期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 39, 期 13, 页码 11503-11509出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2012.04.001
关键词
Artificial neural network; Logistic regression; Lung nodule; Diagnostic performance; Comparison
类别
资金
- Beijing Municipal Education Commission, China [KM201110025008]
Purpose: To compare the diagnostic performances of artificial neural networks (ANNs) and multivariable logistic regression (LR) analyses for differentiating between malignant and benign lung nodules on computed tomography (CT) scans. Methods: This study evaluated 135 malignant nodules and 65 benign nodules. For each nodule, morphologic features (size, margins, contour, internal characteristics) on CT images and the patient's age, sex and history of bloody sputum were recorded. Based on 200 bootstrap samples generated from the initial dataset, 200 pairs of ANN and LR models were built and tested. The area under the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow statistic and overall accuracy rate were used for the performance comparison. Results: ANNs had a higher discriminative performance than LR models (area under the ROC curve: 0.955 +/- 0.015 (mean +/- standard error) and 0.929 +/- 0.017, respectively, p < 0.05). The overall accuracy rate for ANNs (90.0 +/- 2.0%) was greater than that for LR models (86.9 +/- 1.6%, p < 0.05). The Hosmer-Lemeshow statistic for the ANNs was 8.76 +/- 6.59 vs. 6.62 +/- 4.03 (p > 0.05) for the LR models. Conclusions: When used to differentiate between malignant and benign lung nodules on CT scans based on both objective and subjective features, ANNs outperformed LR models in both discrimination and clinical usefulness, but did not outperform for the calibration. (c) 2012 Elsevier Ltd. All rights reserved.
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