Yongshuai Li1, Yuan Liu2, Mengke Zhang2, Guanglei Zhang3, Zhili Wang2, Jianwen Luo1. 1. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China. 2. Department of Ultrasound, Chinese People's Liberation Army General Hospital, Beijing, China. 3. Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Abstract
OBJECTIVES: We aimed to develop radiomics with attribute bagging, which leverages multimodal ultrasound (US) images to improve the classification accuracy of breast tumors. METHODS: A retrospective study was conducted. B-mode US, shear wave elastographic, and contrast-enhanced US images of 178 patients with 181 tumors (67 malignant and 114 benign) were included. Radiomics with attribute bagging consisted of extraction of 1226 radiomic features and analysis of them with attribute bagging. Histologic examination results acted as the reference standard. Radiomics with several feature selection algorithms were used for comparison. Cross-validation and a holdout test were performed to evaluate their performances. RESULTS: The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of radiomics with attribute bagging with the multimodal US images were 84.12%, 92.86%, 78.80%, and 0.919, respectively, exceeding all the comparison methods. CONCLUSIONS: Radiomics with attribute bagging combined with multimodal US images has the potential to be used for accurate diagnosis of breast tumors in the clinic.
OBJECTIVES: We aimed to develop radiomics with attribute bagging, which leverages multimodal ultrasound (US) images to improve the classification accuracy of breast tumors. METHODS: A retrospective study was conducted. B-mode US, shear wave elastographic, and contrast-enhanced US images of 178 patients with 181 tumors (67 malignant and 114 benign) were included. Radiomics with attribute bagging consisted of extraction of 1226 radiomic features and analysis of them with attribute bagging. Histologic examination results acted as the reference standard. Radiomics with several feature selection algorithms were used for comparison. Cross-validation and a holdout test were performed to evaluate their performances. RESULTS: The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of radiomics with attribute bagging with the multimodal US images were 84.12%, 92.86%, 78.80%, and 0.919, respectively, exceeding all the comparison methods. CONCLUSIONS: Radiomics with attribute bagging combined with multimodal US images has the potential to be used for accurate diagnosis of breast tumors in the clinic.
Authors: Yan Zheng; Lu Bai; Jie Sun; Lin Zhu; Renjun Huang; Shaofeng Duan; Fenglin Dong; Zaixiang Tang; Yonggang Li Journal: Front Oncol Date: 2022-09-20 Impact factor: 5.738