Article Figures & Data
Figures
- Fig 1.
Hierarchic tree classification scheme.
- Fig 2.
A 71-year-old man with a glioblastoma in the left thalamus. A, Axial contrast-enhanced T1-weighted image shows solid enhancement. B, FLAIR image demonstrates hyperintense abnormalities, extending from the thalamus to the occipital lobe (not shown at this section level). C, CBV map demonstrates elevated blood volume of the enhancing part (rCBVmax = 6.52). D, ADC map shows restricted diffusion of the enhancing part (0.75 × 10−3/mm2/s). E−G, FA (E), CL (F), and CP (G) from the enhancing part (0.18, 0.15, and 0.15, respectively) are higher than those for brain metastasis (not shown) and PCL (not shown). H, CS from the enhancing portion (0.68) is lower compared with brain metastasis and PCL.
- Fig 3.
Boxplots of diffusion (A and B) and perfusion (C) characteristics in brain metastases (gray), glioblastomas (white), and PCLs (dotted). The solid line inside the box represents the median value, while the edges represent the 25th and 75th percentiles. Straight line (bar) on each box indicates the range of data distribution. Circles represent outliers (values >1.5 box length from the 75th and 25th percentile). The asterisk above the gray or dotted box indicates a significant difference (P < .05) for glioblastomas versus metastases or glioblastomas versus PCLs, respectively. The asterisk above a horizontal line between gray and dotted boxes indicates a significant difference (P < .05) between metastases and PCLs.
- Fig 4.
ROC curves of the imaging parameters with high predictive power from the enhancing part as well as the LRM for levels 1 (A) and 2 (B) of decision tree steps (Fig 1). LRM of ADC, CS from ER, and rCBV from the IPR were the best predictors for differentiation of glioblastomas from nonglioblastomas with AUC = 0.938 (A), whereas a combination of ADC from the ER and CP from the IPR was the best model for distinguishing lymphomas from metastases with AUC = 0.909 (B).
- Fig 5.
Scatterplot of FA and CS from the enhancing region of the glioblastomas (blue square) and nonglioblastomas (purple square). There is a strong negative correlation between FA and CS (r = 0.99).
Tables
- Table 1:
Sensitivity and specificity of imaging parameters with high predictive power in differentiation of glioblastomas from nonglioblastomas using ROCa
Parameter ROI Cutoff Value Sensitivity Specificity AUC ADC (10−3mm2/s) ER 1.16 0.62 0.81 0.68 FA ER 0.11 0.85 0.78 0.84 IPR 0.15 0.85 0.73 0.81 DPR 0.15 0.62 0.63 0.63 CL ER 0.10 0.62 0.93 0.79 IPR 0.12 0.69 0.63 0.70 CP ER 0.08 0.85 0.56 0.78 IPR 0.12 0.81 0.66 0.76 DPR 0.09 0.81 0.51 0.66 CS ER 0.80 0.85 0.73 0.82 IPR 0.73 0.69 0.76 0.79 DPR 0.73 0.54 0.68 0.61 rCBV IPR 1.20 0.96 0.46 0.71 rCBVmax ER 6.48 0.54 0.80 0.67 IPR 3.88 0.88 0.58 0.71 a P < .20, Wald test.
- Table 2:
Sensitivity and specificity of imaging parameters with high predictive power in differentiation of brain metastases from PCLs using ROCa
Parameter ROI Cutoff Value Sensitivity Specificity AUC ADC (10−3mm2/s) ER 0.85 0.91 0.56 0.78 FA IPR 0.13 0.68 0.69 0.63 DPR 0.15 0.50 0.81 0.65 CL DPR 0.13 0.77 0.50 0.62 CP IPR 0.11 0.64 0.94 0.76 CS IPR 0.76 0.68 0.81 0.67 DPR 0.74 0.55 0.75 0.63 rCBV ER 2.25 0.59 0.75 0.69 DPR 0.66 0.40 0.93 0.66 rCBVmax ER 4.61 0.73 0.69 0.70 DPR 3.19 0.82 0.50 0.63 a P < .20, Wald test.
True Histologic Type Classification % Correct Glioblastomas Metastases PCLs Glioblastomas (n = 26) 22 4 84.6 Metastases (n = 25) 3 19 3 76 PCLs (n = 16) 4 12 75
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