Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images
Abstract Background The purpose of this work was to evaluate computable Breast Imaging Reporting and Data System (BI-RADS) radiomic features to classify breast masses on ultrasound B-mode images. Methods The database consisted of 206 consecutive lesions (144 benign and 62 malignant) proved by percut...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-08-01
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| Series: | European Radiology Experimental |
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| Online Access: | http://link.springer.com/article/10.1186/s41747-019-0112-7 |
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| author | Eduardo Fleury Karem Marcomini |
| author_facet | Eduardo Fleury Karem Marcomini |
| author_sort | Eduardo Fleury |
| collection | DOAJ |
| description | Abstract Background The purpose of this work was to evaluate computable Breast Imaging Reporting and Data System (BI-RADS) radiomic features to classify breast masses on ultrasound B-mode images. Methods The database consisted of 206 consecutive lesions (144 benign and 62 malignant) proved by percutaneous biopsy in a prospective study approved by the local ethical committee. A radiologist manually delineated the contour of the lesions on greyscale images. We extracted the main ten radiomic features based on the BI-RADS lexicon and classified the lesions as benign or malignant using a bottom-up approach for five machine learning (ML) methods: multilayer perceptron (MLP), decision tree (DT), linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). We performed a 10-fold cross validation for training and testing of all classifiers. Receiver operating characteristic (ROC) analysis was used for providing the area under the curve with 95% confidence intervals (CI). Results The classifier with the highest AUC at ROC analysis was SVM (AUC = 0.840, 95% CI 0.6667–0.9762), with 71.4% sensitivity (95% CI 0.6479–0.8616) and 76.9% specificity (95% CI 0.6148–0.8228). The best AUC for each method was 0.744 (95% CI 0.677–0.774) for DT, 0.818 (95% CI 0.6667–0.9444) for LDA, 0.811 (95% CI 0.710–0.892) for RF, and 0.806 (95% CI 0.677–0.839) for MLP. Lesion margin and orientation were the optimal features for all the machine learning methods. Conclusions ML can aid the distinction between benign and malignant breast lesion on ultrasound images using quantified BI-RADS descriptors. SVM provided the highest ROC-AUC (0.840). |
| first_indexed | 2024-12-14T15:10:51Z |
| format | Article |
| id | doaj.art-88546cdf1c814a608f9be14a37678bd6 |
| institution | Directory Open Access Journal |
| issn | 2509-9280 |
| language | English |
| last_indexed | 2024-12-14T15:10:51Z |
| publishDate | 2019-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Radiology Experimental |
| spelling | doaj.art-88546cdf1c814a608f9be14a37678bd62022-12-21T22:56:34ZengSpringerOpenEuropean Radiology Experimental2509-92802019-08-01311810.1186/s41747-019-0112-7Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound imagesEduardo Fleury0Karem Marcomini1Instituto Brasileiro de Controle do Câncer (IBCC)USP São CarlosAbstract Background The purpose of this work was to evaluate computable Breast Imaging Reporting and Data System (BI-RADS) radiomic features to classify breast masses on ultrasound B-mode images. Methods The database consisted of 206 consecutive lesions (144 benign and 62 malignant) proved by percutaneous biopsy in a prospective study approved by the local ethical committee. A radiologist manually delineated the contour of the lesions on greyscale images. We extracted the main ten radiomic features based on the BI-RADS lexicon and classified the lesions as benign or malignant using a bottom-up approach for five machine learning (ML) methods: multilayer perceptron (MLP), decision tree (DT), linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). We performed a 10-fold cross validation for training and testing of all classifiers. Receiver operating characteristic (ROC) analysis was used for providing the area under the curve with 95% confidence intervals (CI). Results The classifier with the highest AUC at ROC analysis was SVM (AUC = 0.840, 95% CI 0.6667–0.9762), with 71.4% sensitivity (95% CI 0.6479–0.8616) and 76.9% specificity (95% CI 0.6148–0.8228). The best AUC for each method was 0.744 (95% CI 0.677–0.774) for DT, 0.818 (95% CI 0.6667–0.9444) for LDA, 0.811 (95% CI 0.710–0.892) for RF, and 0.806 (95% CI 0.677–0.839) for MLP. Lesion margin and orientation were the optimal features for all the machine learning methods. Conclusions ML can aid the distinction between benign and malignant breast lesion on ultrasound images using quantified BI-RADS descriptors. SVM provided the highest ROC-AUC (0.840).http://link.springer.com/article/10.1186/s41747-019-0112-7Breast neoplasmsMachine LearningNeural networks (computer)Support vector machineUltrasonography |
| spellingShingle | Eduardo Fleury Karem Marcomini Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images European Radiology Experimental Breast neoplasms Machine Learning Neural networks (computer) Support vector machine Ultrasonography |
| title | Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images |
| title_full | Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images |
| title_fullStr | Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images |
| title_full_unstemmed | Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images |
| title_short | Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images |
| title_sort | performance of machine learning software to classify breast lesions using bi rads radiomic features on ultrasound images |
| topic | Breast neoplasms Machine Learning Neural networks (computer) Support vector machine Ultrasonography |
| url | http://link.springer.com/article/10.1186/s41747-019-0112-7 |
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