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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.

Prostate lesion segmentation in MR images using radiomics based deeply supervised U-Net

Hambarde Praful, Talbar Sanjay, Mahajan Abhishek, Chavan Satishkumar, Thakur Meenakshi, Sable Nilesh

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 4

1421--1435

Prostate lesion detection in an axial T2 weighted (T2W) MR images is a very challenging task due to heterogeneous and inconsistent pixel representation surrounding the prostate boundary. In this paper, a radiomics based deeply supervised U-Net is proposed for both prostate gland and prostate lesion segmentation. The proposed pipeline is trained and validated on 1174 and 2071 T2W MR images of 40 patients and tested on 250 and 415 T2W MR images of 10 patients for prostate capsule segmentation and prostate lesion segmentation, respectively. Effective segmentation of prostate lesions in various stages of prostate cancer (namely T1, T2, T3, and T4) is achieved using the proposed framework. The mean Dice Similarity Coefficient (DSC) for actual prostate capsule segmentation and prostate lesion segmentation is 0.8958 and 0.9176, respectively. The proposed framework is also tested on Promise12 public dataset for performance analysis in segmenting prostate gland. The segmentation results using proposed architecture are promising compared to state-of-the-art techniques. It also improves the accuracy of the prostate cancer diagnosis.