Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI
Joint Authors
Yuan, Gaoteng
Liu, Yihui
Huang, Wei
Hu, Bing
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-07
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Purpose.
The objective of this study is to investigate the use of texture analysis (TA) of magnetic resonance image (MRI) enhanced scan and machine learning methods for distinguishing different grades in breast invasive ductal carcinoma (IDC).
Preoperative prediction of the grade of IDC can provide reference for different clinical treatments, so it has important practice values in clinic.
Methods.
Firstly, a breast cancer segmentation model based on discrete wavelet transform (DWT) and K-means algorithm is proposed.
Secondly, TA was performed and the Gabor wavelet analysis is used to extract the texture feature of an MRI tumor.
Then, according to the distance relationship between the features, key features are sorted and feature subsets are selected.
Finally, the feature subset is classified by using a support vector machine and adjusted parameters to achieve the best classification effect.
Results.
By selecting key features for classification prediction, the classification accuracy of the classification model can reach 81.33%.
3-, 4-, and 5-fold cross-validation of the prediction accuracy of the support vector machine model is 77.79%~81.94%.
Conclusion.
The pathological grading of IDC can be predicted and evaluated by texture analysis and feature extraction of breast tumors.
This method can provide much valuable information for doctors’ clinical diagnosis.
With further development, the model demonstrates high potential for practical clinical use.
American Psychological Association (APA)
Yuan, Gaoteng& Liu, Yihui& Huang, Wei& Hu, Bing. 2020. Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139538
Modern Language Association (MLA)
Yuan, Gaoteng…[et al.]. Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1139538
American Medical Association (AMA)
Yuan, Gaoteng& Liu, Yihui& Huang, Wei& Hu, Bing. Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139538
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139538