Breast DCE-MRI radiomics: a robust computer-aided system based on reproducible BI-RADS features across the influence of datasets bias and segmentation methods
| العنوان: | Breast DCE-MRI radiomics: a robust computer-aided system based on reproducible BI-RADS features across the influence of datasets bias and segmentation methods |
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| المؤلفون: | Yi Guo, Shiteng Suo, Jia Hua, Mengyun Qiao, Yuanyuan Wang, Jianrong Xu, Chengkang Li, Fang Cheng, Dan Xue |
| المصدر: | International Journal of Computer Assisted Radiology and Surgery. 15:921-930 |
| بيانات النشر: | Springer Science and Business Media LLC, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Computer science, Breast imaging, Intraclass correlation, 0206 medical engineering, Biomedical Engineering, Breast Neoplasms, Health Informatics, BI-RADS, Feature selection, 02 engineering and technology, Sensitivity and Specificity, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Humans, Radiology, Nuclear Medicine and imaging, Segmentation, Breast, business.industry, Reproducibility of Results, Pattern recognition, General Medicine, Prognosis, Magnetic Resonance Imaging, 020601 biomedical engineering, Computer Graphics and Computer-Aided Design, Computer Science Applications, Support vector machine, Concordance correlation coefficient, Computer-aided, Female, Surgery, Computer Vision and Pattern Recognition, Artificial intelligence, business |
| الوصف: | A highly accurate and robust computer-aided system based on quantitative high-throughput Breast Imaging Reporting and Data System (BI-RADS) features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can drive the success of radiomic applications in breast cancer diagnosis. We aim to build a stable system with highly reproducible radiomics features, which can make diagnostic performance independent of datasets bias and segmentation methods. We applied a dataset of 267 patients including 136 malignant and 131 benign tumors from two MRI manufacturers, where 211 cases from a Philips system and 55 cases from a GE system. First, manual annotations, 3D-Unet and 2D-Unet were applied as different segmentation methods. Second, we designed and extracted 3172 features from six modalities of DCE-MRI based on BI-RADS. Third, the feature selection was conducted. Between-class distance was utilized to eliminate the effect of dataset bias caused by two machines. Concordance correlation coefficient, intraclass correlation coefficient and deviation were employed to evaluate the influence of three segmentation methods. We further eliminated features redundancy using genetic algorithm. Finally, three classifiers including support vector machine (SVM), the bagged trees and K-Nearest Neighbor were evaluated by their performance for diagnosing malignant and benign tumors. A total of 246 features were preserved to have high stability and reproducibility. The final feature set showed the robust performance under these factors and achieved the area under curve of 0.88, the accuracy of 0.824, the sensitivity of 0.844, the specificity of 0.807 in differentiating benign and malignant tumors with the SVM classifier using manually segmentation results. The final selected 246 features are reproducible and show little dependence on segmentation methods and data perturbation. The high stability and effectiveness of diagnosis across these factors illustrate that the preserved features can be used for prognostic analysis and help radiologists in the diagnosis of breast cancer. |
| تدمد: | 1861-6429 1861-6410 |
| DOI: | 10.1007/s11548-020-02177-0 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57fda04c2e66a5a91acd674a84c018c2 https://doi.org/10.1007/s11548-020-02177-0 |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi.dedup.....57fda04c2e66a5a91acd674a84c018c2 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 18616429 18616410 |
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| DOI: | 10.1007/s11548-020-02177-0 |