Performance comparison of artificial neural network and logistic regression model for differentiating lung nodules on CT scans

Authors:
Hui Chen;Jing Zhang;Yan Xu;Budong Chen;Kuan Zhang
Affiliations:
School of Biomedical Engineering, Capital Medical University, Beijing 100069, China;School of Biomedical Engineering, Capital Medical University, Beijing 100069, China;Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China;Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China;School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

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, p0.05) 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.