6 research outputs found
Lesion texture on T2WI and <i>IDH1</i> mutation status.
<p><i>IDH1</i> wild type (wt) gliomas showed statistically lower Shannon entropy on T2WI than <i>IDH1</i> mutated (mt) gliomas <b>(A)</b>. This finding was confirmed by the fact that T2WI Shannon entropy showed an AUC of 0.72 in ROC curve analysis with a <i>p</i> value as low as 0.007 <b>(B)</b>. Lesion border sharpness evaluated by Prewitt filtering of the image could not predict the <i>IDH1</i> mutation status of the tumor <b>(C</b> and <b>D)</b>. Values are presented as mean ± 2SD for <b>(A) (C)</b> and <b>(D)</b>.</p
Representative cases.
<p>Representative cases that illustrates the relationship between radiologist’s readings and calculated texture metrics are shown. The upper panel shows the heterogeneity of the lesion assessed by T2 entropy and the lower shows tumor boarder sharpness assessed by Prewitt filtering.</p
Image analysis workflow.
<p>The workflow for image analysis is presented. A high-intensity lesion on T2WI was first segmented in 3-dimensions, creating a voxels-of-interest (VOI). This VOI was applied to the original T2WI in a 256 level gray scale in order to calculate the Shannon entropy of the entire VOI. After the original T2WI was filtered using Prewitt filtering, the rim of the VOI (VOIrim) was applied to the edge enhanced image and the sharpness of the lesion border was calculated, reporting the edge mean and edge median values of the VOIrim.</p
Lesion texture on T2WI and <i>IDH1</i> mutation status.
<p><i>IDH1</i> wild type (wt) gliomas showed statistically lower Shannon entropy on T2WI than <i>IDH1</i> mutated (mt) gliomas <b>(A)</b>. This finding was confirmed by the fact that T2WI Shannon entropy showed an AUC of 0.72 in ROC curve analysis with a <i>p</i> value as low as 0.007 <b>(B)</b>. Lesion border sharpness evaluated by Prewitt filtering of the image could not predict the <i>IDH1</i> mutation status of the tumor <b>(C</b> and <b>D)</b>. Values are presented as mean ± 2SD for <b>(A) (C)</b> and <b>(D)</b>.</p
ROC analysis of image texture metrics.
<p>ROC curve analysis proved that Shannon entropy on T2WI was a reliable indicator for discrimination of homogenous and heterogeneous lesions identified by a neuro-radiologist <b>(A)</b>. ROC curve analysis also proved that both Edge mean <b>(B)</b> and median <b>(C)</b> values were promising indicators for discrimination of lesions with vague and well defined borders and both Edge mean and median values performed in a comparable manner.</p