Purpose: Evaluation of nonenhancing tumor (NET) burden is an important yet challenging part of brain tumor response assessment. This study focuses on using dual-echo turbo spin-echo MRI as a means of quickly estimating tissue T2, which can be used to objectively define NET burden.

Experimental Design: A series of experiments were performed to establish the use of T2 maps for defining NET burden. First, variation in T2 was determined using the American College of Radiology (ACR) water phantoms in 16 scanners evaluated over 3 years. Next, the sensitivity and specificity of T2 maps for delineating NET from other tissues were examined. Then, T2-defined NET was used to predict survival in separate subsets of patients with glioblastoma treated with radiotherapy, concurrent radiation, and chemotherapy, or bevacizumab at recurrence.

Results: Variability in T2 in the ACR phantom was 3% to 5%. In training data, ROC analysis suggested that 125 ms < T2 < 250 ms could delineate NET with a sensitivity of >90% and specificity of >65%. Using this criterion, NET burden after completion of radiotherapy alone, or concurrent radiotherapy, and chemotherapy was shown to be predictive of survival (Cox, P < 0.05), and the change in NET volume before and after bevacizumab therapy in recurrent glioblastoma was also a predictive of survival (P < 0.05).

Conclusions: T2 maps using dual-echo data are feasible, stable, and can be used to objectively define NET burden for use in brain tumor characterization, prognosis, and response assessment. The use of effective T2 maps for defining NET burden should be validated in a randomized, clinical trial. Clin Cancer Res; 21(19); 4373–83. ©2015 AACR.

Translational Relevance

This study provides a comprehensive evaluation of the use of T2 relaxation rates estimated from dual-echo turbo spin-echo images to objectively define nonenhancing tumor (NET) burden in patients with gliomas. Results suggest T2 measurements are relatively stable across scanners and may be useful for delineating NET from other tissues. Results also suggest that the use of T2-defined NET burden may be useful for prognostic and response assessment purposes.

The use of contrast-enhancing tumor burden has been the standard for brain tumor response assessment for more than 60 years; however, approximately 30% to 40% of patients are estimated to experience nonenhancing tumor (NET) progression prior to changes in contrast enhancement (1, 2). Although contrast enhancing tumor is thought to represent the most aggressive portion of the tumor (3, 4) and a large percentage of high-grade gliomas have a significant enhancing component (5), malignant gliomas are known to contain proportions of both neovascularized and infiltrative tumor (6, 7). Because a substantial proportion of treated tumors can have NET progression (1, 6), and progression of NET can lead to neurologic decline, there is an emergent need for consideration of NET burden in an updated response criteria in neuro-oncology, which was further outlined in the Jumpstarting Brain Tumor Drug Development Coalition and Food and Drug Administration (FDA) Workshop in January 2014 (8, 9).

Damadian (10) first documented distinct differences in proton relaxation rates between normal and cancerous tissues as early as 1971, which was also confirmed by various groups in the subsequent years (11–15). Clinical diagnosis and monitoring of brain tumors with MRI, however, remains reliant on the use of T1 or T2 “weighted” images, which express qualitative changes in the MR signal. T2 relaxometry can theoretically be performed using a simple modification to the standard T2-weighted MRI sequence that results in obtaining multiple images at various echo times (TE), which is essentially “free” information (i.e., no substantial added scan time). This information can in theory be used to create quantitative maps of T2, which is specific for various tissue types, including cancer, but relatively independent of MR acquisition parameters, such as TE. For example, an early study by Hoehn-Berlage and colleagues (16) correlated tumor tissue in a cat model with quantitative T2 maps at 4.7T and demonstrated that T2edema > T2tumor > T2normal WM, consistent with the current clinical observation of moderately hyperintense lesions on T2-weighted images being associated with NET (17). This difference in T2 relaxation rates between edema, tumor, and normal tissues has been characterized in both rodent models (18) and human tumors (19–21). In addition, studies have shown that T2 relaxation tends to be monoexponential (16), suggesting evaluation of NET may be possible with a single T2 parameter. We hypothesize that “effective” T2 relaxometry performed using the turbo spin-echo (TSE) may be useful for objectively defining NET tissue using a specific range of T2 values.

Accurate measurements of T2 relaxation times typically involve using relatively time inefficient multiecho Carr–Purcell–Meiboom–Gill (CPMG) sequences. Dual-echo TSE sequences are an attractive clinical alternative to CPMG sequences (22, 23), allowing for “effective” T2 estimation (T2eff) in clinically feasible scan times and clinically useful spatial coverage for a variety of anatomical locations (24–28). In this study, we examined the stability of T2eff estimates in a standard water phantom across a variety of scanners, used dual-echo TSE data from 50 patients with a range of glioma grades scanned on a variety of scanners and field strengths to define T2eff characteristics for various tissues, then applied this information to a cohort of newly diagnosed glioblastoma patients treated with radiotherapy, a cohort of newly diagnosed glioblastoma patients treated with concurrent radiotherapy and temozolomide, and a set of recurrent glioblastoma patients treated with bevacizumab to determine whether the volume of T2eff-defined NET can be used to predict progression-free survival (PFS) and overall survival (OS).

Summary of experiments

A series of experiments were performed in this study to establish the use of T2eff in defining NET burden for use in quantification of tumor burden and response assessment. First, the normal variation expected in T2eff estimates was determined on the American College of Radiology (ACR) standard water phantom using dual-echo TSE images collected for 16 different scanners and field strengths. Next, the sensitivity and specificity of T2eff in delineating NET from edema, necrotic tissue, and normal-appearing brain tissue were estimated in a training set of 50 glioma patients in order to define cutoffs for defining NET. Using thresholds defined by these training data, we examined an independent set of 25 newly diagnosed glioblastoma patients who were treated with radiotherapy, but no concurrent temozolomide and never were exposed to bevacizumab, to determine whether NET burden evaluated at this post-radiotherapy time point could predict PFS and OS. Then, another independent set of 35 glioblastoma patients treated with concurrent radiotherapy and temozolomide, but were never exposed to bevacizumab, were evaluated at the post-radiotherapy time point to determine whether NET burden prior to adjuvant therapy could predict PFS and OS. Finally, we examined a set of 24 recurrent glioblastoma patients treated with bevacizumab to determine whether the change in NET burden could be used to predict PFS and OS.

Phantom studies

A total of 16 MRI scanners spanning various field strengths and manufacturers were evaluated from 2010 to 2015 using the ACR accreditation phantom (Fig. 1). Dual-echo TSE data were acquired according to ACR guidelines (www.acr.org/∼/media/ACR/Documents/Accreditation/MRI/LargePhantomInstructions.pdf). In particular, dual-echo TSE data were obtained with TE1 = 20 ms, TE2 = 80 ms, repetition time (TR) = 2000 ms, field-of-view (FOV) = 25 cm, 11 slices, 5-mm slice thickness with interslice gap of 5 mm, number of excitations (NEX) = 1, and matrix size of 256 × 256. Quantification of T2eff was determined by

formula

where T2eff is the estimated effective T2 in the voxel at coordinate (x, y, z), TE1 and TE2 are the TEs of the first and second MR image, respectively, and S1 and S2 are the MR signal intensity in the voxel at coordinate (x, y, z) at the two TEs, respectively. The mean value of T2eff was determined for the entire phantom at slice 6 using the standard ACR slice prescription, corresponding to a slice containing relatively homogeneous signal intensity and no internal geometric structures.

Figure 1.

Treatment paradigms for testing the prognostic and predictive utility of T2eff-defined NET volumes. A, newly diagnosed glioblastoma patients treated with radiotherapy were evaluated 10 weeks following the start of radiation. B, newly diagnosed glioblastoma patients treated with concurrent radiation and temozolomide therapy followed by adjuvant temozolomide were evaluated 10 weeks following concurrent radiation and temozolomide, prior to the start of adjuvant temozolomide. C, patients with recurrent glioblastoma treated with bevacizumab were evaluated before and after the first dose of bevacizumab therapy.

Figure 1.

Treatment paradigms for testing the prognostic and predictive utility of T2eff-defined NET volumes. A, newly diagnosed glioblastoma patients treated with radiotherapy were evaluated 10 weeks following the start of radiation. B, newly diagnosed glioblastoma patients treated with concurrent radiation and temozolomide therapy followed by adjuvant temozolomide were evaluated 10 weeks following concurrent radiation and temozolomide, prior to the start of adjuvant temozolomide. C, patients with recurrent glioblastoma treated with bevacizumab were evaluated before and after the first dose of bevacizumab therapy.

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Patients

A total of 134 patients with gliomas who were evaluated using dual-echo TSE between 2000 and 2008 were retrospectively evaluated in this study. All patients gave informed written consent to be included in our institutional review board approved neuro-oncology database prior to this study. These 134 patients were broken into four groups for training and testing purposes: (i) 50 patients with various glioma grades used for training; (ii) 25 newly diagnosed glioblastoma patients treated with radiotherapy and no concurrent chemotherapy, none of which ever were exposed to bevacizumab; (iii) 35 newly diagnosed glioblastoma patients treated according to the current standard of care including radiotherapy with concurrent temozolomide (TMZ), followed by adjuvant TMZ; and 4) 24 recurrent glioblastoma patients treated with bevacizumab. Because this was a retrospective study, sample sizes were based solely on availability of data in patients that met the inclusion criteria outlined below.

Training set for defining NET.

A total of 50 glioma patients with obvious NET who were evaluated using dual-echo TSE between 2000 and 2008 were retrospectively evaluated. Patients were selected so the training set contained tumors with a variety of WHO grades, histologic subtypes, and were at various stages of their disease. A total of 34 of 50 (68%) patients were male, approximately half the patients (26/50, 52%) were scanned on a 1.5T MR scanner, and the other half (24/50, 48%) was scanned on a 3T scanner. A total of 32 of 50 (64%) patients had recurrent tumors. Approximately 15 of 50 tumors (30%) were World Health Organization (WHO) grade II astrocytomas, 15 of 50 tumors (30%) were WHO III anaplastic astrocytoma (9/15) or oligodendrogliomas (6/15), and 20 of 50 (40%) were WHO IV glioblastomas. To estimate T2eff values in specific tissues, circular regions of interest (ROI) 1 cm in diameter were placed in areas of obvious normal-appearing white matter (NAWM), edema, NET, and necrosis on T2-weighted images in these 50 patients. Regions of NET were defined as having a lower T2 signal intensity compared with CSF, architectural distortion of the gray–white matter junction, or significant mass effect, whereas edematous tissue was defined as having a higher T2 signal intensity approaching that of CSF, T2 hyperintensity respecting the gray–white matter junction, no significant mass effect, and presence of “finger-like” projections of high-intensity T2 signal within deep white matter, as defined previously by Pope and colleagues (17). The average T2eff for these regions were retained and used to define field strength–specific thresholds to accurately delineate NET from both edema and NAWM using ROC analysis.

Newly diagnosed glioblastoma patients treated with radiotherapy.

A total of 25 newly diagnosed glioblastoma patients [average age, 53.2; range, 24–78 years old; average Karnofsky performance status (KPS), 83; range, 70–100; 16 of 25 were male (64%)] treated with 60 Gy intensity-modulated radiotherapy (IMRT) with no concurrent chemotherapy who were evaluated following completion of IMRT using dual-echo TSE between 2000 and 2008 were retrospectively evaluated (Fig. 1A). None of the 25 patients were ever treated with bevacizumab during their clinical history. All 25 patients were treated with adjuvant chemotherapies, including isotretinoin (Accutane; Roche Pharmaceuticals; 100 mg/m2 orally for 21 consecutive days followed by 7 drug-free days), temozolomide (150 to 200 mg/m2 i.v. or orally for 5 days of a 28-day cycle), lomustine (CCNU, Bristol-Myers Squibb; 110 mg/m2 orally once every 6 weeks), or carboplatin (area under the plasma curve 4 mg/mL-min on day 1 of a 28-day cycle), until treatment failure.

Newly diagnosed glioblastoma patients treated with concurrent radiotherapy and temozolomide followed by adjuvant temozolomide.

A total of 35 newly diagnosed glioblastoma patients [average age, 58; range, 27 to 77 years old; average KPS, 85; range, 70–100; 27/35 were male (77%)], treated concurrently with IMRT and temozolomide followed by adjuvant temozolomide (TMZ) who were evaluated following completion of concurrent IMRT and TMZ using dual-echo TSE between 2000 and 2008, were retrospectively evaluated (Fig. 1B). Patients were treated with 60Gy IMRT with concomitant TMZ (75 mg/m2 orally or intravenously for 42 consecutive days) followed by adjuvant TMZ (150 mg/m2 orally or intravenously for 5 consecutive days in the first 28-day cycle, followed by 200 mg/m2 orally or intravenously for 5 consecutive days in the first 28-day cycle for a maximum of 6 cycles). Adjuvant TMZ was continued until disease progression, death, or completion of 6 cycles. A total of 8 of 35 patients (23%) were treated with bevacizumab during their clinical history, and 6 of the 8 patients were treated at first recurrence. Approximately 29 of the 35 patients (83%) were treated with chemotherapy at first recurrence, including isotretinoin (Accutane, Roche Pharmaceuticals; 100 mg/m2 orally for 21 consecutive days followed by 7 drug-free days, dose range across all patients = 160 to 200 mg; N = 9 patients), lomustine (CCNU, Bristol-Myers Squibb; 110 mg/m2 orally once every 6 weeks, dose range across all patients = 150 to 210 mg; N = 17 patients), or carboplatin (area under the plasma curve 4 mg/mL-min on day 1 of a 28-day cycle; N = 3 patients).

Recurrent glioblastoma treated with bevacizumab.

An independent sample of 24 patients with recurrent glioblastoma [average age, 56; range, 27–91 years old; average KPS, 82; range, 70–100; 12/24 were male (50%)] who failed IMRT and TMZ who also underwent evaluation at recurrence (baseline) and 2 to 4 weeks after the first administration of bevacizumab (10 mg/kg intravenously every 14 days) using dual-echo TSE between 2000 and 2008 were retrospectively evaluated (Fig. 1C). All patients were treated with bevacizumab every 14 days until disease progression or death.

Magnetic resonance imaging

Standard anatomical MR images including dual-echo TSE images for patients included in the training dataset were collected on one of the 1.5T or 3T MR scanners listed in Fig. 1. Standard anatomic images included an axial T1-weighted fast spin-echo sequence or magnetization-prepared rapid acquisition gradient-echo sequence (repetition time ms/TE ms/inversion time ms, 400–3,209/3.6–21.9/0–1,238; section thickness, 3–6.5 mm; intersection gap, 0–2.5 mm; number of signals acquired, one to two; matrix size, 176–512 × 256–512; and field of view, 24–25.6 cm) and a fluid-attenuated inversion-recovery sequence. In addition, axial T1-weighted images enhanced with gadopentetate dimeglumine (Magnevist; Berlex), 0.1 mmol/kg, and acquired shortly after contrast material injection, were matched to nonenhanced T1-weighted images obtained with similar sequence parameters. Dual-echo TSE images for the training dataset included both proton density/T2-weighted imaging pairs (e.g., TE1 = 5–10 ms, TE2 = 80–120 ms) or 2 sets of T2-weighted images (e.g., TE1 = 80–120 ms, TE2 = 160–200 ms). All therapeutic evaluations in this study were collected on a 1.5T MR scanner (Avanto or Sonata, Siemens Healthcare; Excite HDx or LX, GE Medical Systems) and all dual-echo TSE images had full brain coverage with TE1 = 9–15 ms, TE2 = 118–135 ms, TR = 4,000 ms, matrix size = 256 × 256, FOV = 240 mm, and slice thickness = 3 mm with no interslice gap.

Definition of disease progression

Progression was defined prospectively by the treating neuro-oncologists using a method consistent with the current RANO criteria (29). In particular, progression was defined if subsequent scans showed an increase in imaging-evaluable tumor (≥25% increase in the sum of enhancing lesions, new enhancing lesions >1 cm2, an unequivocal qualitative increase in NET, or an unequivocal new area of noncontrast enhancing tumor). Patients were required to have stable or decreasing contrast agent dose before partial or complete response could be determined. In addition, patients requiring increased dosage of steroids in order to maintain neurologic function, even in the absence of worsening on anatomical images, were considered to be stable, but required early reevaluation. Patients who experienced significant neurologic decline were also declared to have progressed at the time of irreversible decline. Landmark PFS was therefore defined as being the number of days between the posttreatment MRI scan (e.g., post-radiotherapy or post-bevacizumab) and declared progression. Landmark OS was defined as the number of days between the posttreatment MRI scan to death.

Automated segmentation and quantification of NET burden

Multiple studies have demonstrated that the T2 characteristics for NET fall between T2 measurements of NAWM and edema (i.e., T2effNAWM < T2efftumor < T2effedema; 16–21). The particular thresholds used for tumor segmentation were determined empirically from the 50 glioma patients as per the experiment described above. After defining these thresholds, a 3-step automated procedure was performed to segment regions of NET from surrounding tissues (Supplementary Fig. SA): (i) First, the skull was stripped and all tissue with T2eff greater than the threshold to delineate tumor and NAWM were isolated, effectively removing NAWM and retain tumor, edema, and necrosis. (ii) Next, one image voxel surrounding these regions was eroded in order to remove tissues on the border of edema and NAWM, for example, where a partial volume voxel may have T2eff characteristics similar to tumor. A whole brain mask was then applied to confine all ROIs inside the brain. (iii) Finally, NET was segmented from regions of edema within the ROIs using the empirical thresholds defined previously and a cluster-based algorithm using a single voxel nearest-neighbor algorithm was used to isolate the largest contiguous clusters with T2eff values consistent with NET within the brain. Cluster sizes greater than 0.1 cc confluent with the primary enhancing lesion were retained for subsequent analyses. A single investigator confirmed adequate automatic segmentation, including verification that clusters were isolated in regions that could contain tumor.

Statistical analyses

Phantom measurements.

To determine the stability of T2eff measurements in the ACR phantom over time, we examined the average coefficient of variance for 1.5T and 3T scanners independently over 3 years. A t test was used to compare average T2eff measurement values between 1.5T and 3T scanners.

Training data

For comparisons of T2eff measurements in various tissue types, a one-way ANOVA was used to examine T2eff measurements in NET across tumor grade, and a two-way ANOVA including interaction terms along with the Tukey test for multiple comparisons was used to examine differences in T2eff measurements across tissue types and field strengths. In addition, ROC analysis was used to determine the sensitivity and specificity for delineating various tissue types, including NET from edema and NET from NAWM for specific T2eff thresholds.

Newly diagnosed glioblastoma treated with radiotherapy.

For newly diagnosed glioblastoma patients treated with radiation alone followed by adjuvant chemotherapy, ROC analysis was used to determine whether the volume of NET burden (selected from ROC analysis of training data) could stratify patients who progressed 6 months as well as those who expired within 9 months following completion of radiotherapy. Log-rank univariate analysis applied to Kaplan–Meier data was used to determine whether patients with a large NET burden (from ROC analysis) had significantly shorter PFS and OS. A Cox multivariate proportional hazards model was then used to determine whether NET volume quantified using T2eff was an independent predictor of PFS and OS after including covariates including patient age and KPS.

Newly diagnosed glioblastoma treated with concurrent radiotherapy and temozolomide followed by adjuvant temozolomide.

For newly diagnosed glioblastoma patients treated with concurrent radiation and temozolomide followed by adjuvant temozolomide, ROC analysis was used to determine whether the volume of NET burden (selected from ROC analysis of training data) could stratify patients who progressed 6 months as well as those who expired within 9 months following completion of radiotherapy. Log-rank univariate analysis applied to Kaplan–Meier data was used to determine whether patients with a large NET burden (from ROC analysis) had significantly shorter PFS and OS. A Cox multivariate proportional hazards model was then used to confirm whether NET burden quantified using T2eff was an independent predictor of PFS and OS after including covariates including patient age and KPS.

Recurrent glioblastoma treated with bevacizumab.

For recurrent glioblastoma patients treated with bevacizumab, ROC analysis was used to determine whether the volume of NET burden (selected from ROC analysis of training data) could stratify patients who progressed 6 months as well as those who expired within 9 months following start of bevacizumab. Log-rank univariate analysis applied to Kaplan–Meier data was used to determine whether small changes in NET burden (from ROC analysis) had significantly shorter PFS and OS. A Cox multivariate proportional hazards model was then used to confirm that change in NET burden before and after bevacizumab was an independent predictor of PFS and OS after including covariates including patient age and KPS.

Evaluations of the ACR phantoms using the dual-echo TSE sequence showed reasonable stability over time, averaging a coefficient of variance of approximately 3.4% for 3T scanners and 4.5% for 1.5T scanners when evaluated over the last 3 years. The coefficient of variance of T2eff measurements across the 3T scanners was 3.1% and across all 1.5T scanners was 4.5%, suggesting long-term stability was similar to the stability across scanners at a single time point. T2eff measurements on 3T MR scanners were significantly lower than 1.5T scanners (Fig. 2; t test, P < 0.0001; T2eff in 3T = 79.3 ± 2.5 ms vs. 1.5T = 98.8 ± 4.4 ms), confirming the expected decrease in T2 relaxation times for increasing magnetic field strength.

Figure 2.

Dual-echo TSE estimates of T2eff obtained in an American College of Radiology (ACR) phantom for 16 different MRI scanners. Results show a significant difference in T2eff measurements in 3T (mean = 79.3 ms) versus 1.5T (mean = 98.8 ms) scanners.

Figure 2.

Dual-echo TSE estimates of T2eff obtained in an American College of Radiology (ACR) phantom for 16 different MRI scanners. Results show a significant difference in T2eff measurements in 3T (mean = 79.3 ms) versus 1.5T (mean = 98.8 ms) scanners.

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A total of 50 patients with various tumor grades containing regions of obvious NET as training data for delineation of NET from other tissues based on specific T2eff measurements. In general, T2eff maps appeared relatively similar across field strengths (Fig. 3), with areas of obvious edema or necrosis showing a higher T2eff, areas of NAWM showing the lowest T2eff, and regions of suspected NET adjacent or contiguous with enhancing tumor showing T2eff measurements between edema and NAWM. No differences in T2eff measurements were observed in NET components across tumor grade (ANOVA, P = 0.8678). Measurement of T2eff in the various tissues confirmed this observation (Fig. 4A). A two-way ANOVA showed a significant difference between tissue types (P < 0.0001), field strength (P < 0.0001), and the interaction between tissue types and field strength (P < 0.0001). Multiple comparisons tests showed significant differences between T2eff values for each tissue type within both 1.5T and 3T data (Tukey test, P < 0.01 for all comparisons between tissue types within 1.5T and 3T). Within each tissue type, T2eff values were significantly different across field strengths in edema and necrotic tissues (P < 0.01), but no difference was observed between T2eff values within NAWM or tumor. This suggests that a single threshold may be used across field strengths to differentiate NAWM and tumor from other tissues.

Figure 3.

Postcontrast T1-weighted images, dual-echo TSE images, and T2eff maps of five patients with glioblastoma scanned on different MRI scanners with slightly different acquisition parameters.

Figure 3.

Postcontrast T1-weighted images, dual-echo TSE images, and T2eff maps of five patients with glioblastoma scanned on different MRI scanners with slightly different acquisition parameters.

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Figure 4.

Training data used for delineating NET from other tissues. A, mean T2eff measurements for normal-appearing white matter (NAWM), NET, edema, and necrosis for both 1.5T and 3T. Error bars represent SEM across all 25 patients scanned at either 1.5T or 3T. B, ROC curves showing delineation between edema and necrosis, NET and edema, and NET and NAWM for both 1.5T and 3T. C, an example of a patient with glioblastoma with a large NET mass as evidenced by the well-circumscribed, slightly T2-hyperintense lesion adjacent to the resection cavity. T2eff maps show that this mass falls within the range defined as NET in this study (125 ms < T2eff < 250 ms). D, an example of a glioblastoma patient with a large T2-hyperintense lesion consistent with a mostly edematous mass as evidenced by the high T2-hyperintense signal and “finger-like” white matter extensions. An area consistent with T2eff-defined NET is shown (red arrow), which also is colocalized to the area of contrast enhancement (not shown). E, a patient with a low-grade glioma shown to have an NET mass within the splenium of the corpus callosum on T2eff maps. F, MRSI choline metabolic maps show high choline concentration in this area, consistent with metabolically active tumor.

Figure 4.

Training data used for delineating NET from other tissues. A, mean T2eff measurements for normal-appearing white matter (NAWM), NET, edema, and necrosis for both 1.5T and 3T. Error bars represent SEM across all 25 patients scanned at either 1.5T or 3T. B, ROC curves showing delineation between edema and necrosis, NET and edema, and NET and NAWM for both 1.5T and 3T. C, an example of a patient with glioblastoma with a large NET mass as evidenced by the well-circumscribed, slightly T2-hyperintense lesion adjacent to the resection cavity. T2eff maps show that this mass falls within the range defined as NET in this study (125 ms < T2eff < 250 ms). D, an example of a glioblastoma patient with a large T2-hyperintense lesion consistent with a mostly edematous mass as evidenced by the high T2-hyperintense signal and “finger-like” white matter extensions. An area consistent with T2eff-defined NET is shown (red arrow), which also is colocalized to the area of contrast enhancement (not shown). E, a patient with a low-grade glioma shown to have an NET mass within the splenium of the corpus callosum on T2eff maps. F, MRSI choline metabolic maps show high choline concentration in this area, consistent with metabolically active tumor.

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ROC analysis suggested that T2eff could significantly stratify NET from edema (Fig. 4B; 1.5T: ROC AUC = 0.9000 ± 0.0219, P < 0.0001; 3T: AUC = 0.9773 ± 0.01199, P < 0.0001). A threshold of 250 ms could be used to stratify NET from edema with a sensitivity of 91% and specificity of 71% at 1.5T, and a sensitivity of 100% and specificity of 65% at 3T. ROC analysis suggested that T2eff could also stratify NET from NAWM (Fig. 4B; 1.5T: AUC = 0.9934 ± 0.0043, P < 0.0001; 3T: AUC = 0.9974 ± 0.0030, P < 0.0001). A threshold of 125 ms could be used to stratify NET from NAWM with a sensitivity of sensitivity of 96% and specificity of 97% at 1.5T, and 94% sensitivity and 97% specificity at 3T. Thus, we propose using a range of T2eff values between 125 ms < T2eff < 250 ms, on both 1.5T and 3T MR scanners, to objectively define NET for the purpose of tumor burden quantification. Figure 4C shows a patient with a recurrent glioblastoma containing a large, well-circumscribed NET appearing as slightly T2 hyperintense on T2-weighted images at 3T. T2eff maps clearly show that this NET falls largely within the defined range of 125 ms < T2eff< 250 ms. Conversely, Fig. 4D also shows a patient with recurrent glioblastoma scanned at 3T with a large extent of edematous tissue, as demonstrated by the “finger-like” extensions of very bright T2 hyperintensity on T2-weighted images. Consistent with these qualitative observations, most of this lesion had elevated T2eff within the range associated with edema. In addition, an area near the right insula with relatively lower signal intensity on T2-weighted images (red arrow) also containing contrast enhancement (not shown) appears to have T2eff values within the range defined as NET. MR spectroscopic imaging (MRSI) also confirmed that areas defined as NET on T2eff maps were often associated with increased choline concentration in a subset of patients (Fig. 4E and F), suggestive of freely mobile phosphocholine from the cell membrane turnover in actively proliferating tumor cells.

Using 125 ms < T2eff < 250 ms to objectively define NET, we aimed to test whether the volume of NET burden following radiotherapy alone was a prognostic predictor in patients with newly diagnosed glioblastoma (Fig. 5A–C). The average volume of NET burden for the cohort was estimated at 39.6 cc ± 7.2 cc (SEM) and a range from 0.3 cc to 119 cc. Results suggest that NET volume measured following radiotherapy could identify patients who progressed within 6 months following completion of radiotherapy (Fig. 5A; ROC AUC = 0.8590 ± 0.07248, P = 0.002) as well as those who expired within 9 months following completion of radiotherapy (Fig. 5A; ROC AUC = 0.8467 ± 0.08341, P = 0.004). A threshold of 32 cc, the median volume of NET, was shown to have a sensitivity of 69% and specificity of 79% for identifying patients who progressed within 6 months of radiotherapy and a sensitivity of 70% and specificity of 90% for identifying patients who expired within 9 months of completion of radiotherapy. Patients with NET volumes larger than 32 cc had a significantly shorter PFS (Fig. 5B; log-rank, P = 0.0013, HR = 2.219, median survival for high vs. low NET volume = 132 vs. 294 days) and OS (Fig. 5C; log-rank, P = 0.0071, HR = 2.982, median OS for high vs. low NET volume = 228 vs. 681 days). Cox multivariate regression including clinical covariates of both age and KPS confirmed that NET volume after radiotherapy was a significant predictor of subsequent PFS (Table 1; Cox proportional hazards, P = 0.0216, HR = 2.8410 ± 1.5756 SEM) and OS (Cox proportional hazards, P = 0.0014, HR = 5.3475 ± 1.6925 SEM). Age at diagnosis was also a prognostic factor in patients with newly diagnosed glioblastoma treated with radiotherapy (age: PFS, P = 0.0132, HR = 1.0461 ± 1.0184 SEM; OS, P = 0.0028, HR = 1.0631 ± 1.0207), but KPS was not predictive (KPS: PFS, P = 0.4433; OS, P = 0.1526).

Figure 5.

ROC curves, PFS, and OS using T2eff-defined NET volumes in newly diagnosed glioblastoma patients treated with radiotherapy or radiotherapy and concurrent temozolomide, and recurrent glioblastoma treated with bevacizumab. A, ROC analysis performed using NET volume estimates following radiotherapy to predict 6-month PFS (PFS6) and 9-month OS (OS9). B, Kaplan–Meier curves showing that the median volume of NET (32 cc) could stratify long-term and short-term PFS in newly diagnosed glioblastoma patients treated with radiotherapy (P = 0.0013). C, Kaplan–Meier curves showing the median volume of NET (32 cc) could also stratify long-term and short-term OS in newly diagnosed glioblastoma patients treated with radiotherapy (P = 0.0071). D, ROC analysis performed using NET volume estimates following concurrent radiotherapy and temozolomide to predict PFS6 and OS9. Results did not show statistical significance for either PFS6 or OS9 in this patient cohort. E, Kaplan–Meier curves illustrating that the median volume of NET (23 cc) could stratify long- and short-term PFS in newly diagnosed glioblastoma patients treated with concurrent radiotherapy and temozolomide followed by adjuvant temozolomide (P = 0.0350). F, Kaplan–Meier curves showing that median NET volume (23 cc) could stratify long-term and short-term OS (P = 0.0335). G, ROC analysis performed using the change in NET volume before and after bevacizumab treatment in recurrent glioblastoma to predict PFS6 and OS9. H, Kaplan–Meier curves demonstrating that recurrent glioblastoma patients with more than 50% decrease in NET volume following bevacizumab therapy (“responders”) have a longer PFS compared with patients demonstrating less than a 50% decrease in NET volume (“nonresponders”; P = 0.0070). I, Kaplan–Meier curves showing that recurrent glioblastoma patients showing “response” to bevacizumab on NET have a significantly lower OS compared with “nonresponders” (P = 0.0105).

Figure 5.

ROC curves, PFS, and OS using T2eff-defined NET volumes in newly diagnosed glioblastoma patients treated with radiotherapy or radiotherapy and concurrent temozolomide, and recurrent glioblastoma treated with bevacizumab. A, ROC analysis performed using NET volume estimates following radiotherapy to predict 6-month PFS (PFS6) and 9-month OS (OS9). B, Kaplan–Meier curves showing that the median volume of NET (32 cc) could stratify long-term and short-term PFS in newly diagnosed glioblastoma patients treated with radiotherapy (P = 0.0013). C, Kaplan–Meier curves showing the median volume of NET (32 cc) could also stratify long-term and short-term OS in newly diagnosed glioblastoma patients treated with radiotherapy (P = 0.0071). D, ROC analysis performed using NET volume estimates following concurrent radiotherapy and temozolomide to predict PFS6 and OS9. Results did not show statistical significance for either PFS6 or OS9 in this patient cohort. E, Kaplan–Meier curves illustrating that the median volume of NET (23 cc) could stratify long- and short-term PFS in newly diagnosed glioblastoma patients treated with concurrent radiotherapy and temozolomide followed by adjuvant temozolomide (P = 0.0350). F, Kaplan–Meier curves showing that median NET volume (23 cc) could stratify long-term and short-term OS (P = 0.0335). G, ROC analysis performed using the change in NET volume before and after bevacizumab treatment in recurrent glioblastoma to predict PFS6 and OS9. H, Kaplan–Meier curves demonstrating that recurrent glioblastoma patients with more than 50% decrease in NET volume following bevacizumab therapy (“responders”) have a longer PFS compared with patients demonstrating less than a 50% decrease in NET volume (“nonresponders”; P = 0.0070). I, Kaplan–Meier curves showing that recurrent glioblastoma patients showing “response” to bevacizumab on NET have a significantly lower OS compared with “nonresponders” (P = 0.0105).

Close modal
Table 1.

Cox proportional hazards model results for newly diagnosed glioblastoma treated with radiotherapy

PFSOS
HRPHRP
Age 1.0461 ± 1.0184 0.0132 1.0631 ± 1.0207 0.0028 
KPS 1.0184 ± 1.0240 0.4433 0.9624 ± 1.0271 0.1526 
T2eff defined NET >32 cc 2.8410 ± 1.5756 0.0216 5.3475 ± 1.6925 0.0014 
PFSOS
HRPHRP
Age 1.0461 ± 1.0184 0.0132 1.0631 ± 1.0207 0.0028 
KPS 1.0184 ± 1.0240 0.4433 0.9624 ± 1.0271 0.1526 
T2eff defined NET >32 cc 2.8410 ± 1.5756 0.0216 5.3475 ± 1.6925 0.0014 

NOTE: Error bars represent ± SEM.

Next, we aimed to test whether the volume of NET burden following radiotherapy combined with temozolomide followed by adjuvant temozolomide was a prognostic predictor in patients with newly diagnosed glioblastoma (Fig. 5D and F). The average volume of NET burden for the cohort was estimated at 34.1 cc ± 4.7 cc (SEM) and a range from 0.6 cc to 122 cc. Results suggest that NET volume evaluated following combined radiotherapy and temozlomide could not identify patients who progressed within 6 months following completion of radiochemotherapy (Fig. 5D; ROC AUC = 0.5906 ± 0.0963, P = 0.3850) or patients at risk for expiring within 9 months following completion of radiochemotherapy (Fig. 5D; ROC AUC = 0.6733 ± 0.0925, P = 0.08304). Patients with NET volumes larger than the median NET volume of 23 cc for this cohort following radiochemotherapy were at significantly higher risk for shorter PFS (Fig. 5E; log-rank, P = 0.035, HR = 2.217, median PFS for high vs. low NET volume = 67 vs. 149 days) and OS (Fig. 5F; log-rank, P = 0.0335, HR = 1.1660, median OS for high vs. low NET volume = 181 vs. 301 days). Cox multivariate regression including clinical covariates confirmed this trend (Table 2), showing that NET volume following completion of radiochemotherapy was a significant independent predictor of both PFS (Cox proportional hazards, P = 0.0323, HR = 2.3708 ± 1.4690 SEM) and OS (Cox proportional hazards, P = 0.0004, HR = 2.7907 ± .5173 SEM). Age and KPS at diagnosis were not prognostic factors for PFS or OS in newly diagnosed glioblastoma patients treated with radiochemotherapy (age: PFS, P = 0.7711, OS, P = 0.3520; KPS: PFS, P = 0.2745; OS, P = 0.2450).

Table 2.

Cox proportional hazards model results for newly diagnosed glioblastoma treated with concurrent radiotherapy and temozolomide

PFSOS
HRPHRP
Age 0.9939 ± 1.0211 0.7711 1.0251 ± 1.0270 0.3520 
KPS 1.0227 ± 1.0207 0.2745 1.0270 ± 1.0232 0.2450 
T2eff defined NET >23 cc 2.3708 ± 1.4969 0.0323 2.7907 ± 1.5173 0.0138 
PFSOS
HRPHRP
Age 0.9939 ± 1.0211 0.7711 1.0251 ± 1.0270 0.3520 
KPS 1.0227 ± 1.0207 0.2745 1.0270 ± 1.0232 0.2450 
T2eff defined NET >23 cc 2.3708 ± 1.4969 0.0323 2.7907 ± 1.5173 0.0138 

NOTE: Error bars represent ± SEM.

Finally, we tested whether early NET response measured as the change in NET volume before and after bevacizumab therapy could be used to predict survival in patients with recurrent glioblastoma (Fig. 5G and I). The average volume of NET burden for the cohort prior to and after bevacizumab therapy was estimated at 30.3 cc ± 3.7 cc (SEM) and 15.8 cc ± 3.5 cc (SEM), respectively. The average change in NET volume was −14.5 cc ± 2.7 cc (SEM). Results demonstrated that changes in NET volume before and after bevacizumab therapy could be used to identify patients who progressed within 6 months of completion of the first dose of bevacizumab (Fig. 5G; ROC AUC = 0.7786 ± 0.0967, P = 0.022), as well as patients who expired within 9 months of completion of the first dose of bevacizumab (Fig. 5G; ROC AUC = 0.8681 ± 0.07399, P = 0.002). A decrease in NET volume greater than 50% was found to have a sensitivity of 70% and specificity of 71% for identifying patients that will progress within 6 months of starting bevacizumab therapy, along with a sensitivity of 83% and specificity of 67% for identifying patients that will expire within 9 months of starting bevacizumab therapy. Patients showing a “response,” or more than 50% decrease in NET volume after bevacizumab therapy, had a significantly longer PFS (Fig. 5H; log-rank, P = 0.00070, HR = 2.565, median PFS for responders vs. nonresponders = 215 vs. 84 days) and OS (Fig. 5I; log-rank, P = 0.0105, HR = 2.00, median OS for responders vs. nonresponders = 375 vs. 187 days). Cox multivariate regression confirmed this observation (Table 3), showing longer PFS (Cox proportional hazards, P = 0.0071, HR = 1.0189 ± 1.0070) and OS (Cox proportional hazards, P = 0.0377, HR = 1.0122 ± 1.0058) in patients demonstrating more than a 50% decrease in NET volume after the first dose of bevacizumab. Both age and KPS were not significant prognostic factors for recurrent glioblastoma patients treated with bevacizumab (age: PFS, P = 0.1090, OS, P = 0.1220; KPS: PFS, P = 0.5283, OS, P = 0.7142).

Table 3.

Cox proportional hazards model results for recurrent glioblastoma treated with bevacizumab

PFSOS
HRPHRP
Age 0.9703 ± 1.0190 0.1090 0.9746 ± 1.0168 0.1220 
KPS 0.9840 ± 1.0259 0.5283 0.9909 ± 1.0254 0.7142 
Reduction in NET > 50% 1.0189 ± 1.0070 0.0071 1.0122 ± 1.0058 0.0377 
PFSOS
HRPHRP
Age 0.9703 ± 1.0190 0.1090 0.9746 ± 1.0168 0.1220 
KPS 0.9840 ± 1.0259 0.5283 0.9909 ± 1.0254 0.7142 
Reduction in NET > 50% 1.0189 ± 1.0070 0.0071 1.0122 ± 1.0058 0.0377 

NOTE: Error bars represent ± SEM.

Results from this study support the hypothesis that T2 maps quickly obtained using dual-echo TSE images can be used to objectively define and quantify NET burden. Results support the concept that quantification of NET is clinically meaningful and prognostic, as demonstrated both by the prognostic significance of NET tumor burden following radiotherapy with or without temozolomide as well as the change in NET volume as a simple response measure during bevacizumab therapy in recurrent glioblastoma. This is a particularly timely question as this concept has come into serious debate recently (9). In particular, studies have suggested limited value of including challenging and costly qualitative assessment of NET response when evaluating drug efficacy, as response rates and PFS estimates seem comparable with and without the use of NET assessment (1) and in most instances, NET progression results in contrast enhancing tumor progression by the subsequent follow-up examination (30). In addition, recent results from ACRIN 6677/RTOG 0625, a prospective, randomized, phase II multicenter trial comparing bevacizumab with either irinotecan or temozolomide in recurrent glioblastoma, demonstrated that response rate of contrast enhancing tumor was predictive of OS, whereas NET progression rates did not predict OS (31). Regardless, a large proportion of patients are expected to experience NET progression prior to changes in contrast enhancement and the vast majority of low-grade gliomas do not have contrast enhancement, reinforcing the need for an objective definition of nonehancing tumor using quantitative MRI.

Although ROC analysis demonstrated that NET burden could be used to quickly identify newly diagnosed glioblastomas at high risk for progressing by 6 months or expiring before 9 months when treated with radiotherapy alone, this same time point was of limited predictive value in patients treated with radiotherapy and concurrent temozolomide, the current standard of care for newly diagnosed glioblastoma. These results may be influenced by other molecular characteristics of the tumor, such as MGMT promoter methylation status or gene expression subtype. Future studies with larger patient cohorts are necessary to identify the roles of these additional prognostic factors.

Results demonstrate that T2eff measurements using dual-echo TSE are relatively stable, varying approximately 3% to 5% over time and across scanners of similar field strength when evaluated in phantoms. This is consistent with previous reports showing variation in normal tissues of between 5% and 15% (23, 32, 33), on par with errors due to the specific pulse sequence and vendor specific errors (34). T2 estimation using dual-echo TSE techniques, however, has been shown to slightly overestimate T2 by as much as 10% (26, 35–37). This bias in T2 estimation arises from multiple sources, including the use of only a few echoes for estimation, spurious signals from stimulated echoes, radiofrequency pulse imperfections, and different phase encoding profile orders (26, 38, 39). Despite this inaccuracy, T2 mapping using dual-echo TSE acquisition has proven very useful for identification of various pathologies, including Alzheimer's and other neurodegenerative diseases (23, 36, 40, 41). Techniques to overcome this slight bias have been developed (38), although they remain relatively cumbersome to implement and are not often used in clinical practice. Importantly, the Alzheimer's disease neuroimaging initiative chose to implemented dual-echo T2-weighted TSE for estimation of pathology-specific changes in quantitative T2; thus, a standardized dual-echo TSE sequence is openly available from all major MRI vendors, which further supports the view of using dual-echo T2-weighted TSE to quantify “effective T2” (T2eff) for use in objectively defining NET burden within clinically realizable scan times.

It is important to point out that there remains significant overlap between T2eff measurements within regions believed to contain mixtures of edema and NET, thus quantification of NET burden in this study may not be completely accurate. During the training phase, we chose to specifically identify regions containing obvious, pure NET with little partial volume contamination from edema or necrosis. It is both conceivable and highly likely that some regions classified as edema and not included in the quantification of NET burden contain active tumor, whereas some regions classified as NET may in fact contain substantial proportions of edema. Future studies aimed at histologic validation should be performed in order to further aid in defining NET burden using T2eff measurements.

Additional considerations and study limitations should also be addressed. For example, white matter changes after radiotherapy may have confounded our interpretation of various tissue types; however, we strategically included recurrent glioma patients in our training cohort in order to provide a more robust range of T2 values that might include treatment-related changes. Finally, this study lacks separate, independent validation of the primary findings. Future studies aimed at independent validation in a randomized, clinical trial, along with comparison to other imaging biomarkers, is warranted in order to establish effective T2 maps as a surrogate for quantifying NET burden in patients with gliomas.

Dual-echo TSE can be used to objectively define NET burden for use in brain tumor characterization, prognosis, and response assessment. T2eff measurements obtained using dual-echo TSE has relatively low variability, is feasible at both 1.5T and 3T, and can be used to predict response and survival in patients with gliomas.

B.M. Ellingson reports receiving commercial research grants from Roche/Genentech and Siemens Healthcare, and reports receiving speakers bureau honoraria from and is a consultant/advisory board member for Roche/Genentech. A. Lai reports receiving other research grants from Roche and Takeda, and is a consultant/advisory board member for Novocure and Roche. No potential conflicts of interest were disclosed by the other authors.

Conception and design: B.M. Ellingson, A. Lai, W.B. Pope, T.F. Cloughesy

Development of methodology: B.M. Ellingson, W.B. Pope

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): B.M. Ellingson, A. Lai, P.L. Nghiemphu, T.F. Cloughesy

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): B.M. Ellingson, A. Lai, W.B. Pope, T.F. Cloughesy

Writing, review, and/or revision of the manuscript: B.M. Ellingson, A. Lai, H.N. Nguyen, P.L. Nghiemphu, W.B. Pope, T.F. Cloughesy

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): B.M. Ellingson, H.N. Nguyen

Study supervision: B.M. Ellingson, T.F. Cloughesy

This work was supported by the National Brain Tumor Society Research Grant (B.M. Ellingson and T.F. Cloughesy), NIH/NCI 1 R21 CA167354-01 (B.M. Ellingson), UCLA Institute for Molecular Medicine Seed Grant (B.M. Ellingson), UCLA Radiology Exploratory Research Grant (B.M. Ellingson), University of California Cancer Research Coordinating Committee Grant (B.M. Ellingson), ACRIN Young Investigator Initiative Grant (B.M. Ellingson), Art of the Brain (T.F. Cloughesy), Ziering Family Foundation in memory of Sigi Ziering (T.F. Cloughesy), Singleton Family Foundation (T.F. Cloughesy), Clarance Klein Fund for Neuro-Oncology (T.F. Cloughesy), National Institute of Health National Institute of General Medical Sciences training grant (GM08042), and the University of California Los Angeles Medical Scientist Training Program.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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