Prospective Analysis of Parametric Response Map–Derived MRI Biomarkers: Identification of Early and Distinct Glioma Response Patterns Not Predicted by Standard Radiographic Assessment

Purpose: Currently, radiologic response of brain tumors is assessed according to the Macdonald criteria 10 weeks from the start of therapy. There exists a critical need to identify nonresponding patients early in the course of their therapy for consideration of alternative treatment strategies. Our study assessed the effectiveness of the parametric response map (PRM) imaging biomarker to provide for an earlier measure of patient survival prediction. Experimental Design: Forty-five high-grade glioma patients received concurrent chemoradiation. Quantitative MRI including apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps were acquired pretreatment and 3 weeks midtreatment on a prospective institutional-approved study. PRM, a voxel-by-voxel image analysis method, was evaluated as an early prognostic biomarker of overall survival. Clinical and conventional MR parameters were also evaluated. Results: Multivariate analysis showed that PRMADC+ in combination with PRMrCBV− obtained at week 3 had a stronger correlation to 1-year and overall survival rates than any baseline clinical or treatment response imaging metric. The composite biomarker identified three distinct patient groups, nonresponders [median survival (MS) of 5.5 months, 95% CI: 4.4–6.6 months], partial responders (MS of 16 months, 95% CI: 8.6–23.4 months), and responders (MS has not yet been reached). Conclusions: Inclusion of PRMADC+ and PRMrCBV− into a single imaging biomarker metric provided early identification of patients resistant to standard chemoradiation. In comparison to the current standard of assessment of response at 10 weeks (Macdonald criteria), the composite PRM biomarker potentially provides a useful opportunity for clinicians to identify patients who may benefit from alternative treatment strategies. Clin Cancer Res; 17(14); 4751–60. ©2011 AACR.

[1]  R. Mirimanoff,et al.  Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. , 2005, The New England journal of medicine.

[2]  B. Rosen,et al.  High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: Experimental comparison and preliminary results , 1996, Magnetic resonance in medicine.

[3]  Scott Fields,et al.  Mapping pathophysiological features of breast tumors by MRI at high spatial resolution , 1997, Nature Medicine.

[4]  Yue Cao,et al.  Survival prediction in high-grade gliomas by MRI perfusion before and during early stage of RT [corrected]. , 2006, International journal of radiation oncology, biology, physics.

[5]  Timothy D Johnson,et al.  Functional diffusion map as an early imaging biomarker for high-grade glioma: correlation with conventional radiologic response and overall survival. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[6]  Thomas L Chenevert,et al.  Diffusion imaging for therapy response assessment of brain tumor. , 2009, Neuroimaging clinics of North America.

[7]  Glyn Johnson,et al.  Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. , 2003, AJNR. American journal of neuroradiology.

[8]  R. Kauppinen,et al.  Monitoring of gliomas in vivo by diffusion MRI and 1H MRS during gene therapy‐induced apoptosis: interrelationships between water diffusion and mobile lipids , 2009, NMR in biomedicine.

[9]  Benedick A Fraass,et al.  Survival and failure patterns of high-grade gliomas after three-dimensional conformal radiotherapy. , 2002, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[10]  P. Tofts.  Modeling tracer kinetics in dynamic Gd‐DTPA MR imaging , 1997, Journal of magnetic resonance imaging : JMRI.

[11]  Bradford A Moffat,et al.  Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[12]  B. Rosen,et al.  Perfusion imaging with NMR contrast agents , 1990, Magnetic resonance in medicine.

[13]  J E Heiserman,et al.  Relative Cerebral Blood Volume Values to Differentiate High-Grade Glioma Recurrence from Posttreatment Radiation Effect: Direct Correlation between Image-Guided Tissue Histopathology and Localized Dynamic Susceptibility-Weighted Contrast-Enhanced Perfusion MR Imaging Measurements , 2009, American Journal of Neuroradiology.

[14]  D. Nelson,et al.  Recursive partitioning analysis of prognostic factors in three Radiation Therapy Oncology Group malignant glioma trials. , 1993, Journal of the National Cancer Institute.

[15]  Kathleen M. Schmainda,et al.  Utility of functional diffusion maps to monitor a patient diagnosed with gliomatosis cerebri , 2010, Journal of Neuro-Oncology.

[16]  T. Chenevert,et al.  Diffusion magnetic resonance imaging: an imaging treatment response biomarker to chemoradiotherapy in a mouse model of squamous cell cancer of the head and neck. , 2008, Translational oncology.

[17]  R. Schilsky.  How not to treat cancer. , 2008, The Lancet. Oncology.

[18]  Fiona J Gilbert,et al.  Use of new imaging techniques to predict tumour response to therapy. , 2010, The Lancet. Oncology.

[19]  S. Ng,et al.  Significant Temporal Evolution of Diffusion Anisotropy for Evaluating Early Response to Radiosurgery in Patients with Vestibular Schwannoma: Findings from Functional Diffusion Maps , 2010, American Journal of Neuroradiology.

[20]  J. M. Taylor,et al.  Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. , 2000, Journal of the National Cancer Institute.

[21]  P. LaViolette,et al.  Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity , 2010, Journal of magnetic resonance imaging : JMRI.

[22]  M. Law,et al.  Magnetic resonance perfusion and permeability imaging in brain tumors. , 2009, Neuroimaging clinics of North America.

[23]  T. Cascino,et al.  Response criteria for phase II studies of supratentorial malignant glioma. , 1990, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[24]  Charles R. Meyer,et al.  Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations , 1997, Medical Image Anal..

[25]  Bradford A Moffat,et al.  A feasibility study of parametric response map analysis of diffusion-weighted magnetic resonance imaging scans of head and neck cancer patients for providing early detection of therapeutic efficacy. , 2009, Translational oncology.

[26]  P. Kelly,et al.  Perfusion Magnetic Resonance Imaging Predicts Patient Outcome as an Adjunct to Histopathology: A Second Reference Standard in the Surgical and Nonsurgical Treatment of Low-grade Gliomas , 2006, Neurosurgery.

[27]  Yue Cao,et al.  Clinical investigation survival prediction in high-grade gliomas by MRI perfusion before and during early stage of RT , 2006 .

[28]  Timothy D Johnson,et al.  Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[29]  K. Aldape,et al.  Beyond grade: molecular pathology of malignant gliomas. , 2009, Seminars in radiation oncology.

[30]  Alessia Pica,et al.  Phase I/IIa study of cilengitide and temozolomide with concomitant radiotherapy followed by cilengitide and temozolomide maintenance therapy in patients with newly diagnosed glioblastoma. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[31]  R. Schilsky.  Personalized medicine in oncology: the future is now , 2010, Nature Reviews Drug Discovery.

[32]  R. Lucht,et al.  Microcirculation and microvasculature in breast tumors: Pharmacokinetic analysis of dynamic MR image series , 2004, Magnetic resonance in medicine.

[33]  Timothy D Johnson,et al.  The parametric response map is an imaging biomarker for early cancer treatment outcome , 2009, Nature Medicine.

[34]  Bing Ma,et al.  Voxel-by-Voxel Functional Diffusion Mapping for Early Evaluation of Breast Cancer Treatment , 2009, IPMI.

[35]  J. Nevins,et al.  Refocusing the War on Cancer: The Critical Role of Personalized Treatment , 2010, Science Translational Medicine.

[36]  B. Rosen,et al.  High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis , 1996, Magnetic resonance in medicine.

[37]  Yanlei Li,et al.  Diffusion-weighted imaging in predicting and monitoring the response of uterine cervical cancer to combined chemoradiation. , 2009, Clinical radiology.

[38]  Timothy D Johnson,et al.  A feasibility study evaluating the functional diffusion map as a predictive imaging biomarker for detection of treatment response in a patient with metastatic prostate cancer to the bone. , 2007, Neoplasia.

[39]  Susan M. Chang,et al.  Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[40]  R. Mirimanoff,et al.  Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. , 2009, The Lancet. Oncology.

[41]  D. Norman,et al.  Criteria for evaluating patients undergoing chemotherapy for malignant brain tumors. , 1977, Journal of neurosurgery.