Staff View: Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study

Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study

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 s...

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Main Authors:	Homayoun, Hassan, Chan, Wai Yee, Kuzan, Taha Yusuf, Leong, Wai Ling, Altintoprak, Kubra Murzoglu, Mohammadi, Afshin, Vijayananthan, Anushya, Rahmat, Kartini, Leong, Sook Sam, Mirza-Aghazadeh-Attarif, Mohammad, Ejtehadifard, Sajjad, Faeghi, Fariborz, Acharya, U. Rajendra, Ardakani, Ali Abbasian
Format:	Article
Published:	Elsevier 2022
Subjects:	RC Internal medicine
Online Access:	http://eprints.um.edu.my/41592/
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spelling	my.um.eprints.415922023-12-03T02:04:37Z http://eprints.um.edu.my/41592/ Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study Homayoun, Hassan Chan, Wai Yee Kuzan, Taha Yusuf Leong, Wai Ling Altintoprak, Kubra Murzoglu Mohammadi, Afshin Vijayananthan, Anushya Rahmat, Kartini Leong, Sook Sam Mirza-Aghazadeh-Attarif, Mohammad Ejtehadifard, Sajjad Faeghi, Fariborz Acharya, U. Rajendra Ardakani, Ali Abbasian RC Internal medicine 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. Elsevier 2022-07 Article PeerReviewed 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 <https://doi.org/10.1016/j.bbe.2022.07.004>. 10.1016/j.bbe.2022.07.004
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	RC Internal medicine
spellingShingle	RC Internal medicine Homayoun, Hassan Chan, Wai Yee Kuzan, Taha Yusuf Leong, Wai Ling Altintoprak, Kubra Murzoglu Mohammadi, Afshin Vijayananthan, Anushya Rahmat, Kartini Leong, Sook Sam Mirza-Aghazadeh-Attarif, Mohammad Ejtehadifard, Sajjad Faeghi, Fariborz Acharya, U. Rajendra Ardakani, Ali Abbasian Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study
description	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.
format	Article
author	Homayoun, Hassan Chan, Wai Yee Kuzan, Taha Yusuf Leong, Wai Ling Altintoprak, Kubra Murzoglu Mohammadi, Afshin Vijayananthan, Anushya Rahmat, Kartini Leong, Sook Sam Mirza-Aghazadeh-Attarif, Mohammad Ejtehadifard, Sajjad Faeghi, Fariborz Acharya, U. Rajendra Ardakani, Ali Abbasian
author_facet	Homayoun, Hassan Chan, Wai Yee Kuzan, Taha Yusuf Leong, Wai Ling Altintoprak, Kubra Murzoglu Mohammadi, Afshin Vijayananthan, Anushya Rahmat, Kartini Leong, Sook Sam Mirza-Aghazadeh-Attarif, Mohammad Ejtehadifard, Sajjad Faeghi, Fariborz Acharya, U. Rajendra Ardakani, Ali Abbasian
author_sort	Homayoun, Hassan
title	Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study
title_short	Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study
title_full	Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study
title_fullStr	Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study
title_full_unstemmed	Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study
title_sort	applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: a multi-center study
publisher	Elsevier
publishDate	2022
url	http://eprints.um.edu.my/41592/
_version_	1784586068340768768
score	13.222552

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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