Preliminary evaluation of data mining on non-masslike enhancement of breast lesions on MRI
10.3760/cma.j.issn.1005-1201.2009.05.002
- VernacularTitle:数据挖掘技术判定MRI乳腺非肿块样强化病灶的初步研究
- Author:
Hongna TAN
;
Yi SU
;
Ruimin LI
;
Ying CHEN
;
Peihua WANG
;
Feng TANG
;
Jian MAO
;
Xigang SHEN
;
Min QIAN
;
Yajia GU
- Publication Type:Journal Article
- Keywords:
Breast neoplasms;
Data display;
Magnetic resonance imaging;
Cross-over studies
- From:
Chinese Journal of Radiology
2009;43(5):455-459
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the diagnostic values of the breast imaging reporting and data system-MRI (BI-RADS-MRI)description about non-masslike enhancement by data mining. Methods Fifty-five patients with non-masslike enhancement lesions showed on breast contrast-enhanced MRI were evaluated using two data mining algorithms (Logistic regression and decision tree) and 10-fold cross-validation methods. Results There were 28 malignant and 27 benign lesions. The most frequent findings of the malignant lesions were clustered ring enhancement and clumped enhancement [ 12 and 4 lesions, respectively; 84. 2% (16/19) in decision trees, partial regression coefficients in Logistic model were 2. 128 and 1.723, respectively], whereas homogenous, stippled, reticular internal and linear ductal enhancement were the most frequent findings in benign lesions [ 4、9、1 and 7 lesions, respectively; 72. 4% (21/29) in decision tree, partial regression coefficients in Logistic model were 0.357 (homogenous), 1. 861 (stippled) and 18. 870( reticular), respectively]. 10-fold cross-validation indicated that decision tree (C5.0) achieved an accuracy of 69.3% with a sensitivity of 66.7% and a specificity of 71.7% in comparison to the Logistic regression model with an accuracy of 57. 0%, a sensitivity of 43.3% and a specificity of 71.7%. Conclusions The diagnosis efficacy of non-masslike enhancement interpretation according to BI-RADS-MRI is not high. It is very important to find more potential features of non-masslike enhancement to improve the diagnosis accuracy.
