Breast cancer Ki-67 expression prediction by digital breast tomosynthesis radiomics features
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Open Access
Type
ArticleAuthor/s
Tagliafico, Alberto SBignotti, Bianca
Rossi, Federica
Matos, Joao
Calabrese, Massimo
Valdora, Francesca
Houssami, Nehmat
Abstract
The aim of this paper was to investigate whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) are associated with Ki-67 expression of breast cancer.
This was a prospective ethically approved study of 70 women diagnosed with invasive breast ...
See moreThe aim of this paper was to investigate whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) are associated with Ki-67 expression of breast cancer. This was a prospective ethically approved study of 70 women diagnosed with invasive breast cancer in 2018, including 40 low Ki-67 expression (Ki-67 proliferation index <14%) cases and 30 high Ki-67 expression (Ki-67 proliferation index ≥ 14%) cases. A set of 106 quantitative radiomic features, including morphological, grey/scale statistics, and texture features, were extracted from DBT images. After applying least absolute shrinkage and selection operator (LASSO) method to select the most predictive features set for the classifiers, low versus high Ki-67 expression was evaluated by the area under the curve (AUC) at receiver operating characteristic analysis. Correlation coefficient was calculated for the most significant features.
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See moreThe aim of this paper was to investigate whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) are associated with Ki-67 expression of breast cancer. This was a prospective ethically approved study of 70 women diagnosed with invasive breast cancer in 2018, including 40 low Ki-67 expression (Ki-67 proliferation index <14%) cases and 30 high Ki-67 expression (Ki-67 proliferation index ≥ 14%) cases. A set of 106 quantitative radiomic features, including morphological, grey/scale statistics, and texture features, were extracted from DBT images. After applying least absolute shrinkage and selection operator (LASSO) method to select the most predictive features set for the classifiers, low versus high Ki-67 expression was evaluated by the area under the curve (AUC) at receiver operating characteristic analysis. Correlation coefficient was calculated for the most significant features.
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Date
2019Source title
European Radiology ExperimentalVolume
3Issue
36Publisher
Springer OpenLicence
Creative Commons Attribution 4.0Faculty/School
Faculty of Medicine and Health, Sydney School of Public HealthShare