Neural Network Ensemble-Based Computer-Aided Diagnosis for Differentiation of Lung Nodules on CT Images Clinical Evaluation

Rationale and Objectives: To evaluate the diagnostic performance of a neural network ensemble-based computer-aided diagnosis (CAD) scheme for classifying lung nodules on thin-section computed tomography (CT). Materials and Methods: Thirty-two CT images that depicted 19 malignant nodules and 13 benign nodules were used. One of three possible classifications (probably benign, uncertain, and probably malignant) for each nodule was determined by using a neural network ensemble-based CAD scheme. The images were presented to three senior radiologists (each with more than 10 years of thoracic radiology experience) who were asked to determine the classification for each nodule blindly. The radiologists made their diagnostic decisions solely based on images and excluded any external data. The performance of the CAD scheme and of the radiologists was evaluated with receiver operating characteristic (ROC) analysis and agreement analysis. Results: Areas under the ROC curve (Az values) for the CAD scheme and the radiologist group were 0.79 and 0.82, respectively, and the partial areas under the ROC curves at a range of sensitivity values greater than or equal to 90% were 0.051 and 0.020 (P = .203), respectively. The weighted Kappa coefficients between the CAD scheme and each radiologist were 0.657, 0.431, and 0.606, respectively. For the diagnosis of the 11 small nodules (with diameters not greater than 10 mm), areas under the ROC curves of the CAD scheme and the radiologist group were 0.915 and 0.683 (P = .227), respectively. Conclusions: The diagnostic performance of the neural network ensemble-based CAD scheme is similar to that of senior radiologists for classifying lung nodules on thin-section CT. Furthermore, the CAD scheme has certain advantages in diagnosing small lung nodules.