BMC Cancer | |
Improved automated early detection of breast cancer based on high resolution 3D micro-CT microcalcification images | |
Jan Cornelis1  Redona Brahimetaj1  Bart Jansen2  Ramses Forsyth3  Johan De Mey4  Inneke Willekens4  Annelien Massart4  | |
[1] Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Pleinlaan 2, B-1050, Brussels, Belgium;Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Pleinlaan 2, B-1050, Brussels, Belgium;IMEC, Kapeldreef 75, B-3001, Leuven, Belgium;Pathology Department, Universitair Ziekenhuis (UZ) Brussels, Laarbeeklaan 101, 1090, Brussels, Belgium;Radiology Department, Universitair Ziekenhuis (UZ) Brussels, Laarbeeklaan 101, 1090, Brussels, Belgium; | |
关键词: Breast Cancer; Microcalcifications; Computer aided detection and diagnosis systems; X-ray micro-CT; Radiomics; Machine learning; | |
DOI : 10.1186/s12885-021-09133-4 | |
来源: Springer | |
【 摘 要 】
BackgroundThe detection of suspicious microcalcifications on mammography represents one of the earliest signs of a malignant breast tumor. Assessing microcalcifications’ characteristics based on their appearance on 2D breast imaging modalities is in many cases challenging for radiologists. The aims of this study were to: (a) analyse the association of shape and texture properties of breast microcalcifications (extracted by scanning breast tissue with a high resolution 3D scanner) with malignancy, (b) evaluate microcalcifications’ potential to diagnose benign/malignant patients.MethodsBiopsy samples of 94 female patients with suspicious microcalcifications detected during a mammography, were scanned using a micro-CT scanner at a resolution of 9 μm. Several preprocessing techniques were applied on 3504 extracted microcalcifications. A high amount of radiomic features were extracted in an attempt to capture differences among microcalcifications occurring in benign and malignant lesions. Machine learning algorithms were used to diagnose: (a) individual microcalcifications, (b) samples. For the samples, several methodologies to combine individual microcalcification results into sample results were evaluated.ResultsWe could classify individual microcalcifications with 77.32% accuracy, 61.15% sensitivity and 89.76% specificity. At the sample level diagnosis, we achieved an accuracy of 84.04%, sensitivity of 86.27% and specificity of 81.39%.ConclusionsBy studying microcalcifications’ characteristics at a level of details beyond what is currently possible by using conventional breast imaging modalities, our classification results demonstrated a strong association between breast microcalcifications and malignancies. Microcalcification’s texture features extracted in transform domains, have higher discriminating power to classify benign/malignant individual microcalcifications and samples compared to pure shape-features.
【 授权许可】
CC BY
【 预 览 】
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RO202202170312749ZK.pdf | 1057KB | download |