Julia Cluceru^{1}, Sarah Nelson^{1}, Annette Molinaro^{1}, Joanna Phillips^{1}, Marram Olson^{1}, Marisa LaFontaine^{1}, Angela Jakary^{1}, Devika Nair^{1}, Soonmee Cha^{1}, Susan Chang^{1}, and Janine Lupo^{1}

Despite previous research on physiological and metabolic MR imaging techniques with standard clinical anatomical MRI of patients with recurrent glioma, there is still no one parameter that can differentiate recurrent glioblastoma (rGBM) from treatment-induced effects (TxE) with high enough accuracy to be used clinically. We assessed the value of incorporating anatomical, perfusion-weighted, diffusion-weighted, and spectroscopic imaging parameters to identify TxE in patients suspected of rGBM. nPH from DSC perfusion-weighted imaging and Choline-to-NAA Index from MR spectroscopic imaging were found to be the most related to pathological markers of tumor and TxE.

Methods

Methods

*Patients & Samples:* 48 patients with an original diagnosis of high-grade glioma were included in this study. Each patient was recruited prior to resection for suspected rGBM. A total of 100 samples were used for analysis. Each sample was evaluated for the presence of tumor (tumors score 0-3) and ki67 for cellular proliferation.

*Image Acquisition: *Patients were scanned on a 3T GE scanner with an 8-channel head coil. Standard anatomic imaging included T2-weighted FLAIR and FSE as well as T1-weighted pre- and post-gadolinium contrast images. Diffusion-weighted imaging (DWI), DSC perfusion-weighted imaging with T2*-weighted EPI images (TR/TE/flip angle = 1250–1500/35–54ms/30–35degrees, 128×128matrix, slice thickness=3–5mm, 60–80 timepoints), and 3D MRSI using PRESS volume localization and VSS pulses for lipid signal suppression (TR/TE =1104/144ms, FOV=16×16×16cm3, nominal voxel size=1×1×1cm3) were also acquired.

*Image Processing*: After aligning all images, maps of the normalized apparent diffusion coefficient (nADC) and normalized fractional anisotropy (nFA) were generated on a voxel-by-voxel basis after TOPUP distortion correction from the DWI. Normalized cerebral blood volume (nCBV), percent deltaR2* signal recovery (recov), and normalized deltaR2* peak height (nPH) were calculated from an average curve at the biopsy location using software developed by our lab. Lactate-edited MRSI data were reconstructed and the signal from the individual channels combined to quantify lactate (Lac) and lipid (Lip) levels. The z-scores of the Cho-to-NAA index (CNI) and the Cho-to-Cr index (CCRI) were generated from a linear regression–based algorithm. [2]

*Statistical Analyses:* Three outcomes were explored for their utility in distinguishing TxE from rGBM: 1) A binomial tumor score binning TS 0 with TS 1 and TS 2 with TS 3; 2) A multinomial tumor score 0 through 3; and 3) A continuous score of the proliferation index Ki-67. Wilcoxon rank sum tests or Kruskal-Wallis rank tests were used to evaluate significance between low and high TS or among tumor score (0-3) categories for each imaging feature. For the binomial outcome analyses, generalized equation estimations (GEE) were implemented using the geepack package in R with patient as a random effect [3], while the multgee package was used for multinomial outcomes. Linear mixed-effects models were employed to relate each imaging feature to ki67 using the nlme package in R. For all models, univariate analyses were first performed for each imaging metric and features with p<0.2 were included in the multivariate model. Step-wise model selection was performed on significant variables and the model with the smallest AIC was selected.

*Tumor Score (high/low): *Figure1A shows the differences in imaging metrics among tumor scores. nPH, nCBV, and CNI significantly distinguished between low and high tumor scores. After iterative removal of insignificant features in the multivariate analysis, only nPH and CNI remained as predictors (Table 1).

*Tumor Score (0-3): *Figure 1B shows differences among tumor score as an ordinal outcome. Although nFA and %recov showed trends with increasing TS, only nPH and nCBV were significant. The significance values of the coefficients that remain in the final model are listed in Table 1 and generally reflect the results we observe in the Kruskal-Wallis analysis, with the exception of CCRI, which impacts the model more than the initial evaluation suggests.

*ki-67:* Figure 1C shows a significant correlation between Ki-67 and nADC, nPH, nCBV and CNI. Univariate linear mixed models for each imaging feature resulted in an initial multivariate model with CCrI, CNI, nPH, nCBV and nADC included as covariates. After step-wise model selection, CNI and nPH remained in the final model, with coefficients listed in Table 1.

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Table 1. Descriptions of the model parameters from each analysis, their coefficients, corresponding statistics, and coefficient p-values.

Figure 1. Box plots and scatter plots describing the relationship of each outcome to significant parameters. Most significant features for A) binomial differentiation per the Wilcoxon rank sum test, B) multinomial differentiation per the Kruskal-Wallis rank test, and C) linear differentiation per the spearman correlation coefficient rho and corresponding p-value.