Response to neoadjuvant chemoradiotherapy for locally advanced rectum cancer: Texture analysis of dynamic contrast-enhanced MRI

2018 ◽  
Vol 49 (3) ◽  
pp. 885-893 ◽  
Author(s):  
Hai-hua Zou ◽  
Jing Yu ◽  
Yun Wei ◽  
Jiang-fen Wu ◽  
Qing Xu
Keyword(s):  
Texture Analysis ◽  
Locally Advanced ◽  
Neoadjuvant Chemoradiotherapy ◽  
Rectum Cancer ◽  
Dynamic Contrast Enhanced Mri ◽  
Dynamic Contrast Enhanced ◽  
Enhanced Mri ◽  
Contrast Enhanced ◽  
Contrast Enhanced Mri

Intergrating conventional MRI, texture analysis of dynamic contrast-enhanced MRI, and susceptibility weighted imaging for glioma grading

2018 ◽  
Vol 60 (6) ◽  
pp. 777-787 ◽  
Author(s):  
Chun-Qiu Su ◽  
Shan-Shan Lu ◽  
Qiu-Yue Han ◽  
Mao-Dong Zhou ◽  
Xun-Ning Hong
Keyword(s):  
Texture Analysis ◽  
Susceptibility Weighted Imaging ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced Mri ◽  
Dynamic Contrast Enhanced ◽  
Conventional Mri ◽  
Enhanced Mri ◽  
Glioma Grading ◽  
Contrast Enhanced ◽  
Contrast Enhanced Mri

Background The application of conventional magnetic resonance imaging (MRI) in glioma grading is limited and non-specific. Purpose To investigate the application values of MRI, texture analysis (TA) of dynamic contrast-enhanced MRI (DCE-MRI) and intratumoral susceptibility signal (ITSS) on susceptibility weighted imaging (SWI), alone and in combination, for glioma grading. Material and Methods Fifty-two patients with pathologically confirmed gliomas who underwent DCE-MRI and SWI were enrolled in this retrospective study. Conventional MRIs were evaluated by the VASARI scoring system. TA of DCE-MRI-derived parameters and the degree of ITSS were compared between low-grade gliomas (LGGs) and high-grade gliomas (HGGs). The diagnostic ability of each parameter and their combination for glioma grading were analyzed. Results Significant statistical differences in VASARI features were observed between LGGs and HGGs ( P < 0.05), of which the enhancement quality had the highest area under the curve (AUC) (0.873) with 93.3% sensitivity and 80% specificity. The TA of DCE-MRI derived parameters were significantly different between LGGs and HGGs ( P < 0.05), of which the uniformity of Ktrans had the highest AUC (0.917) with 93.3% sensitivity and 90% specificity. The degree of ITSS was significantly different between LGGs and HGGs ( P < 0.001). The AUC of the ITSS was 0.925 with 93.3% sensitivity and 90% specificity. The best discriminative power was obtained from a combination of enhancement quality, Ktrans- uniformity, and ITSS, resulting in 96.7% sensitivity, 100.0% specificity, and AUC of 0.993. Conclusion Combining conventional MRI, TA of DCE-MRI, and ITSS on SWI may help to improve the differentiation between LGGs and HGGs.

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