＜正＞ Background Computer-aided diagnosis（CAD）of lung cancer is the subject of many current researches.Statisticalmethods and artificial neural networks have been applied to more quantitatively characterize solitary pulmonary nodules（SPNs）.In this study,we developed a CAD scheme based on an artificial neural network to distinguish malignant frombenign SPNs on thin-section computed tomography（CT）images,and investigated how the CAD scheme can helpradiologists with different levels of experience make diagnostic decisions.Methods Two hundred thin-section CT images of SPNs with proven diagnoses（135 small peripheral lung cancers and65 benign nodules）were analyzed.Three clinical features and nine CT signs of each case were studied by radiologists,and the indices of qualitative diagnosis were quantified.One hundred and forty nodules were selected randomly to formtraining samples,on which the neural network model was built.The remaining 60 nodules,forming test samples,werepresented to 9 radiologists with 3-20 years of clinical experience,accompanied by standard reference images.Theradiologists were asked to determine whether a nodule was malignant or benign first without and then with CAD output.Diagnostic performance was evaluated by receiver operating characteristic（ROC）analysis.Results CAD outputs on test samples had higher agreement with pathological diagnoses（Kappa=0.841,P＜0.001）.Compared with diagnostic results without CAD output,the average area under the ROC curve with CAD output was 0.96（P＜0.001）for junior radiologists,0.94（P=0.014）for secondary radiologists and 0.96（P=0.221）for senior radiologists,respectively.The differences in diagnostic performance with CAD output among the three levels of radiologists were notstatistically significant（P=0.584,0.920 and 0.707,respectively）.Conclusions This CAD scheme based on an artificial neural network could improve diagnostic performance and assistradiologists in distinguishing malignant from benign SPNs on thin-section CT images.
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