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Originally published as JCO Early Release 10.1200/JCO.2007.15.2363 on June 9 2008 © 2008 American Society of Clinical Oncology.
Functional Diffusion Map As an Early Imaging Biomarker for High-Grade Glioma: Correlation With Conventional Radiologic Response and Overall Survival
From the Departments of Radiology, Radiation Oncology, Neurology, and Biostatistics; and the Center for Molecular Imaging, University of Michigan Medical Center, Ann Arbor, Michigan Corresponding author: Thomas L. Chenevert, PhD, University of Michigan, B2A209 UH 1500 East Medical Center Dr, Ann Arbor, MI 48109-0030; e-mail: tlchenev{at}umich.edu
Purpose 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. Results 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. Conclusion 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 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
Treatment
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
Diffusion MRI
fDM Analysis
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
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
Changes in fDM 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
Overall Survival As a Function of fDM Stratification and RR
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.
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
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
published online ahead of print at www.jco.org 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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Copyright © 2008 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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ABSTRACT
INTRODUCTION
4.7%. Median survival was 10.9 versus 52.6 months, respectively (P < .003; hazard ratio [HR] = 2.7; 95% CI, 1.5 to 5.9). (B) Overall survival as a function of RR at 10 weeks from the start of treatment, where the yellow curve (n = 25) represents those patients with progressive disease and the upper blue curve represents those with stable disease (n = 27) or partial response (n = 3). Median survival was 10.9 versus 31.6 months, respectively (P < .0007, HR = 2.9; 95% CI, 1.7 to 7.2). (C) Overall survival as a function of the composite index of response. The combination of fDM stratification at 3 weeks from the start of treatment and later radiographic response at 10 weeks provides a robust predictor of patient survival (log-rank P < .0002). Both the intermediate responding population (gray line, middle curve; n = 14; median survival, 14.4 months; P < .02) and the best responding population (yellow line, top curve; n = 22; median survival, 52.6 months; P < .0001) were distinct from the worst-responding radiographic group (blue line, bottom curve; n = 19; median survival, 8.1 months). VI, areas within the tumor where apparent diffusion coefficient increased (> 55 x 10–5 mm2/sec).
stable disease [SD] 
