Characterization of breast tumors using machine learning based upon multiparametric magnetic resonance imaging features. (28th December 2021)

• Record Type: Journal Article
• Authors: Thakran, Snekha; Gupta, Rakesh Kumar; Singh, Anup
• Is Part Of: NMR in biomedicine. Volume 35:Number 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-28
• Subjects: adaptive boosting technique -- breast tumor -- machine learning -- multiparametric MRI -- quantitative and texture analysis; Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.4665
• Languages: English
• ISSNs: 0952-3480

Abstract

Abstract : Magnetic resonance imaging (MRI) is playing an important role in the classification of breast tumors. MRI can be used to obtain multiparametric (mp) information, such as structural, hemodynamic, and physiological information. Quantitative analysis of mp‐MRI data has shown potential in improving the accuracy of breast tumor classification. In general, a large set of quantitative and texture features can be generated depending upon the type of methodology used. A suitable combination of selected quantitative and texture features can further improve the accuracy of tumor classification. Machine learning (ML) classifiers based upon features derived from MRI data have shown potential in tumor classification. There is a need for further research studies on selecting an appropriate combination of features and evaluating the performance of different ML classifiers for accurate classification of breast tumors. The objective of the current study was to develop and optimize an ML framework based upon mp‐MRI features for the characterization of breast tumors (malignant vs. benign and low‐ vs. high‐grade). This study included the breast mp‐MRI data of 60 female patients with histopathology results. A total of 128 features were extracted from the mp‐MRI tumor data followed by features selection. Five ML classifiers were evaluated for tumor classification using 10‐fold crossvalidation with 10 repetitions. The support vector machine (SVM) classifier based on optimum features selected using a wrapper method with an adaptive boosting (AdaBoost) technique provided the highest sensitivity (0.96 ± 0.03), specificity (0.92 ± 0.09), and accuracy (94% ± 2.91%) in the classification of malignant versus benign tumors. This method also provided the highest sensitivity (0.94 ± 0.07), specificity (0.80 ± 0.05), and accuracy (90% ± 5.48%) in the classification of low‐ versus high‐grade tumors. These findings suggest that the SVM classifier outperformed other ML methods in the binary classification of breast tumors. Abstract : This study devised a machine learning‐based framework for the characterization of breast tumors using an optimized feature vector computed from multiparametric MRI data. The support vector machine classifier based on optimum features selected using a wrapper method with an adaptive boosting technique achieved high performance accuracy (94% ± 2.91% in the classification of malignant vs. benign and 90% ± 5.48% in the classification of low‐ vs. high‐grade tumors) compared with other methods.