Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors

• Published in: Computational and mathematical methods in medicine Vol. 2022; pp. 1633858 - 18
• Main Author: Muhtadi, Sabiq
• Format: Journal Article
• Language: English
• Published: United States Hindawi 07-03-2022
• Subjects: Algorithms; Breast Neoplasms - classification; Breast Neoplasms - diagnostic imaging; Computational Biology; Databases, Factual - statistics & numerical data; False Positive Reactions; Female; Humans; Image Interpretation, Computer-Assisted - statistics & numerical data; ROC Curve; Sensitivity and Specificity; Support Vector Machine; Ultrasonography, Mammary - statistics & numerical data
• ISSN: 1748-670X; 1748-6718
• DOI: 10.1155/2022/1633858

Breast cancer is a global epidemic, responsible for one of the highest mortality rates among women. Ultrasound imaging is becoming a popular tool for breast cancer screening, and quantitative ultrasound (QUS) techniques are being increasingly applied by researchers in an attempt to characterize breast tissue. Several different quantitative descriptors for breast cancer have been explored by researchers. This study proposes a breast tumor classification system using the three major types of intratumoral QUS descriptors which can be extracted from ultrasound radiofrequency (RF) data: spectral features, envelope statistics features, and texture features. A total of 16 features were extracted from ultrasound RF data across two different datasets, of which one is balanced and the other is severely imbalanced. The balanced dataset contains RF data of 100 patients with breast tumors, of which 48 are benign and 52 are malignant. The imbalanced dataset contains RF data of 130 patients with breast tumors, of which 104 are benign and 26 are malignant. Holdout validation was used to split the balanced dataset into 60% training and 40% testing sets. Feature selection was applied on the training set to identify the most relevant subset for the classification of benign and malignant breast tumors, and the performance of the features was evaluated on the test set. A maximum classification accuracy of 95% and an area under the receiver operating characteristic curve (AUC) of 0.968 was obtained on the test set. The performance of the identified relevant features was further validated on the imbalanced dataset, where a hybrid resampling strategy was firstly utilized to create an optimal balance between benign and malignant samples. A maximum classification accuracy of 93.01%, sensitivity of 94.62%, specificity of 91.4%, and AUC of 0.966 were obtained. The results indicate that the identified features are able to distinguish between benign and malignant breast lesions very effectively, and the combination of the features identified in this research has the potential to be a significant tool in the noninvasive rapid and accurate diagnosis of breast cancer.