Integrated Systems and Technologies : Mathematical Oncology Response Classi fi cation Based on a Minimal Model of Glioblastoma Growth Is Prognostic for Clinical Outcomes and Distinguishes Progression from Pseudoprogression

Glioblastoma multiforme is the most aggressive type of primary brain tumor. Glioblastoma growth dynamics vary widely across patients, making it difficult to accurately gauge their response to treatment. We developed a model-based metric of therapy response called Days Gained that accounts for this heterogeneity. Here, we show in 63 newly diagnosed patients with glioblastoma that Days Gained scores from a simple glioblastoma growth model computed at the time of the first postradiotherapy MRI scan are prognostic for time to tumor recurrence and overall patient survival. After radiation treatment, Days Gained also distinguished patients with pseudoprogression from those with true progression. Because Days Gained scores can be easily computed with routinely available clinical imaging devices, this model offers immediate potential to be used in ongoing prospective studies. Cancer Res; 73(10); 2976–86. 2013

[1]  Kristin R. Swanson,et al.  Discriminating Survival Outcomes in Patients with Glioblastoma Using a Simulation-Based, Patient-Specific Response Metric , 2013, PloS one.

[2]  Kevin W Eliceiri,et al.  NIH Image to ImageJ: 25 years of image analysis , 2012, Nature Methods.

[3]  Paul E Kinahan,et al.  Applying a patient-specific bio-mathematical model of glioma growth to develop virtual [18F]-FMISO-PET images. , 2011, Mathematical medicine and biology : a journal of the IMA.

[4]  A G Sorensen,et al.  Pseudoprogression and Pseudoresponse: Imaging Challenges in the Assessment of Posttreatment Glioma , 2011, American Journal of Neuroradiology.

[5]  D. Born,et al.  Pseudoprogression: Relevance With Respect to Treatment of High-Grade Gliomas , 2011, Current treatment options in oncology.

[6]  Xavier Robin,et al.  pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.

[7]  Peter Canoll,et al.  Magnetic Resonance Imaging Characteristics of Glioblastoma Multiforme: Implications for Understanding Glioma Ontogeny , 2010, Neurosurgery.

[8]  K Hendrickson,et al.  Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach , 2010, Physics in medicine and biology.

[9]  J. Uhm Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group , 2010 .

[10]  Roy C. P. Kerckhoffs,et al.  Current progress in patient-specific modeling , 2010, Briefings Bioinform..

[11]  M. Okada,et al.  [New response evaluation criteria in solid tumours-revised RECIST guideline (version 1.1)]. , 2009, Gan to kagaku ryoho. Cancer & chemotherapy.

[12]  Albert Lai,et al.  Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model. , 2009, Cancer research.

[13]  M. J. van den Bent,et al.  Pseudoprogression and pseudoresponse in the treatment of gliomas , 2009, Current opinion in neurology.

[14]  Gargi Chakraborty,et al.  Quantitative metrics of net proliferation and invasion link biological aggressiveness assessed by MRI with hypoxia assessed by FMISO-PET in newly diagnosed glioblastomas. , 2009, Cancer research.

[15]  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.

[16]  K. Swanson,et al.  A mathematical model for brain tumor response to radiation therapy , 2009, Journal of mathematical biology.

[17]  Dieta Brandsma,et al.  Incidence of early pseudo‐progression in a cohort of malignant glioma patients treated with chemoirradiation with temozolomide , 2008, Cancer.

[18]  J M Wardlaw,et al.  Velocity of radial expansion of contrast-enhancing gliomas and the effectiveness of radiotherapy in individual patients: a proof of principle. , 2008, Clinical oncology (Royal College of Radiologists (Great Britain)).

[19]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[20]  Osman Ratib,et al.  OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images , 2004, Journal of Digital Imaging.

[21]  Laurent Capelle,et al.  Continuous growth of mean tumor diameter in a subset of grade II gliomas , 2003, Annals of neurology.

[22]  Reto Meuli,et al.  Promising survival for patients with newly diagnosed glioblastoma multiforme treated with concomitant radiation plus temozolomide followed by adjuvant temozolomide. , 2002, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[23]  Matthew J. McAuliffe,et al.  Medical Image Processing, Analysis and Visualization in clinical research , 2001, Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001.

[24]  J. Murray,et al.  A quantitative model for differential motility of gliomas in grey and white matter , 2000, Cell proliferation.

[25]  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.

[26]  J. Hanley The Robustness of the "Binormal" Assumptions Used in Fitting ROC Curves , 1988, Medical decision making : an international journal of the Society for Medical Decision Making.

[27]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[28]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .