Abstract
ObjectiveTo investigate whether texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with human epidermal growth factor receptor type 2 (HER2) 2+ status of breast cancer.Materials and methods92 MRI cases including 52 HER2 2+ positive and 40 negative patients confirmed by fluorescence in situ hybridization were retrospectively selected. The lesion area was semi-automatically delineated, and a total of 488 texture features were respectively extracted from precontrast, postcontrast, and subtraction images. The Student’s t-test or Mann-Whitney U test was performed to identify statistically significant features between different HER2 2+ amplification groups. Least absolute shrinkage and selection operator (LASSO) was used to search for the optimal feature subsets. Three machine learning classifiers, logistic regression analysis (LRA), quadratic discriminant analysis (QDA), and support vector machine (SVM), were used with a leave-one-out cross validation method to establish the classification models of HER2 2+ status. Classification performance was evaluated by receiver operating characteristic (ROC) analysis.ResultsBased on the texture analysis with SVM model, the areas under the ROC curve (AUCs) were 0.890 for subtraction images, 0.736 for postcontrast images, and 0.672 for precontrast images, respectively. For LRA model, the AUCs were 0.884, 0.733, and 0.623, respectively. For QDA model, the AUCs were 0.831, 0.726, and 0.568, respectively. LRA and the SVM model with subtraction images reached significantly better performance than the QDA model (P = 0.0227 and P = 0.0088, respectively).ConclusionTexture features of breast cancer extracted from DCE-MRI are associated with HER2 2+ status. Additional studies are necessary to confirm the present preliminary findings.
Highlights
Breast cancer is a heterogeneous tumor and it is categorized into different molecular subtypes [luminal A, luminal B, human epidermal growth factor receptor type 2 (HER2)-positive, and triple negative] [1,2]
It can be found that the logistic regression analysis (LRA) and the support vector machine (SVM) model with subtraction images reached significantly better performance than the quadratic discriminant analysis (QDA) model (P = 0.0227 and P = 0.0088, respectively) (Table 5)
There is no significant difference between the areas under the ROC curves (AUC) from the LRA and the SVM model
Summary
Breast cancer is a heterogeneous tumor and it is categorized into different molecular subtypes [luminal A, luminal B, human epidermal growth factor receptor type 2 (HER2)-positive, and triple negative (basal like)] [1,2]. HER2 is an important biomarker for determining the molecular subtype of breast cancer, and its expression can usually be determined by immunohistochemistry (IHC). For HER2 scores 0, 1+, and 3+, IHC is accurate for assessing negative or positive status. For HER2 2+ patients, further fluorescence in situ hybridization (FISH) examination is essential to confirm the gene status. Detection of the amplification status of HER2 2+ by FISH prolongs the time for accurate diagnosis, and the timeliness and accuracy of diagnosis are extremely important for doctors and patients. Identifying a cost- and time-effective alternative method for distinguishing HER2 2 + positive and negative status would be beneficial [4]
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