similarly, after multivariable adjustment, SM/DBT was associated with slightly lower specificity compared with DM/DBT (adjusted OR, 0.95; adjusted 95% CI: 0.90, 0.99; P = .02). There was no statistically significant difference in biopsy rate (P = .54), false-negative rate (P = .38), cancer detection rate (P = .55), invasive or in situ cancer detection rate (P = .52 and P = .98, respectively), positive predictive value (P = .78), or sensitivity (P = .33) for SM/DBT versus DM/DBT overall or within either institution (P > .05 for all).ConclusionBreast cancer screening performance is maintained within benchmarks when synthetic mammography replaces digital mammography in digital breast tomosynthesis imaging.© RSNA, 2020Online supplemental material is available for this article.See also the editorial by Lång in this issue.

Dataset Information

The compressed breast during mammography and breast tomosynthesis: in vivo shape characterization and modeling.

Publications

The compressed breast during mammography and breast tomosynthesis: in vivo shape characterization and modeling.

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