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Keywords {🔍}

breast, features, expression, cancer, study, radiomic, dbt, high, article, analysis, imaging, low, images, tomosynthesis, radiomics, digital, quantitative, table, tumour, patients, image, data, auc, google, scholar, size, tagliafico, extracted, proliferation, statistical, studies, area, access, published, full, versus, curve, correlation, significant, medical, software, values, contributed, consent, research, radiology, open, predictive, results, correlated,

Topics {✒️}

dynamic contrast-enhanced mri experimental studies/data analysis open-source software platform digital breast tomosynthesis reflecting intra-tumoural heterogeneity reflect intra-tumoural heterogeneity high ki-67-expressed lesions alberto stefano tagliafico vacuum/assisted breast biopsy diffusion-weighted imaging 25th european congress full access article download pdf digtal breast tomosynthesis receiver operating characteristic quantitative imaging network tumoural aggressiveness assessment related subjects quantitative imaging features dce-mri radiomics features european economic area high ki-67 expression privacy choices/manage cookies breast cancer screening early breast cancer sydney medical school invasive breast cancer cell proliferation dce-mri-based features dbt-based features identified dense breast found breast cancer patients ki-67 proliferation index percent confidence intervals contrast media injection predict ki-67 expression high ki-67 group breast cancer biology intra-observer agreement quantitative image analysis breast tumour extracted proliferation marker ki-67 central tomo slices univariate linear regression breast cancer cases invasive breast carcinoma low ki-67 group image computing platform radiology section quantitative radiomic features

Schema {🗺️}

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         headline:Breast cancer Ki-67 expression prediction by digital breast tomosynthesis radiomics features
         description:To investigate whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) are associated with Ki-67 expression of breast cancer. This is 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. A combination of five features yielded AUC of up to 0.698. The five most predictive features (sphericity, autocorrelation, interquartile range, robust mean absolute deviation, and short-run high grey-level emphasis) showed a statistical significance (p ≤ 0.001) in the classification. Thirty-four features were significantly (p ≤ 0.001) correlated with Ki-67, and five of these had a correlation coefficient of > 0.5. The present study showed that quantitative radiomic imaging features of breast tumour extracted from DBT images are associated with breast cancer Ki-67 expression. Larger studies are needed in order to further evaluate these findings.
         datePublished:2019-08-14T00:00:00Z
         dateModified:2019-08-14T00:00:00Z
         pageStart:1
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         license:http://creativecommons.org/licenses/by/4.0/
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            Cell proliferation
            Ki-67 expression
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            Radiomics
            Imaging / Radiology
            Diagnostic Radiology
            Interventional Radiology
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            Ultrasound
            Internal Medicine
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      headline:Breast cancer Ki-67 expression prediction by digital breast tomosynthesis radiomics features
      description:To investigate whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) are associated with Ki-67 expression of breast cancer. This is 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. A combination of five features yielded AUC of up to 0.698. The five most predictive features (sphericity, autocorrelation, interquartile range, robust mean absolute deviation, and short-run high grey-level emphasis) showed a statistical significance (p ≤ 0.001) in the classification. Thirty-four features were significantly (p ≤ 0.001) correlated with Ki-67, and five of these had a correlation coefficient of > 0.5. The present study showed that quantitative radiomic imaging features of breast tumour extracted from DBT images are associated with breast cancer Ki-67 expression. Larger studies are needed in order to further evaluate these findings.
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      dateModified:2019-08-14T00:00:00Z
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         Cell proliferation
         Ki-67 expression
         Mammography
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         Diagnostic Radiology
         Interventional Radiology
         Neuroradiology
         Ultrasound
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