Objectives: To evaluate the diagnostic performance of texture analysis (TA) applied on non-contrast-enhanced susceptibility-weighted imaging (SWI) to differentiate acute (enhancing) from chronic (non-enhancing) multiple sclerosis (MS) lesions.
Methods: We analyzed 175 lesions from 58 patients with relapsing-remitting MS imaged on a 3.0 T MRI scanner and applied TA on T2-w and SWI images to extract texture features. We evaluated the presence or absence of lesion enhancement on T1-w post-contrast images and performed a computational statistical analysis to assess if there was any significant correlation between the texture features and the presence of lesion activity. ROC curves and leave-one-out cross-validation were used to evaluate the performance of individual features and multiparametric models in the identification of active lesions.
Results: Multiple TA features obtained from SWI images showed a significantly different distribution in acute and chronic lesions (AUC, 0.617-0.720). Multiparametric predictive models based on logistic ridge regression and partial least squares regression yielded an AUC of 0.778 and 0.808, respectively. Results from T2-w images did not show any significant predictive ability of neither individual features nor multiparametric models.
Conclusions: Texture analysis on SWI sequences may be useful to differentiate acute from chronic MS lesions. The good diagnostic performance could help to reduce the need of intravenous contrast agent administration in follow-up MRI studies.
Key points: • Texture analysis applied on SWI sequences may be useful to differentiate acute from chronic multiple sclerosis lesions • The good diagnostic performance could help to minimize the need of intravenous contrast agent administration in follow-up MRI studies.
Keywords: Contrast agent; Logistic models; Magnetic resonance imaging; Multiple sclerosis.