A radiomics study of textural features using magnetic resonance imaging for classification of breast cancer subtypes

Tang, Z. Y. and Tan, Li Kuo and Ng, B. Y. and Rahmat, Kartini and Ramli, M. T. and Ninomiya, K. and Wong, J. H. D. (2020) A radiomics study of textural features using magnetic resonance imaging for classification of breast cancer subtypes. In: 11th International Seminar on Medical Physics (ISMP) 2019, 7-8 November 2019, Kuala Lumpur, Malaysia.

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Official URL: https://iopscience.iop.org/article/10.1088/1742-65...


Breast cancer is usually screened using mammography and biopsy is used to confirm diagnosis. Recent radiomics approaches suggest predictive associations between images and medical outcome. This study aims to classify breast cancer subtypes using textural features derived from magnetic resonance imaging (MRI). Thirty-two lesions with histologic results that were definite were studied. A total of 174 textural features were extracted from four MRI sequences (Axial STIR, dynamic contrast enhance ( DCE) Phase 2, dynamic contrast enhance (DCE) subtracted Phase 2 and T1-weighted), and analysed using t-test, Kruskal-Wallis and principal component analysis (PCA). Evaluation was done using multinomial logistic regression and leave-one-out-cross-validation (LOOCV) methods. We found 14 texture features that consistently showed significant difference between malignant and normal breast tissues across all MRI sequences. Four textural features were useful in histological status with t-test accuracy of 71.4% and PCA accuracy of 64.3%. In hormonal receptor status, only five textural features were useful. The accuracies were also found to be poorer with 46.4% accuracy based on Kruskal-Wallis method and 46.4% accuracy using PCA method. As this is a preliminary study, the analysis should be extended to a larger sample size to accurately determine the possibility of clinical diagnosis.

Item Type: Conference or Workshop Item (Paper)
Additional Information: 11th International Seminar on Medical Physics (ISMP), Kuala Lumpur, Malaysia, Nov 07-08, 2019
Uncontrolled Keywords: Radiomics study; Textural features; Magnetic resonance imaging; Classification; Breast cancer subtypes
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Medicine > Biomedical Imaging Department
Depositing User: Ms Zaharah Ramly
Date Deposited: 13 Apr 2023 07:09
Last Modified: 13 Apr 2023 07:09
URI: http://eprints.um.edu.my/id/eprint/37192

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