Open Access
Open access
Nature Communications, volume 5, issue 1, publication number 3797

Non-invasive classification of microcalcifications with phase-contrast X-ray mammography

Zhentian Wang 1
Nik Hauser 2
Gad Singer 3
Mafalda Trippel 3
Rahel A. Kubik-Huch 4
Christof W Schneider 5
Marco Stampanoni 1, 6
2
 
Department of Gynecology and Obstetrics, Interdisciplinary Breast Center Baden, Kantonsspital Baden, Baden, Switzerland
3
 
Institute of Pathology, Kantonsspital Baden, Baden, Switzerland
4
 
Department of Radiology, Kantonsspital Baden, Baden, Switzerland
Publication typeJournal Article
Publication date2014-05-15
scimago Q1
SJR4.887
CiteScore24.9
Impact factor14.7
ISSN20411723
General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy
Abstract
Microcalcifications can be indicative in the diagnosis of early breast cancer. Here we report a non-invasive diagnostic method that may potentially distinguish between different types of microcalcifications using X-ray phase-contrast imaging. Our approach exploits the complementary nature of the absorption and small-angle scattering signals of microcalcifications, obtained simultaneously with an X-ray grating interferometer on a conventional X-ray tube. We demonstrate that the new approach has 100% sensitivity and specificity when applied to phantom data, and we provide evidence of the solidity of the technique by showing its discrimination power when applied to fixed biopsies, to non-fixed tissue specimens and to fresh, whole-breast samples. The proposed method might be further developed to improve early breast cancer diagnosis and has the potential to increase the diagnostic accuracy and reduce the number of uncomfortable breast biopsies, or, in case of widespread microcalcifications, to select the biopsy site before intervention. X-ray absorption imaging is used for early breast cancer detection but can barely identify the morphology of microcalcifications—a possible indicator of cancer. Wang et al.develop a technique to non-invasively classify different types of microcalcifications and achieve 100% sensitivity on phantom data.
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