Homayoun, Hassan and Chan, Wai Yee and Kuzan, Taha Yusuf and Leong, Wai Ling and Altintoprak, Kubra Murzoglu and Mohammadi, Afshin and Vijayananthan, Anushya and Rahmat, Kartini and Leong, Sook Sam and Mirza-Aghazadeh-Attarif, Mohammad and Ejtehadifard, Sajjad and Faeghi, Fariborz and Acharya, U. Rajendra and Ardakani, Ali Abbasian (2022) Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study. Biocybernetics and Biomedical Engineering, 42 (3). pp. 921-933. ISSN 0208-5216, DOI https://doi.org/10.1016/j.bbe.2022.07.004.
Full text not available from this repository.Abstract
Artificial intelligence (AI) algorithms have an enormous potential to impact the field of radiology and diagnostic imaging, especially the field of cancer imaging. There have been efforts to use AI models to differentiate between benign and malignant breast lesions. However, most studies have been single-center studies without external validation. The present study examines the diagnostic efficacy of machine-learning algorithms in differen-tiating benign and malignant breast lesions using ultrasound images. Ultrasound images of 1259 solid non-cystic lesions from 3 different centers in 3 countries (Malaysia, Turkey, and Iran) were used for the machine-learning study. A total of 242 radiomics features were extracted from each breast lesion, and the robust features were considered for models' development. Three machine-learning algorithms were used to carry out the classification task, namely, gradient boosting (XGBoost), random forest, and support vector machine. Sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were determined to evaluate the models. Thirty-three robust features differed significantly between the two groups from all of the features. XGBoost, based on these robust features, showed the most favorable profile for all cohorts, as it achieved a sensitivity of 90.3%, specificity of 86.7%, the accuracy of 88.4%, and AUC of 0.890. The present study results show that incorporating selected robust radiomics features into well-curated machine-learning algorithms can gen-erate high sensitivity, specificity, and accuracy in differentiating benign and malignant breast lesions. Furthermore, our results show that this optimal performance is preserved even in external validation datasets.(c) 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Artificial Intelligence; Breast cancer; Computer-assisted Diagnosis; Machine Learning; Radiomics |
| Subjects: | R Medicine > RC Internal medicine |
| Divisions: | Faculty of Medicine > Biomedical Imaging Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 23 Nov 2023 09:11 |
| Last Modified: | 03 Dec 2023 02:04 |
| URI: | http://eprints.um.edu.my/id/eprint/41592 |
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