Minerva medica · 2020
Efficacy of texture analysis in determining the gene amplification status of HER2 2+ for invasive ductal carcinoma cases.
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Zheng Xu, Yin Jiandong
Аннотация
BACKGROUND: Gene amplification of human epidermal growth factor receptor2 (HER2) 2+ is essential to be determined for treatment planning. A search of the PubMed database indicates that the correlation between texture features from dynamic contrast enhanced (DCE)-MRI and HER2 2+ status has not been investigated extensively in invasive ductal carcinoma cases.
METHODS: Seventy-one DCE-MRI cases of HER2 2+ status verified using fluorescence in-situ hybridization (FISH) were selected, including 36 positive and 35 negative cases. Overall, 279 texture features were derived from lesion regions of interest manually drawn onto the subtraction images between pre- and post-contrast agent. Fisher coefficient, mutual information, minimization of both classification error probability and average correlation coefficients as well as a combination of all three methods (MPF) were independently used to reduce the dimensionality of texture parameters. A popular machine learning algorithm, the Support Vector Machine, was further applied to determine HER2 2+ status. Receiver operating characteristic (ROC) analysis was conducted to evaluate the classification performance.
RESULTS: Diagnostic accuracy was optimal when the most significant discriminatory features were selected using MPF. The area under ROC curve reached 0.863 with corresponding accuracy, sensitivity and specificity rates of 81.80%, 85.71% and 77.78%, respectively.
CONCLUSIONS: Texture analysis based on breast MRI delivered consistently high performance with FISH detection and may serve as a useful supplementary tool for determining the gene amplification status of HER2 2+ for cases with invasive ductal carcinoma.
METHODS: Seventy-one DCE-MRI cases of HER2 2+ status verified using fluorescence in-situ hybridization (FISH) were selected, including 36 positive and 35 negative cases. Overall, 279 texture features were derived from lesion regions of interest manually drawn onto the subtraction images between pre- and post-contrast agent. Fisher coefficient, mutual information, minimization of both classification error probability and average correlation coefficients as well as a combination of all three methods (MPF) were independently used to reduce the dimensionality of texture parameters. A popular machine learning algorithm, the Support Vector Machine, was further applied to determine HER2 2+ status. Receiver operating characteristic (ROC) analysis was conducted to evaluate the classification performance.
RESULTS: Diagnostic accuracy was optimal when the most significant discriminatory features were selected using MPF. The area under ROC curve reached 0.863 with corresponding accuracy, sensitivity and specificity rates of 81.80%, 85.71% and 77.78%, respectively.
CONCLUSIONS: Texture analysis based on breast MRI delivered consistently high performance with FISH detection and may serve as a useful supplementary tool for determining the gene amplification status of HER2 2+ for cases with invasive ductal carcinoma.