Evaluation Of Solitary Pulmonary Nodule With The Maximum Likehood Method

Purpose: To analyze various features of solitary pulmonary nodule (SPN) on HRCT, and with the application of the maximum likehood method, study the diagnosis of 3 kinds of SPNs (namely the peripheral lung cancer, hamartoma, and tuberculoma) using these features, and hence the usefulness of them in differential diagnosis. Comparisons are made on the diagnostic results from the using of the traditional method, the maximum likehood method, and the artificial neural network (ANN) system to investigate the diagnostic performance of these methods.Materials and methods: From clinically and pathologically proved cases, collect 150 cases of SPNs (including for each of the 3 kinds of SPNs 50 cases, and with their diameters all not more than 3cm). All are treated with ordinary CT, with the focus being scanned using HRCT (with the scan thickness of 1.25-3mm, and the bone construction). For a systematic statistic analysis, 10 features of SPN on HRCT are selected, including namely calcification, fat, vacuole sign, cavication, air bronchograms, lobulation, spiculation, vascular convergence, pleural indentation, and satellite. In the application of the maximum likehood method, first transform the occurrence probabilities for all the features of a nodule into scores. Then counting the judging scores according to the nodule characteristics of the patient, and finally determine the type of the nodule based on this score value.With all of the 3 SPNs, randomly chose 25 cases from each to collectively form the training data set for the ANN testing system established with the ANN Toolbox in MATLAB6.1. Taking the rest of the data (which has not been used as training data) as testing data, conduct the diagnosis analysis using the ANN already trained.Results: The correct rate of the maximum likehood method for the diagnosis of peripheral lung cancer, hamartoma and tuberculoma is 86%, 92%, and 90% respectively, with theaverage accurate diagnosis rate being 89.3% which is higher than the traditional method being 82%, but there is no significant difference in statistical study (P>0.05). The most suggestive features for peripheral lung cancer are vacuole sign, lobulation, air bronchograms, and vascular convergence, of which the revealing percentage is 95%, 70.3%, 66.7% and 55.7% respectively; while the dominant features for hamartoma are fat composition, and calcification, with their revealing percentage being 100% and 31.2% respectively; and those for tuberculoma are cavication, satellite, calcification composition, and pleural indentation, with the revealing percentage being 100%, 91.7%, 55.7% and 52.1% respectively. The correct rate of ANN for the diagnosis of the 3 kinds SPNs (each having 25 cases) is 80%, 82% and 84% respectively, with an average accurate diagnosis rate being 82%. There is no significant difference in statistical study (P>0.05).Conclusions: The maximum likehood method is an useful instrument for the statistical diagnosis of SPNs. Comparing with the traditional method, it results in higher accurate rate for all the 3 kinds of SPNs usually seen, and can be made use of in the guiding of daily method. ANN is a forefront in the application of AI in computer aided diagnosis (CAD). By using digitalized statistical diagnosis, it provides the intelligentized foreground of radiology.