Usefulness of Texture Analysis in Differentiating Transient from Persistent Part-solid Nodules(PSNs): A Retrospective Study
Figure 4
C-statistic analysis of multiple logistic regression models in discriminating transient PSNs from persistent PSNs.
There were three combinations of independent predictors in the differentiation between transient and persistent PSNs. The highest area under the curve (AUC) was achieved for the combination of clinical, thin-section CT and texture analysis (AUC = 0.929 ? 0.0272). The AUC of clinical and thin-section CT predictors alone (AUC = 0.790 ? 0.0522) was not significantly different from the AUC of computer-aided quantified pixel value predictors alone (AUC = 0.831 ? 0.0503) (P = 0.598). However, the AUC of the combination of clinical, thin-section CT and computer-aided quantified pixel value predictors was significantly higher than that of either the clinical and thin-section CT or the texture analysis alone (P=0.004 and P=0.04). AUCs are shown as means ? standard deviations.