Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study
Article
Homayoun, Hassan, Chan, Wai Yee, Kuzan, Taha Yusuf, Leong, Wai Ling, Altintoprak, Kübra Murzoglu, Mohammadi, Afshin, Vijayananthan, Anushya, Rahmat, Kartini, Leong, Sook Sam, Mirza-Aghazadeh-Attari, Mohammad, Ejtehadifar, Sajjad, Faeghi, Fariborz, 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. https://doi.org/10.1016/j.bbe.2022.07.004
| Article Title | Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study |
|---|---|
| ERA Journal ID | 211872 |
| Article Category | Article |
| Authors | Homayoun, Hassan, Chan, Wai Yee, Kuzan, Taha Yusuf, Leong, Wai Ling, Altintoprak, Kübra Murzoglu, Mohammadi, Afshin, Vijayananthan, Anushya, Rahmat, Kartini, Leong, Sook Sam, Mirza-Aghazadeh-Attari, Mohammad, Ejtehadifar, Sajjad, Faeghi, Fariborz, Acharya, U. Rajendra and Ardakani, Ali Abbasian |
| Journal Title | Biocybernetics and Biomedical Engineering |
| Journal Citation | 42 (3), pp. 921-933 |
| Number of Pages | 13 |
| Year | 2022 |
| Publisher | Elsevier BV |
| Place of Publication | Netherlands |
| ISSN | 0208-5216 |
| 2391-467X | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.bbe.2022.07.004 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0208521622000687 |
| 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 differentiating 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 generate 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. |
| Keywords | Artificial Intelligence; Computer-assisted Diagnosis; Machine Learning; Breast Cancer; Radiomics |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Tehran University of Medical Sciences, Iran |
| Gleneagles Hospital, Malaysia | |
| Sancaktepe Sehit Prof. Dr. Ilhan Varank Training and Research Hospital, Turkey | |
| University of Malaya, Malaysia | |
| Urmia University of Medical Science, Iran | |
| MARA University of Technology, Malaysia | |
| Tabriz University of Medical Sciences, Iran | |
| Shahid Beheshti University of Medical Sciences, Iran | |
| Ngee Ann Polytechnic, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan |
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https://research.usq.edu.au/item/z1v9w/applications-of-machine-learning-algorithms-for-prediction-of-benign-and-malignant-breast-lesions-using-ultrasound-radiomics-signatures-a-multi-center-study
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