Will New Technologies Replace Mammography CAD as We Know It?

Marshall, Julian; Kshirsagar, Ashwini; Narin, Sibel; Gkanatsios, Nikos

doi:10.1007/978-3-319-07887-8_5

Cited by 2 publications

(3 citation statements)

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“…A newly developed 2D imaging “modality” called C-View (synthetic 2D view) can be generated from the 3D DBT data during the mammography exam [ 111 ], eliminating the need for additional 2D exposures. Initial clinical studies have shown that screening with C-View imaging may result in clinical performance superior to that of a conventional 2D mammogram [ 112 ]. However, further evaluation and validation are needed to verify the usage of C-View in a clinical environment.…”

Section: Screen Mammographymentioning

confidence: 99%

A Review on Automatic Mammographic Density and Parenchymal Segmentation

Juette

Denton

et al. 2015

International Journal of Breast Cancer

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Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

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Section: Screen Mammographymentioning

confidence: 99%

A Review on Automatic Mammographic Density and Parenchymal Segmentation

Juette

Denton

et al. 2015

International Journal of Breast Cancer

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“…The C-View TM image is created directly from the 3D data set. 3 Despite these benefits, one potential hurdle for DBT is calcification imaging. Multiple studies have suggested that calcification clusters are not as clearly visible in DBT.…”

Section: Introductionmentioning

confidence: 99%

“…This paper evaluates the potential merits of this prototype CAD algorithm (Hologic ® ImageChecker ® 3D Calc CAD v1.0). 3 Unlike the conventional 2D CAD algorithm for DM (Hologic ® ImageChecker ® CAD v9.4), the 3D CAD algorithm is not yet approved by the United States Food and Drug Administration (FDA), and is intended for investigational use in the United States. Drawing from Penn's database of screening exams, the cases considered in this project span a broad range of features, including:…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of 2D and 3D CAD for calcifications in digital breast tomosynthesis

et al. 2015

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Many medical centers offer digital breast tomosynthesis (DBT) and 2D digital mammography acquired under the same compression (i.e., "Combo" examination) for screening. This paper compares a conventional 2D CAD algorithm (Hologic ® ImageChecker ® CAD v9.4) for calcification detection against a prototype 3D algorithm (Hologic ® ImageChecker ® 3D Calc CAD v1.0). Due to the newness of DBT, the development of this 3D CAD algorithm is ongoing, and it is currently not FDA-approved in the United States. For this study, DBT screening cases with suspicious calcifications were identified retrospectively at the University of Pennsylvania. An expert radiologist (E.F.C.) reviewed images with both 2D and DBT CAD marks, and compared the marks to biopsy results. Control cases with one-year negative follow-up were also studied; these cases either possess clearly benign calcifications or lacked calcifications. To allow the user to alter the sensitivity for cancer detection, an operating point is assigned to each CAD mark. As expected from conventional 2D CAD, increasing the operating point in 3D CAD increases sensitivity and reduces specificity. Additionally, we showed that some cancers are occult to 2D CAD at all operating points. By contrast, 3D CAD allows for detection of some cancers that are missed on 2D CAD. We also demonstrated that some non-cancerous CAD marks in 3D are not present at analogous locations in the 2D image. Hence, there are additional marks when using both 2D and 3D CAD in combination, leading to lower specificity than with conventional 2D CAD alone.

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Will New Technologies Replace Mammography CAD as We Know It?

Cited by 2 publications

References 0 publications

A Review on Automatic Mammographic Density and Parenchymal Segmentation

A Review on Automatic Mammographic Density and Parenchymal Segmentation

A comparative analysis of 2D and 3D CAD for calcifications in digital breast tomosynthesis

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