Multiparametric Evaluation in Differentiating Glioma Recurrence from Treatment-Induced Necrosis Using Simultaneous 18F-FDG-PET/MRI: A Single-Institution Retrospective Study

A. Jena; S. Taneja; A. Jha; N.K. Damesha; P. Negi; G.K. Jadhav; S.M. Verma; S.K. Sogani

doi:10.3174/ajnr.A5124

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Fig 1.
PET/MR imaging of a 73-year-old man with posttreatment (operation, radiation therapy, and chemotherapy) right temporoparietal glioblastoma multiforme with suspected recurrence proved to be treatment-induced necrosis on 11-month PET/MR imaging follow-up. Axial T1-weighted postcontrast image (A) shows an enhancing lesion along the margins of the operated bed with a freehand ROI drawn defining the enhancing component, which was copied and pasted on the FDG image (B), PET/MR fused image (C), CBV map (D), and ADC map (E) to derive SUV_max and SUV_mean, CBV_mean, and ADC_mean show no focal increased FDG uptake, CBV, and diffusion restriction on the ADC map. Multivoxel ¹H-MR spectroscopy (F) along the enhancing margin shows no increased Cho/Cr ratio.
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Fig 2.
PET/MR images of a 49-year-old woman with posttreatment (operation and radiaton therapy) left frontotemporal anaplastic oligodendroglioma that proved to be a recurrence on histopathologic examination (glioblastoma multiforme with an oligodendroglial component; World Health Organization grade IV with a large area of necrosis). Axial T1-weighted postcontrast image (A) shows an enhancing lesion in the tumor bed and involving the corpus callosum. Freehand ROI drawn defining the enhancing component of the lesion and copied and pasted on the FDG image (B), the PET/MR fused image (C), the CBV map (D), and the ADC map (E) to derive SUV_max and SUV_mean, CBV_mean, and ADC_mean shows increased FDG uptake, CBV, and diffusion restriction in the ADC map. Multivoxel ¹H-MR spectroscopy (F) obtained on the FDG avid enhancing area shows an increased Cho/Cr ratio. The enhancing region anterior to the target lesion has no FDG uptake and no increased CBV, and diffusion restriction represents necrosis (white arrow).
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Fig 3.
Box-and-whisker plots comparing rCBV_mean (A), ADC_mean (B), Cho/Cr (C), and TBR_mean (D) between the glioma recurrence and treatment-induced necrosis. Whiskers represent the range of data; boxes represent the distance between the first and third quartiles.
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Fig 4.
Receiver operating characteristic curves with their respective AUC values of MR imaging parameters (A) showing the high diagnostic performance of Cho/Cr in the detection of glioma recurrence. With multivariate ROC analysis, the ROC curve and AUC of all 3 MR imaging parameters combined show a significant increment in AUC over the individual MR imaging parameters (B). ROC curves with their respective AUC values of FDG parameters (C) show the high diagnostic performance of TBR_mean in the detection of glioma recurrence. Multivariate ROC analysis, ROC curve, and AUC of the best performing FDG-PET/MR imaging combination of ADC_mean, Cho/Cr, and TBR_mean show a significant increment over individual MR imaging or PET parameters (D).

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Table 1:

Patient characteristics

Characteristic	Value
Age (mean) (yr)	50 ± 12
Sex (No. of patients) (%)
Male	29 (82.8)
Female	6 (17.2)
Primary histopathology (No. of lesions) (%)
Glioblastoma	17 (41.5)
Anaplastic astrocytoma	9 (21.9)
Anaplastic oligodendroglioma	4 (9.8)
Oligodendroglioma	7 (17.1)
Others	4 (9.7)
WHO classification (No. of lesions) (%)
Grade II	9 (22.0)
Grade III	13 (31.7)
Grade IV	19 (46.3)
Primary site of glioma (No. of lesions) (%)
Frontal	15 (36.6)
Parietal	2 (4.9)
Temporal	10 (24.4)
Multilobar	14 (34.1)
Primary treatment (No. of patients) (%)
Operation+radiation therapy	6 (17.2)
Operation+radiation therapy+chemotherapy	29 (82.8)
Final diagnosis (No. of lesions) (%)
Recurrence	30 (73.2)
Histopathology	21 (51.2)
Clinicoradiologic follow-up	9 (22.0)
Treatment-induced necrosis	11 (26.8)
Histopathology	2 (4.9)
Clinicoradiologic follow-up	9 (21.9)

Note:—WHO indicates World Health Organization.

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Table 2:

Diagnostic performance of individual parameters in the detection of glioma recurrence^{^a}

	rCBV_mean ≥ 1.709	ADC_mean ≤ 1507	Cho/Cr ≥ 1.405	TBR_max ≥ 1.579	TBR_mean ≥ 1.179
Sensitivity	82.8%	86.7%	100.0%	93.3%	90.0%
Specificity	63.6%	54.5%	66.7%	72.7%	81.8%
PPV	85.7%	83.9%	88.9%	90.3%	93.1%
NPV	58.3%	60.0%	100.0%	80.0%	75.0%
Accuracy	77.5%	78.0%	90.9%	87.8%	87.8%
AUC±SE	0.68 ± 0.111	0.752 ± 0.015	0.861 ± 0.08	0.827 ± 0.078	0.888 ± 0.059
P value	.008^{^b}	.013^{^b}	<.001^{^b}	<.001^{^b}	<.001^{^b}

Note:—PPV indicates positive predictive value; NPV, negative predictive value; SE, standard error.
↵a The ADC_mean value is expressed as ×10⁻⁶mm²/s and rCBV_mean. Cho/Cr, TBR_max, _and TBR_mean are ratios and hence unitless. P values mentioned are generated while calculating the AUC from the ROC analysis.
↵b P values of rCBV_mean, ADC_mean, Cho/Cr, TBR_max, and TBR_mean are <.05 and hence are statistically significant. P value reflects the significance of the ROC analysis of individual parameters represented in the table.

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Table 3:

Multivariate ROC analysis showing AUC±SE values for various combinations of FDG-PET and MRI parameters in the detection of glioma recurrence^{^a}

	rCBV_mean (0.680 ± 0.011)	ADC_mean (0.752 ± 0.085)	Cho/Cr (0.861 ± 0.080)	ADC_mean + rCBV_mean (0.781 ± 0.079)	ADC_mean + Cho/Cr (0.912 ± 0.051)	rCBV_mean + Cho/Cr (0.860 ± 0.083)	ADC_mean + rCBV_mean + Cho/Cr (0.913 ± 0.053)
ADC_mean (0.752 ± 0.085)	0.781 ± 0.079
Cho/Cr (0.861 ± 0.080)	0.860 ± 0.083	0.912 ± 0.051		0.913 ± 0.053^{^b}
TBR_max (0.827 ± 0.078)	0.831 ± 0.081	0.848 ± 0.072	0.894 ± 0.059	0.850 ± 0.073	0.935 ± 0.044	0.889 ± 0.061	0.932 ± 0.046
TBR_mean (0.888 ± 0.059)	0.884 ± 0.063	0.888 ± 0.058	0.935 ± 0.044	0.897 ± 0.057	0.935 ± 0.046^{^b.^c}	0.928 ± 0.047	0.932 ± 0.048^{^b}

↵a Each entry represents the AUC±SE of a combination of parameters mentioned in the respective rows and columns. Values in parentheses represent AUC±SE of that particular parameter or combination. Blank cells are left to avoid repetition of values.
↵b A combination of FDG-PET and MR imaging parameters has a better AUC±SE than a combination of MR imaging parameters.
↵c Note that the maximum AUC was achieved with a combination of ADC_mean, Cho/Cr, and TBR_mean.