Objective:To amaylze imaging diagnostic test data using receiver operating characteristic curve (ROC) combined with dual-normal model parameter method. Methods:Continuous data of a single common imaging diagnostic test and ordered segment information of two completely diagnostic tests were selected for analysis. The former group consisted of 49 cases of gastric neoplastic lesions was examined by conventional MRI and diffusion-weighted imaging(DWI) to analyze the difference of apparent diffusion coefficient (ADC) between gastric benign and malignant placeholder and determine the best cut-off point;the latter group consisted of 55 cases of Solitary pulmonary nodules(SPN) with HRCT and PET/CT data. Two types of data were analyzed by ROC analysis software (ROCKIT). The ＂gold standard＂ of diagnostic data was pathological results of significance(α = 0.05). Results:Area under the ROC curve(Az) of the former diagnostic tests was 0.963 5;the parameters a was 2.347 6,and the relevant parameter b was 0.844 7;Youden index corresponding to the maximum cut-off point was 1.915. The area under the ROC curve obtained by the latter diagnostic experiments HRCT and PET/CT were 0.812 7 and 0.959 0,respectively. The correlation coefficient was 0.827 9. Z test of the area under the curves of both group got the one-tailed P value of 0.000 1. Conclusion:The dual-normal parameters model combined with ROC curve analysis software ROCKIT could systematically analyze the imaging diagnostic data. ROC evaluation can be achieved in a single diagnostic test for continuous data to determine the optimum operating point and ordered categorical data on the diagnostic performance comparison.