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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 Kübra Murzoglu, Mohammadi Afshin, Vijayananthan Anushya, Rahmat Kartini, Leong Sook Sam, Mirza-Aghazadeh-Attari Mohammad, Ejtehadifar Sajjad, Faeghi Fariborz, Acharya U. Rajendra, Ardakani Ali Abbasian

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 3

921--933

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.

Deep learning on ultrasound images of thyroid nodules

Sharifi Yasaman, Bakhshali Mohamad Amin, Dehghani Toktam, DanaiAshgzari Morteza, Sargolzaei Mahdi, Eslami Saeid

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 2

636--655

Due to safety, easy accessibility, noninvasively and cost-effectiveness of ultrasound imaging, this technology becomes one of the main contributors for analyzing thyroid nodules. However, interpretation of ultrasound images is a challenging task that subjects to the radiologist’s prior medical knowledge and observational skills. There is a significant need for reliable, objective, and automated approaches for the meaningful assessment of ultrasound images. Many areas of machine learning including computer vision and image processing have been revolutionized by the recent advances in the field of deep learning. The current study systematically reviews the existing literatures and evaluates technical characteristics of the deep learning applications on the ultrasound images of thyroid nodules. In this review, all of the included studies have been published from 2017 to 2020 indicating the recent growing interest in the utilization of deep learning-based techniques for assessment of ultrasound images of thyroid nodules. Although deep learning has demonstrated potential for analyzing thyroid nodules’ ultrasound images, this review highlights several existing barriers that need to be addressed in future works such as dealing with data limitation, generating public and valid datasets, and determining standard evaluation metrics. This survey outlines several methods (e.g., data augmentation and transfer learning) recently proposed to address similar challenges in other fields. Furthermore, to improve the diagnostic accuracy of the deep learning models, utilization of complementary information with multi-modal images are suggested.