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
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Published: Elsevier 2022
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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/
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score 13.15806