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J Clin Oncol. 2008 July 10; 26(20): 3387–3394.
PMCID: PMC3266717

Functional Diffusion Map As an Early Imaging Biomarker for High-Grade Glioma: Correlation With Conventional Radiologic Response and Overall Survival



Assessment of radiologic response (RR) for brain tumors utilizes the Macdonald criteria 8 to 10 weeks from the start of treatment. Diffusion magnetic resonance imaging (MRI) using a functional diffusion map (fDM) may provide an earlier measure to predict patient survival.

Patients and Methods

Sixty patients with high-grade glioma were enrolled onto a study of intratreatment MRI at 1, 3, and 10 weeks. Receiver operating characteristic curve analysis was used to evaluate imaging parameters as a function of patient survival at 1 year. Both log-rank and Cox proportional hazards models were utilized to assess overall survival.


Greater increases in diffusion in response to therapy over time were observed in those patients alive at 1 year compared with those who died as a result of disease. The volume of tumor with increased diffusion by fDM at 3 weeks was the strongest predictor of patient survival at 1 year, with larger fDM predicting longer median survival (52.6 v 10.9 months; log-rank, P < .003; hazard ratio [HR] = 2.7; 95% CI, 1.5 to 5.9). Radiologic response at 10 weeks had similar prognostic value (median survival, 31.6 v 10.9 months; log-rank P < .0007; HR = 2.9; 95% CI, 1.7 to 7.2). Radiologic response and fDM differed in 25% of cases. A composite index of response including fDM and RR provided a robust predictor of patient survival and may identify patients in whom RR does not correlate with clinical outcome.


Compared with conventional neuroimaging, fDM provided an earlier assessment of equal predictive value, and the combination of fDM and RR provided a more accurate prediction of patient survival than either metric alone.


For malignant glioma, the Macdonald criteria are the primary radiologic response (RR) method, and have been correlated with survival.1-7 In addition, three-dimensional measurements of tumor volume have also been suggested to have a stronger association with survival.8 One disadvantage of size/volume measures is the time for changes to occur,1,9,10 with 8 to 10 weeks necessary to assess response.

Diffusion magnetic resonance imaging (MRI), which measures the random (Brownian) motion of water, has been proposed as an early biomarker for tumor response.11 Increased diffusion of water molecules (measured as an increase in the apparent diffusion coefficient [ADC]) occurs shortly after a successful treatment, and correlates with the breakdown of cellular membranes and reduction in cell density that both precede changes in tumor size. Diffusion MRI has been evaluated in preclinical12-27 and clinical studies.28-36 Quantification of diffusion changes has evolved from the mean change in ADC12,28 to a voxel-by-voxel approach termed the functional diffusion map (fDM).37-39 One potential disadvantage of the mean ADC is that different areas of tumor with increasing and decreasing changes in diffusion would cancel out, such that there would be no observed change in overall mean ADC, thus decreasing sensitivity. The fDM, by measuring regional changes, is not limited in this manner and correlates with overall survival (OS) in a rodent glioma model.39 In patients with diverse primary brain tumors38 or high-grade glioma,37 early changes in fDM (both increasing and decreasing diffusion) correlated with RR. In the present study, instead of correlating fDM with RR, itself a surrogate end point, we ascertained whether diffusion MRI could directly predict patient survival.



Patients with primary brain tumors were enrolled onto a protocol of intratreatment MRI. We obtained informed consent, and the institutional review board approved images and medical record use. A total of 60 patients were evaluated on this study, of whom 34 were included in a previous analysis.37


Radiotherapy was delivered using three-dimensional conformal therapy or intensity-modulated radiotherapy with at least 6-MV photons. Standard technique included a 2.0- to 2.5-cm margin on either the enhancing region on gadolinium-enhanced scans or the abnormal signal on T2-weighted scans to 46 to 50 Gy, with the gross tumor treated to a final median dose of 70 Gy in 6 to 7 weeks (Table 1).40 Twenty-one of these patients were treated on a phase 2 protocol of high-dose (> 60 Gy) radiation therapy concurrent with temozolomide. Chemotherapy was delivered as dependent on clinical circumstances (Table 1).

Table 1.
Receiver Operating Characteristic Curve Analysis of the Ability of Different Radiographic Criteria to Predict Patient Survival 1 Year From Diagnosis

Radiographic Scans

Diffusion MRI and standard MRI (fluid attenuation inversion recovery, T2-weighted and gadolinium-enhanced T1-weighted MRI) were performed 1 week before and 1, 3, and 10 weeks after the start of radiation with follow-up scans every 2 to 3 months.

End Points

RR at 10 weeks was based on changes in tumor volume on T1 contrast-enhanced MRI and steroid doses and were classified as complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD).1 Steroid doses were recorded before each scan, weekly during radiotherapy, and at each follow-up.

Diffusion MRI

MRI scanning occurred on a General Electric (Waukesha, WI) 1.5T MRI system (n = 45 patients) or a Philips (Best, the Netherlands) Achieva 3T system (n = 15 patients). Diffusion imaging utilized a single-shot, spin-echo, diffusion-sensitized, echo-planar imaging (EPI) acquisition sequence. On the 1.5T system, 24 6-mm axial-oblique sections were acquired using a 22 cm-field of view (FOV) and 128 matrix (voxel = 17.7 mm3) with “b-factor” = 0 and 1,000 seconds/mm2 along three orthogonal directions (repetition time = 10,000 ms; echo time = 71 to 100 ms, and number of averages [NAV] = 1). On the 3T system, at least 28 4-mm axial-oblique sections were acquired through the brain using a 24-cm FOV and 128 matrix (voxel size = 14 mm3; repetition time = 2,636 ms; echo time = 46 ms; NAV = 1 for b = 0, and NAV = 2 for b = 1,000 seconds/mm2) with diffusion sensitization along three orthogonal directions. Parallel imaging (sensitivity encoding factor = 3) was used at 3T to reduce spatial distortion. The diffusion images for the three orthogonal directions were combined to calculate an ADC map.41

fDM Analysis

Images were registered to the pretreatment MRI with a mutual information algorithmn,42 and regions of interest (ROIs) contoured on contrast-enhanced T1-weighted images. A minimum of 4 mL of tumor on postoperative scans was necessary for eligibility. If a resection cavity was present, it was included within the ROI if circumscribed by contrast enhancement and excluded if outside the enhancing region. Three patients did not have contrast-enhancing tumors before surgery, so ROI definition utilized all available MR sequences. Only voxels present in both the pretherapy and post-therapy tumor volumes were included for fDM analysis. Individual voxels were stratified into three categories based on the change in ADC from the pretreatment scan to each time point. Red voxels represented areas within the tumor where ADC increased (> 55 × 10−5 mm2/sec), blue voxels represented decreased ADC (< 55 × 10−5 mm2/sec), and green voxels represented no change. This thresholds represent the 95% CI for change (Fig 1) in ADC for uninvolved cerebral hemisphere.38 The percentage of the tumor within these three categories were calculated as VI, VD, and V0, respectively, and the total percentage of tumor with a significant change in diffusion values was VT, where VT = VI + VD.

Fig 1.
Representative functional diffusion map (fDM) analysis over time. Functional diffusion maps at (A) 1, (B) 3, and (C) 10 weeks for two patients treated with fractionated radiation therapy. The patient on the left was scored as responsive by fDM at 3 weeks ...

Statistical Analysis

The thresholds for determining whether changes in volume, mean ADC, or fDM correlated with patient survival were determined using receiver operating characteristic (ROC) curve analysis. ROC curves identify the optimal threshold for a binary classifier using a graphical plot of sensitivity versus [1 –specificity]. The area under the curve (AUC) represents the overall predictive value across all thresholds, with perfect predictive value yielding AUC = 1.0. Optimizing a metric on a single data set introduces inherent bias (type III error) favoring a correlation with the end point of interest. Therefore, we performed leave-one-out cross-validation. This is a method for minimizing prediction error that involves leaving individual values out from the data set, performing the stratification, and then repeating the process “n” number of times where n = the number of individual samples. This method results in an approximation of the unbiased estimate of the true predictive value.43 Given the 15 metrics evaluated by ROC curve analysis, a correction for multiple comparisons (Bonferroni correction) was applied such that only variables with an unadjusted P of less than .0033 were considered significant. Differences based on categoric variables were assessed using Fisher's exact test, continuous variables utilized t test, and trends were assessed with the Cochran-Armitage test. Survival analysis utilized log-rank and Cox proportional hazards models. Statistical analysis utilized MedCalc v9.3 (MedCalc Software, Mariakerke, Belgium).


Patient Population

Between November 1, 2000, and November 1, 2006, 70 patients with primary brain tumors were enrolled onto a prospective study of early tumor response. Sixty-seven of these patients had WHO grade 3 or IV astrocytoma, and 60 had assessable results form the population for this study. Seven patients were excluded for the following reasons: three had repeat surgical procedures within one month, one had claustrophobia, and three declined treatment. Pretreatment scans were performed 6 (± 3.9) days from start of treatment, 54 patients had a scan at 1 week (6 ± 2.8 days), all 60 at 3 weeks (21 ± 5.6 days), and 55 at 10 weeks (71 ± 14.2 days). Median survival is 13.9 months, and at last contact, 30% of patients (18 of 60) were still alive with a median follow-up of 23.1 months.

Evaluation of Response Measures

A total of five metrics were measured at the three time points, including the percentage of change in tumor volume, the percentage of change in mean tumor ADC, and three submetrics of fDM (increasing ADC [fDM-VI], decreasing ADC [fDM-VD], or any change in ADC [fDM-VT]. Comparisons with standard RR were limited to the 55 patients with scans at 10 weeks. ROC curve analysis was performed to predict patient survival 1 year from diagnosis (34 of 55 patients were alive 1 year from diagnosis, and 21 if 55 died; Table 2). All but two deaths were secondary to tumor progression.

Table 2.
Pretreatment and Treatment-Related Patient Characteristics

Change in Tumor Volume

There were modest changes in tumor volume; median at 1, 3, and 10 weeks, respectively, was +0.2% (interquartile range [IQR], –19.4 to +19.4), +2.0% (IQR, –30.0 to 3.5), and +0.3% (IQR, –32.2 to +56.6). With smaller increases in volume at 10 weeks for those who were alive at 1 year compared with those who died (median, −0.1% [IQR, –38.2 to +41.7] v +46.9% [IQR, –17.5 to 122.2]; P < .09). By ROC curve analysis (Table 2) the change in tumor volume at each time point exhibited a trend toward predicting patient survival at 1 year but did not reach statistical significance (P < .09 at each time). When tumor response at 10 weeks was stratified by Macdonald criteria, this increased the predictive value (Table 2). No patient had CR, three had PR, 27 had SD, and 25 had PD. The presence of SD or PR at 10 weeks was the best volume-based correlate with survival at 1 year (P < .04).

Changes in Mean ADC

The changes in mean tumor ADC at 1, 3, and 10 weeks were, respectively, +0.4% (IQR, –4.5 to +5.6), +2.9% (IQR, –3.1 to +6.9), and +10.3% (IQR, –1.6 to +21.6). Three-week mean ADC was associated with 1-year survival, with those alive exhibiting increased ADC (median, +3.4% [IQR, –2.0 to + 12.0]) compared with a decreased ADC (median, −1.5% [IQR, –6.9 to +0.9]) in those who died (P < .03). By ROC curve analysis (Table 2), the change in mean tumor ADC at 3 weeks was associated with 1-year survival (P < .02) but the change at 1 and 10 weeks was not (P > .1). After correcting for multiple comparisons, even the 3-week metric was of only of borderline significance.

Changes in fDM

When regional tumor diffusion data were analyzed by fDM (Fig 1), the percentage of tumor with increasing diffusion (fDM-VI) over time was associated with survival 1 year from diagnosis. VI increased linearly over time, with median increases of 1.6% (IQR, 0.4 to 4.3), 4.0% (IQR, 1.0 to 7.5), and 12.2% (IQR, 3.8 to 27.5) at 1, 3, and 10 weeks, respectively, with greater increases in VI for those alive at 1 year compared with those who had died (Cochran-Armitage P < .001; Appendix Fig A1, online only). To assess fDM as an early biomarker we focused on VI at 3 weeks. By ROC curve analysis the strongest relationship between any of the imaging metrics, and survival at 1 year was observed for VI at 3 weeks (P < .0002; Table 2; Appendix Fig A2, online only).

Previously,37,38 both increasing and decreasing fDM at 3 weeks was correlated with RR at 10 weeks. In the present analysis, however, no correlation was found between patient survival at 1 year and decreasing diffusion by fDM (P > .1 at 1, 3, and 10 weeks; Fig A1). Adding VD to VI (to yield VT) was, therefore, associated with a lower predictive value for survival, and all analysis focused on fDM-VI at 3 weeks.

Optimization of fDM-VI

When assessed as a continuous variable, increasing VI at 3 weeks was correlated with increasing OS (P < .02). Given the continuous nature of VI, ROC curve analysis suggested a threshold of 4.7%, where VI 4.7% or greater at 3 weeks was stratified as response and VI less than 4.7% as nonresponse. After leave-one-out cross-validation, VI remained a significant predictor of patient survival at 1 year (P < .001; AUC = 0.723; sensitivity = 69.7% [95% CI, 51.3 to 84.4]; specificity = 75.0% [95% CI, 50.9 to 91.2]; positive predictive value [PPV] = 82.1%; negative predictive value [NPV] = 60.0%).

Overall Survival As a Function of fDM Stratification and RR

Using the VI threshold of 4.7%, those with higher VI had median survival 52.6 months whereas those with lower VI had median survival of only 10.9 months (P < .003; HR = 2.7; 95% CI, 1.5 to 5.9; Fig 2A). Conventional RR at 10 weeks was similarly prognostic (Fig 2B). Those with SD/PR had median survival of 31.6 months, whereas those with PD had median survival of 10.9 months (P < .0007; HR = 2.9; 95% CI, 1.7 to 7.2). For comparison with RR, fDM was limited to the 55 patients who had RR at 10 weeks, but if this analysis is extended to include all 60 patients, fDM was similarly prognostic (P < .005; HR = 2.4; 95% CI, 1.4 to 4.8).

Fig 2.
Overall survival as a function of functional diffusion map (fDM), radiologic response (RR), and their composite. (A) Overall survival by log-rank test based on fDM stratification at 3 weeks from the start of treatment where the yellow curve (n = 27) represents ...

There was an association between RR and fDM stratification (P < .001; Fig 3) with concurrence in 75% of cases (41 of 55). Figure 1 presents two patients in whom fDM and RR differed in their stratification of response. The patient on the left was classified as PD by RR, but in contrast fDM documented a VI of 26.4% at 3 weeks (middle panel), and the patient was classified as responding by fDM. Despite PD, this patient clinically stabilized and is alive without progression at 33 months. In contrast, the patient on the right had SD by RR, but had minimal change in tumor ADC at 3 weeks by fDM (middle panel, 1.6%), clinically progressed within 5 months, and died at 7 months.

Fig 3.
Correlation of functional diffusion map (fDM) mediated evaluation of response with radiologic response (RR). There was a strong correlation between increasing fDM-VI at 3 weeks and subsequent RR at 10 weeks going from worst to best (progressive disease ...

Given the differences between conventional RR and fDM in 25% of patients, a composite index of response was developed based on fDM and RR, and was the most robust response-based model for OS (P < .0002; Fig 2C). The composite identified three groups of patients. Those with the best prognosis were without radiographic progression (SD/PR) and responsive by fDM, and had a median survival of 52.6 months. Those with the worst prognosis had low VI by fDM and PD by RR, and their median survival was 8.1 months. The intermediate group, comprising patients in whom fDM and RR differed, had a median survival of 14.4 months. Both the intermediate group (P < .02; HR = 2.4; 95% CI, 1.2 to 4.6) and the best-prognosis group (P < .0001; HR = 4.2; 95% CI, 2.4 to 12.9) were distinct from the worst-prognosis composite group.

Evaluation of Other Prognostic Variables

To further assess the utility of fDM, we evaluated common variables previously found to correlate with survival in high-grade glioma (Table 1). Those disproportionately represented in the fDM responding group were younger age (P < .03), higher baseline tumor ADC (P < .005), and increased frequency of surgical resection (P < .002). On univariate analysis, only age (< 50 v ≥ 50 years; P < .006) and pathologic grade (WHO grade 3 v 4; P < .05) correlated with OS. Performance status, surgical resection, pretreatment tumor ADC, use of chemotherapy, and radiation dose did not (Table 3). If limited to the patients with grade 4 tumors treated with definitive radiation therapy (≥ 60 Gy; n = 41) there was prolonged survival associated with concurrent temozolomide and radiation compared with radiation alone (P < .05). When these individual variables were included in a multivariate model, only age and fDM were retained (Table 3, model 1).

Table 3.
Variables Associated With Patient Survival

The best predictor of OS was the Radiation Therapy Oncology Group (RTOG) recursive partition analysis44(RPA; Table 3; P < .0004). When fDM was added to the RPA, both retained prognostic value (Table 3, model 2). Interestingly, across the five categories (there were no patients in class 2) there was an inverse relationship between class and the likelihood of response by fDM: 75%, 73%, 50%, 33%, and 25%, for classes 1, 3, 4, 5, and 6, respectively (P < .01; Table A1, online only). For each class, median survival was longer in the group responding by fDM than in those not responding. Thus, although the numbers were small, it does appear that fDM retained prognostic value across the whole spectrum of disease. Patients predicted to have a worse outcome by RPA were also less likely to be responsive to therapy even as early as 3 weeks into treatment.


For glioma patients, the standard determination of RR is conventional MRI.1 In this study, RR based on the Macdonald criteria at 10 weeks did correlate with 1-year survival (PPV = 77.8% and = NPV 56.0%). Although this metric has been widely accepted, it does not allow for individualization of radiation treatment because the measurement is made well after the completion of therapy. Diffusion MRI evaluated using the fDM-VI at 3 weeks also correlated with patient survival at 1 year (PPV = 82.1% and NPV = 60.0%), and might allow for response-based therapy alteration.

Interestingly, although fDM-VI was prognostic at both 3 and 10 weeks, the greatest differentiation between responding and nonresponding tumors was observed at the early time point. It was previously noted that changes in diffusion MRI in both preclinical and clinical evaluations often precede volumetric response, and in fact, by the time tumors were documented to have responded by size criteria, many of the early changes observed by diffusion MRI had already resolved.12,28 Thus, although fDM was prognostic at both 3 and 10 weeks the greatest discrimination was observed before overt changes in tumor size had occurred, and fDM lost some prognostic value after early diffusion changes had dissipated.

The use of 3-week fDM-VI as an early biomarker for survival was at least as prognostic as the Macdonald criteria at 10 weeks, with similar PPV and NPV, but was obtained 7 to 8 weeks earlier. Combining fDM and RR into a composite provided the best response-based prediction, which provides an alternative use of fDM wherein current clinical care is maintained with the addition of fDM yielding a more accurate evaluation. This may help discern radiographic progression from “pseudoprogression,” a recently identified clinical phenomenon wherein patients demonstrate radiographic evidence for progression of disease that may resolve without a change of treatment and without clinical progression.45 Size- and volume-based measures of response are also highly dependent on steroid dosing because these can influence tumor volume, blood vessel permeability, and contrast enhancement. In the current analysis, volume changes alone were significantly less prognostic than when steroid dosing was included in the evaluation, as in the Macdonald criteria. In contrast, diffusion changes within the gross tumor are largely unaffected after steroid treatment, whereas a moderate decline in peritumoral ADC has been observed after steroid treatment.46 Peritumoral edema was not included for fDM analysis and, therefore, steroid dosing did not influence fDM stratification.

At present, clinical variables are used to predict patient prognosis. However, there is also a growing body of genetic evidence that will certainly be used in the future to help identify the likelihood of a tumor's responding to treatment, such as specific genetic deletions, activation of oncogenes, loss of tumor suppressor genes, or promoter methylation patterns.47-49 However, most of these tests have not been commonly adopted at present. A metric providing an early measure of actual tumor response—not just the likelihood of response—is critical and will have the capacity to add prognostic value across different genetic backgrounds.

Finally, the results reported in this article must be validated in a larger multi-institutional cohort before the fDM can be adopted as a biomarker for treatment response. In addition, although this study focused on glioma patients treated with radiation therapy with or without chemotherapy, the fDM can, in principle, be applied to most other cancers and treatments given that modern MRI scanners now allow diffusion measurements in other body regions with suitable motion compensation techniques.32-34,36,50


Although all authors completed the disclosure declaration, the following author(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a “U” are those for which no compensation was received; those relationships marked with a “C” were compensated. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.

Employment or Leadership Position: Alnawaz Rehemtulla, ImBio LLC (U); Brian D. Ross, ImBio LLC (U) Consultant or Advisory Role: None Stock Ownership: Alnawaz Rehemtulla, ImBIO LLC; Brian D. Ross, ImBio LLC Honoraria: Daniel A. Hamstra, Varian Medical Research Research Funding: None Expert Testimony: None Other Remuneration: None


Conception and design: Timothy D. Johnson, Alnawaz Rehemtulla, Brian D. Ross, Thomas L. Chenevert

Financial support: Brian D. Ross, Thomas L. Chenevert

Administrative support: Brian D. Ross

Provision of study materials or patients: Christina Tsien, Theodore S. Lawrence, Larry Junck

Collection and assembly of data: Thomas L. Chenevert

Data analysis and interpretation: Daniel A. Hamstra, Craig J. Galban, Charles R. Meyer, Timothy D. Johnson, Pia C. Sundgren, Christina Tsien, Theodore S. Lawrence, Larry Junck, David J. Ross, Alnawaz Rehemtulla, Brian D. Ross, Thomas L. Chenevert

Manuscript writing: Daniel A. Hamstra, Larry Junck, Brian D. Ross, Thomas L. Chenevert

Final approval of manuscript: Daniel A. Hamstra, Craig J. Galban, Charles R. Meyer, Timothy D. Johnson, Pia C. Sundgren, Christina Tsien, Theodore S. Lawrence, Larry Junck, David J. Ross, Alnawaz Rehemtulla, Brian D. Ross, Thomas L. Chenevert


Fig A1.
(A) fDM-VI and (B) fDM-VD as a function of time since start of treatment and survival status 1 year from diagnosis. fDM analysis was performed 1, 3, and 10 weeks from the start of treatment. Data plotted are the mean fDM values at each time point ± ...

Fig A2.
Receiver operating characteristic curve analysis to predict patient survival 1 year from diagnosis. The curves depicted represent overall predictive accuracy for radiologic response at 10 weeks and fDM-VI at 3 weeks. fDM, functional diffusion map; VI ...

Table A1.
Overall Survival As a Function of fDM and RPA


published online ahead of print at on June 9, 2008.

Supported by Grants No. PO1CA85878, PO1CA59827, 1P01CA87634, R24CA83099, and P50CA93990 from the National Institutes of Health and the National Cancer Institute.

Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.


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