Skip to main content

Table 5 Variables with independent predictive value obtained from combining different magnetic resonance techniques (multivariate logistic regression) in gliomas

From: Added value of advanced over conventional magnetic resonance imaging in grading gliomas and other primary brain tumors

Technique N Predictor variables OR 95% CI P Coefficient (β) Standard error AUC Sensitivity (%) Specificity (%) PPV (%) NPV (%)
MRI 118 Enhancement 58.82 9.35-333.33 <0.0001 4.06 0.93 0.940 97.8 76.9 93.7 90.9
Necrosis 13.89 2.91-66.67 0.001 2,63 0.80
MRI 67 Enhancement 25 3.60-166.67 0.001 2.46 0.99 0.940 96.2 64.3 91.1 81.8
PWI
DWI Necrosis 8.26 1.53-43.48 0.014 2.11 0.86
MRS
MRI 110 Enhancement 50 8.13-333.33 <0.0001 3.94 0.94 0.940 97.6 76 93.3 90.5
PWI
DWI
Necrosis 13.89 2.91- 66.67 0.001 2.64 0.80
  1. AUC, area under the receiver operating characteristic curve; CI, confidence interval; DWI, diffusion-weighted imaging; MRI, magnetic resonance imaging; MRS, MR spectroscopy; N, number of cases from which each classifier was constructed; PWI, perfusion-weighted imaging; rADC, relative apparent diffusion coefficient; OR, odds ratio; PPV, positive predictive value; NPV, negative predictive value.