Computer-aided diagnostic characterization (CADc) aims to support medical imaging decision making by objectively rating the radiologists ' subjective, perceptual opinions of visual diagnostic characteristics of suspicious lesions. This research uses the publicly available Lung Image Database Consortium (LIDC) collection of radiologists ' outlines of nodules and ratings of boundary and shape characteristics: spiculation, margin, lobulation, and sphericity. The approach attempts to reduce the observed disagreement between radiologists on the extent of nodules by combining their spatial opinion using probability maps to create regions of interest (ROIs). From these ROIs, images features are extracted and combined using machine learning models to predict a combined opinion, the median rating and a thresholded, binary version of their diagnostic characteristics. The results show slight to fair agreement—linear-weighted Kappa—between the CADc models and median radiologist opinion for the full scale five-level rating and fair to moderate agreement using a binary version of the median radiologist opinion
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