The Potential Use of DCE-MRI Texture Analysis to Predict HER2 2+ Status

Abstract

Purpose: To evaluate the ability of texture analysis of breast dynamic contrast enhancement-magnetic resonance (DCE-MR) images in differentiating human epidermal growth factor receptor 2 (HER2) 2+ status of breast tumors. Methods: A total of 73 cases were retrospectively selected. HER2 2+ status was confirmed by fluorescence in situ hybridization. For each case, 279 textural features were derived. A student's t -test or Mann-Whitney U test was used to select features with statistically significant differences between HER2 2+ positive and negative groups. A principal component analysis was applied to eliminate feature correlation. Three machine learning classifiers, logistic regression (LR), quadratic discriminant analysis (QDA), and a support vector machine (SVM), were trained and tested using a leave-one-out cross-validation method. The area under a receiver operating characteristic curve (AUC) was measured to assess the classifier's performance. Results: The AUCs for the different classifiers were satisfactory, ranging from 0.808 to 0.865. The classification methods derived with LR and SVM demonstrated similarly high performances, and the accuracy levels were 81.06 and 81.18%, respectively. The AUC for the classifier derived with SVM was the highest (0.865), and a marked specificity (88.90%) was presented. For the classifier with LR, the AUC was 0.851, and the corresponding sensitivity (94.44%) was the highest. Conclusion: The texture analysis for breast DCE-MRI proposed in this study demonstrated potential utility in HER2 2+ status discrimination.

MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions

“…Many studies in the medical image field of machine learning algorithm had noticed the influence on the diagnostic performance caused by the adoption of different texture features. Recently, the IBSI had standardized the extraction of image biomarkers from imaging Previous researches had discussed various kinds of selection methods including Student's t-test, Mann-Whitney U test, ReliefF algorithm (36,37). Based on the results, the interference of the selection method cannot be ruled out.…”

“…All the texture features included in this study were recommended in the IBSI feature reference values and added to the quality of our research ( 20 ). Previous researches had discussed various kinds of selection methods including Student’s t-test, Mann–Whitney U test, ReliefF algorithm ( 36 , 37 ). Based on the results, the interference of the selection method cannot be ruled out.…”

Machine learning‐based model constructed from ultrasound radiomics and clinical features for predicting HER2 status in breast cancer patients with indeterminate (2+) immunohistochemical results

“…According to a study conducted by Zejun Jiang et al, AUCs for texture analysis-based ML models of breast dynamic contrast enhancement-magnetic resonance (DCE-MR) images for predicting HER2 2+ patients with breast cancer ranged from 0.808 to 0.865. 48 However, the sample size of that study was very small, with only 73 cases and 279 texture features and there was no validation set to validate the results. While our models exhibited promise, it's crucial to emphasize their current role as potential adjuncts rather than replacements for FISH testing in differentiating HER2 IHC 2+ breast cancer patients.…”

Potential of the Non-Contrast-Enhanced Chest CT Radiomics to Distinguish Molecular Subtypes of Breast Cancer: A Retrospective Study

“…SVM is a binary classification model, and the core idea is a hyperplane defined in the feature space, which can maximize the geometric interval between different categories, but at the same time, it can also carry out a variety of kernel transformations. This also makes it an essentially non-linear classifier, which is widely used in a variety of scenarios, especially showing great advantages for biomedical classification problems ( 34 – 36 ). SVM is also widely used in breast cancer research.…”