Parenchymal Texture Analysis in Digital Breast Tomosynthesis for Breast Cancer Risk Estimation

Kontos, Despina; Bakić, Predrag R.; Carton, Ann Katherine; Troxel, Andrea B.; Conant, Emily F.; Maidment, Andrew D. A.

doi:10.1016/j.acra.2008.08.014

Cited by 63 publications

(39 citation statements)

References 64 publications

(95 reference statements)

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“…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. Similar results to ours were found in other studies involving three-dimensional imaging techniques such as tomosynthesis 13 and dedicated breast CT, 14 which had better inter-reader variation than twodimensional techniques.…”

Section: Discussionsupporting

confidence: 81%

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: Discussionsupporting

confidence: 81%

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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“…20,50,52 Cumulus has shown a strong positive correlation between parenchymal texture features and breast density with DBT (r 5 0.73); 50 such texture features were shown to be more related to mammographic breast density with DBT than with DM. Cumulus estimated mammographic density values are lower on DBT than on DM; 20,50,52 however, the software has been shown to overestimate breast density by 3% with DBT relative to DM in another study. 20 Reported Cumulusassessed mammographic breast density correlation between DBT and DM ranged from r 5 0.76 to 0.97 and r 5 0.78 to 0.91.…”

Section: Semi-automated Methodsmentioning

confidence: 99%

Measurement of breast density with digital breast tomosynthesis—a systematic review

Ekpo¹,

McEntee²

2014

BJR

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Digital breast tomosynthesis (DBT) has gained acceptance as an adjunct to digital mammography in screening. Now that breast density reporting is mandated in several states in the USA, it is increasingly important that the methods of breast density measurement be robust, reliable and consistent. Breast density assessment with DBT needs some consideration since quantitative methods are modelled for two-dimensional (2D) mammography. A review of methods used for breast density assessment with DBT was performed. Existing evidence shows Cumulus has better reproducibility than that of the breast imaging reporting and data system (BI-RADS®) but still suffers from subjective variability; MedDensity is limited by image noise, whilst Volpara and Quantra are robust and consistent. The reported BI-RADs inter-reader breast density agreement (k) ranged from 0.65 to 0.91, with inter-reader correlation (r) ranging from 0.70 to 0.93. The correlation (r) between BI-RADS and Cumulus ranged from 0.54-0.94, whilst that of BI-RADs and MedDensity ranged from 0.48-0.78. The reported agreement (k) between BI-RADs and Volpara is 0.953. Breast density correlation between DBT and 2D mammography ranged from 0.73 to 0.97, with agreement (k) ranging from 0.56 to 0.96. To avoid variability and provide more reliable breast density information for clinicians, automated volumetric methods are preferred.Breast cancer accounts for approximately 23% of all cancers in females and is the most frequent cause of cancer deaths in females worldwide.

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“…By combining information from different projections, DBT improves the differentiation of the adjacent anatomical structures, alleviating the superimposition effect [13]. More recent studies indicate about 30% increased DBT sensitivity and specificity compared with DM [14], with a 40% reduction in callback in screening [15-18].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Digital Breast Tomosynthesis and Digital Mammography for Detection of Breast Cancer in Kuwaiti Women

et al. 2018

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Objective: To investigate the sensitivity and specificity of digital mammography (DM) and digital breast tomosynthesis (DBT) for the detection of breast cancer in comparison to histopathology findings. Subjects and Methods: We included 65 breast lesions in 58 women, each detected by two diagnostic mammography techniques – DM and DBT using Senographe Essential (GE Healthcare, Buc, France) – and subsequently confirmed by histopathology. The Breast Imaging Reporting and Data System was used for characterizing the lesions. Results: The average age of women was 48.3 years (range 26–81 years). There were 34 malignant and 31 benign breast lesions. The sensitivity of DM and DBT was 73.5 and 100%, respectively, while the specificity was 67.7 and 94%, respectively. Receiver operating characteristic curve analysis showed an overall diagnostic advantage of DBT over DM, with a significant difference between DBT and DM (p < 0.001). By performing Cohen’s kappa test, we found that there was a strong level of agreement according to Altman guidelines between DBT and histopathology findings (0.97), but a weak agreement between DM and histopathology findings (0.47). Conclusion: DBT improves the clinical accuracy of mammography by increasing both sensitivity and specificity. We believe that this improvement is due to improved image visibility and quality. These results could be of interest to health care institutions as they may impact their decision on whether to upgrade to DBT not only for diagnosis, but also for screening.

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Parenchymal Texture Analysis in Digital Breast Tomosynthesis for Breast Cancer Risk Estimation

Cited by 63 publications

References 64 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?

Measurement of breast density with digital breast tomosynthesis—a systematic review

Comparison of Digital Breast Tomosynthesis and Digital Mammography for Detection of Breast Cancer in Kuwaiti Women

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