Breast Density: Comparison of Chest CT with Mammography

Salvatore, Mary M.; Margolies, Laurie; Kale, Minal; Wisnivesky, Juan P.; Kotkin, Sean; Henschke, Claudia I.; Yankelevitz, David F.

doi:10.1148/radiol.13130733

Cited by 33 publications

(24 citation statements)

References 17 publications

(18 reference statements)

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“…Full paper: Semi-automated CT measure of breast density BJR inter-reader agreement of subjective CT readings was no better than that of mammographic readings, contrary to a previous study by Salvatore et al 10 Both rely on subjective assessments of breast density percentage based on a visual analysis of a threedimensional structure in two-dimensional plane. Subjective CT readings are further limited by variable positions of the breasts, and there is subjectivity of reviewers in analysing the slice with the maximum breast tissue.…”

Section: Discussionmentioning

confidence: 69%

“…In a post-mortem study by Johnson et al, 12 using cone beam CT (CBCT) with fuzzy c-mean algorithm and involving 40 post-mortem breasts, the authors found automatic CT measures of breast density more efficient and reduced interobserver variation. Salvatore et al 10 further evaluated a subset of 40 patients with semi-automated CT density measurement and found agreement in 90% of cases. In our study, we found our semi-automated CT density measurements agreed with subjective mammographic assessments in up to 84.4% of cases, with only one level difference in the rest.…”

Section: Discussionmentioning

confidence: 99%

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How does semi-automated computer-derived CT measure of breast density compare with subjective assessments to assess mean glandular breast density, in patients with breast cancer?

Bansal

Kotugodella

2014

The British Journal of Radiology

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Cite this article as: Bansal GJ, Kotugodella S. How does semi-automated computer-derived CT measure of breast density compare with subjective assessments to assess mean glandular breast density, in patients with breast cancer? Br J Radiol 2014;87:20140530. FULL PAPER How does semi-automated computer-derived CT measure of breast density compare with subjective assessments to assess mean glandular breast density, in patients with breast cancer? Methods: A retrospective review of mammograms and CT scans in 77 patients with breast cancer obtained within 1 year of each other was performed. Two radiologists independently reviewed both CT and mammograms and classified each case into four categories as defined by the breast imaging-reporting and data system of the American College of Radiology. Inter-reader agreements were obtained for both mammographic and CT density subjective evaluations by using the Cohen-weighted kappa statistic and Spearman correlation. The semi-automated computer-derived measurement of breast density was correlated with visual measurements.Results: Inter-reader agreements were lower for subjective CT density grades than those for mammographic readings 0.428 [confidence interval (CI), 0.24-0.89] vs 0.571 (CI, 0.35-0.76). There was moderately good correlation between subjective CT density grades and the mammographic density grades for both readers (0.760 for Reader 1 and 0.913 for Reader 2). The semi-automated CT density measurement correlated well with the subjective assessments, with complete agreement of the density grades in 84.9% of patients and only one level difference in the rest. Conclusions: Semi-automated CT density measurements in the evaluation of breast density correlated well with subjective mammographic density measurement. Advances in knowledge: There is good correlation between CT and mammographic density, but further studies are needed on how to incorporate semi-automated CT breast density measurement in the risk stratification of patients.High glandular density on mammograms has been identified as a risk factor for development of breast cancer. [1][2][3] Moreover, mammograms of breasts with high glandular density have low sensitivity for diagnosing breast cancer. There has been an impetus to use methods other than traditional mammograms to evaluate breasts with high density such as ultrasound and MRI. 4,5 On the other hand, CT scans are increasingly performed in a variety of age groups for reasons other than breast symptoms. Following recent recommendations of low-dose chest CT screening for lung cancer in high-risk patients, 6 many females have had CT of the chest performed but not mammograms. We believe that CT provides a valuable resource to get reliable information with respect to breast density. If breast density can be determined accurately and is deemed to be high on CT scan, there is a case to suggest supplemental screening with other modalities in addition to mammography.The aim of this study is to evaluate (1) if semi-automated CT measurement of density is a reliabl...

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Section: Discussionmentioning

confidence: 69%

Section: Discussionmentioning

confidence: 99%

How does semi-automated computer-derived CT measure of breast density compare with subjective assessments to assess mean glandular breast density, in patients with breast cancer?

Bansal

Kotugodella

2014

The British Journal of Radiology

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“…Efforts have been made to measure breast density from clinical standard chest CT study (27), LDCT (28, 29), cone beam CT (30), and dedicated breast CT (31, 32). Among these, LDCT is used as a clinical exam for lung cancer screening and the number of performed LDCT is increasingly rapidly.…”

Section: Discussionmentioning

confidence: 99%

Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening

Chen

Chan

et al. 2016

Academic Radiology

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Rationale and Objectives Low dose chest computed tomography (LDCT), increasingly being used for screening of lung cancer, may also be used to measure breast density, which is proven as a risk factor for breast cancer. In this study we developed a segmentation method to measure quantitative breast density on CT images and correlated with MR density. Materials and Methods Forty healthy females receiving both LDCT and breast magnetic resonance imaging (MRI) were studied. A semi-automatic method was applied to quantify the breast density on LDCT images. The intra-, inter-operator reproducibility was evaluated. The volumetric density on MRI was obtained by using a well-established automatic template-based segmentation method. The breast volume (BV), fibroglandular tissue volume (FV), and percent breast density (PD) measured on LDCT and MRI were compared. Results The measurements of BV, FV, and PD on LDCT images yields highly consistent results, with the intraclass correlation coefficient (ICC) of 0.999 for BV, 0.977 for FV, and 0.966 for PD for intra-operator reproducibility; and ICC of 0.953 for BV, 0.974 for FV, and 0.973 for PD for inter-operator reproducibility. The BV, FV, and PD measured on LDCT and MRI were well correlated (all r≥0.90). Bland-Altman plots showed that a larger BV and FV were measured on LDCT compared with MRI. Conclusions The preliminary results showed that quantitative breast density can be measured from LDCT, and that our segmentation method could yield a high reproducibility on the measured volume and percent breast density. The results measured on LDCT and MRI were highly correlated. Our results showed that LDCT may provide valuable information about breast density for evaluating breast cancer risk.

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“…The separation between B and the posterior muscles M P as illustrated in Figure 4 (c); (2). The separation between B and the anterior muscles M A as illustrated in Figure 4 (d).…”

Section: Separation From the Posterior Muscles M Pmentioning

confidence: 97%

“…Anterior muscles M A lying on the thoracic wall such as pectoralis major muscle and intercostal muscles; (2). Posterior muscles M P attached to scapula such as latissimus dorsi muscle and teres major muscle.…”

Section: Algorithmmentioning

confidence: 99%

Segmentation of the whole breast from low-dose chest CT images

et al. 2015

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The segmentation of whole breast serves as the first step towards automated breast lesion detection. It is also necessary for automatically assessing the breast density, which is considered to be an important risk factor for breast cancer. In this paper we present a fully automated algorithm to segment the whole breast in low-dose chest CT images (LDCT), which has been recommended as an annual lung cancer screening test. The automated whole breast segmentation and potential breast density readings as well as lesion detection in LDCT will provide useful information for women who have received LDCT screening, especially the ones who have not undergone mammographic screening, by providing them additional risk indicators for breast cancer with no additional radiation exposure. The two main challenges to be addressed are significant range of variations in terms of the shape and location of the breast in LDCT and the separation of pectoral muscles from the glandular tissues. The presented algorithm achieves robust whole breast segmentation using an anatomy directed rule-based method. The evaluation is performed on 20 LDCT scans by comparing the segmentation with ground truth manually annotated by a radiologist on one axial slice and two sagittal slices for each scan. The resulting average Dice coefficient is 0.880 with a standard deviation of 0.058, demonstrating that the automated segmentation algorithm achieves results consistent with manual annotations of a radiologist.

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Breast Density: Comparison of Chest CT with Mammography

Abstract: Preliminary results suggest that on further validation, breast density readings at CT may provide important additional risk information on CT of the chest and that computer-derived measurements may be helpful in such assessment.

Cited by 33 publications

References 17 publications

How does semi-automated computer-derived CT measure of breast density compare with subjective assessments to assess mean glandular breast density, in patients with breast cancer?

How does semi-automated computer-derived CT measure of breast density compare with subjective assessments to assess mean glandular breast density, in patients with breast cancer?

Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening

Segmentation of the whole breast from low-dose chest CT images

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