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- W2347837224 abstract "Objective To establish a new-type method in differentiating benign from malignant solitary pulmonary nodule (SPN) on thin-slice CT using artificial neural networks (ANN) theory, and to evaluate its diagnostic value and the aided role to different level radiologists. Methods Two hundred cases with pathologically proved SPN by operation or biopsy (small peripheral lung cancer 135, benign nodules 65) were collected; 3 clinical characteristics and 9 thin-slice CT characteristics were observed and quantified the qualitative characteristics. About 70% of all cases (140 cases) were selected randomly to form training samples, on which ANN model were built and compared with Logistic regression obtained by SPSS. The diagnostic consistent rates and areas under ROC of the two models were then calculated. The trained ANN model was used to test the other 60 cases, and the areas under ROC before and after ANN were analyzed in three different level radiologists. Results The total consistent rate of ANN was greater than that of Logistic model (98.0% vs 86.0%, P0.001). Areas under ROC curve were 0.996±0.004 and 0.936±0.017, respectively, and the difference between the two models was significant (P0.001). The areas under ROC curve in ANN model, the junior, middle and senior radiologists without ANN were 0.954, 0.737, 0.813 and 0.874, respectively, and the difference between ANN model and the junior, middle radiologists were significant (P=0.001, P=0.007, respectively), while the difference between ANN model and the senior radiologists was not significant (P=0.070). The areas under ROC curve in the junior, middle and senior radiologists with ANN were 0.920, 0.938, and 0.952, respectively, and the performance of junior, middle and senior radiologists with ANN were significantly improved (P0.001, P=0.001, P=0.039, respectively). The difference of ability to diagnose SPN among different level radiologists with ANN was not significant (P=0.614 for junior-middle, P=0.369 for junior-senior, P=0.645 for middle-senior). Conclusion ①The types of elementary CT signs of SPN in the study can be used to establish ANN model. ②ANN model is superior to traditional logistic model in differentiating primary benign from malignant SPN on thin-slice CT. ③ANN model plays an effective role in aiding different level radiologists to make decisions." @default.
- W2347837224 created "2016-06-24" @default.
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- W2347837224 date "2008-01-01" @default.
- W2347837224 modified "2023-09-25" @default.
- W2347837224 title "Diagnostic value of artificial neural network in solitary pulmonary nodules" @default.
- W2347837224 hasPublicationYear "2008" @default.
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