Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI

Abstract

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.

Artificial intelligence to support person-centred care in breast imaging - A scoping review

“…Most studies included women of an age range that made them eligible to participate in screening programs. However, younger women were also included in some studies aging between 18 and 30 years old (23)(24)(25)(26)(27)(28)(29).…”

Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging

“…Instead of the spatial dimension, these transformations convert the image to investigate texture in an alternative dimension such as time or frequency. Although not as widely used as GLCM, transform-based approaches in breast cancer have also identified, characterized, and predicted response (119)(120)(121)(122)(123).…”

Radiomic Evaluations of the Diagnostic Performance of DM, DBT, DCE MRI, DWI, and Their Combination for the Diagnosisof Breast Cancer

“…Subsequent analysis, including statistics, machine learning classifiers, and nomogram can give associations between imaging features and the underlying pathophysiology (18). Radiomics-based studies on breast cancer have been proposed for predicting the axillary lymph node metastasis (19)(20)(21)(22)(23), molecular subtypes (24)(25)(26)(27)(28), tumor grades (29)(30)(31), and treatment responses (32)(33)(34)(35)(36)(37). Some recent studies also conducted a radiomics-based quantified analysis for the diagnosis of breast cancer based on DM (38,39), DBT (40,41), and MRI (42,43) separately, and demonstrated improvements of the diagnostic performance using radiomics compared with visual examinations by radiologists.…”