Original investigationPrediction of Malignant Breast Lesions from MRI Features: A Comparison of Artificial Neural Network and Logistic Regression Techniques
Section snippets
Malignant and Benign Lesion Database
The database analyzed in this study included 43 malignant and 28 benign lesions, the same database as reported in a previous study (18). The 43 patients with malignant lesions were aged 29 to 76 years (mean ± standard deviation, 48 ± 9 years; median, 48 years). The 28 patients with benign lesions were aged 21 to 74 years (mean, 45 ± 7 years; median, 45 years). The MRI studies were performed using a Philips Eclipse 1.5-T scanner (Philips, Cleveland, OH). The images were acquired using a
ANN Diagnostic Feature Selection and Evaluation
Before ANN training, features were transformed to z scores on the basis of the mean and SD of the entire set of 71 lesions. A three-layer ANN with 5 hidden nodes in the hidden layer was chosen after a number of trial-and-error runs. The diagnostic measures used to assess differentiation between 43 malignant and 28 benign lesions are summarized in Table 1. Considering the eight morphology features, the classifier selected by ANN included three parameters: lesion volume, NRL entropy, and
Discussion
The primary aim of this study was to compare ANN and logistic regression analysis for lesion classification to differentiate between malignant and benign breast lesions in patients. Using our data set of 71 lesions, the ANN procedure was applied to select the best classifiers for morphologic and texture (GLCM and Laws) category features. The three selected morphologic features (volume, NRL entropy, and compactness) achieved a moderate AUC of 0.80 and estimated accuracy of 0.77. The three
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This work was supported in part by grant R01 CA90437 (O. Nalcioglu) and R21 CA121568 (M.-Y.Su) from the National Cancer Institute (Bethesda, MD), grants 9WB-0020 (M.-Y. Su) and 14GB-0148 (K. Nie) from the California Breast Cancer Research Program (Oakland, CA), and Cancer Center Support Grant No. 2P30CA062203-13S (F.L. Meyskens, Jr) from the National Cancer Institute (Bethesda, MD).