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17 March 2008 Comparison of computer-aided diagnosis performance and radiologist readings on the LIDC pulmonary nodule dataset
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One challenge facing radiologists is the characterization of whether a pulmonary nodule detected in a CT scan is likely to be benign or malignant. We have developed an image processing and machine learning based computer-aided diagnosis (CADx) method to support such decisions by estimating the likelihood of malignancy of pulmonary nodules. The system computes 192 image features which are combined with patient age to comprise the feature pool. We constructed an ensemble of 1000 linear discriminant classifiers using 1000 feature subsets selected from the feature pool using a random subspace method. The classifiers were trained on a dataset of 125 pulmonary nodules. The individual classifier results were combined using a majority voting method to form an ensemble estimate of the likelihood of malignancy. Validation was performed on nodules in the Lung Imaging Database Consortium (LIDC) dataset for which radiologist interpretations were available. We performed calibration to reduce the differences in the internal operating points and spacing between radiologist rating and the CADx algorithm. Comparing radiologists with the CADx in assigning nodules into four malignancy categories, fair agreement was observed (κ=0.381) while binary rating yielded an agreement of (κ=0.475), suggesting that CADx can be a promising second reader in a clinical setting.
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Luyin Zhao, Michael C. Lee, Lilla Boroczky, Victor Vloemans, and Roland Opfer "Comparison of computer-aided diagnosis performance and radiologist readings on the LIDC pulmonary nodule dataset", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69151I (17 March 2008);

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