Variability and accuracy of different software packages for dynamic susceptibility contrast magnetic resonance imaging for distinguishing glioblastoma progression from pseudoprogression
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Bradley J Erickson | Rickey E Carter | Zachary S Kelm | Panagiotis D Korfiatis | Ravi K Lingineni | John R Daniels | Jan C Buckner | Daniel H Lachance | Ian F Parney | J. Buckner | B. Erickson | P. Korfiatis | R. Carter | D. Lachance | I. Parney | Z. Kelm | J. Daniels | R. Lingineni
[1] M Takahashi,et al. Posttherapeutic intraaxial brain tumor: the value of perfusion-sensitive contrast-enhanced MR imaging for differentiating tumor recurrence from nonneoplastic contrast-enhancing tissue. , 2000, AJNR. American journal of neuroradiology.
[2] Leland S. Hu,et al. Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival , 2012, Neuro-oncology.
[3] 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.
[4] Girolamo Crisi,et al. Differences in Dynamic Susceptibility Contrast MR Perfusion Maps Generated by Different Methods Implemented in Commercial Software , 2014, Journal of computer assisted tomography.
[5] Stephen M Smith,et al. Fast robust automated brain extraction , 2002, Human brain mapping.
[6] Chul-Kee Park,et al. Gliomas: Application of Cumulative Histogram Analysis of Normalized Cerebral Blood Volume on 3 T MRI to Tumor Grading , 2013, PloS one.
[7] Cem Parlak,et al. Pseudoprogression in Patients With Glioblastoma Multiforme After Concurrent Radiotherapy and Temozolomide , 2012, American journal of clinical oncology.
[8] Guido Gerig,et al. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.
[9] Glyn Johnson,et al. Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. , 2004, AJNR. American journal of neuroradiology.
[10] Daniel P Barboriak,et al. Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response. , 2006, Radiology.
[11] Steven Piantadosi,et al. Survival of Patients with Newly Diagnosed Glioblastoma Treated with Radiation and Temozolomide in Research Studies in the United States , 2010, Clinical Cancer Research.
[12] Parinaz Massoumzadeh,et al. Comparison of perfusion- and diffusion-weighted imaging parameters in brain tumor studies processed using different software platforms. , 2014, Academic radiology.
[13] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[14] D. Kong,et al. Diagnostic Dilemma of Pseudoprogression in the Treatment of Newly Diagnosed Glioblastomas: The Role of Assessing Relative Cerebral Blood Flow Volume and Oxygen-6-Methylguanine-DNA Methyltransferase Promoter Methylation Status , 2011, American Journal of Neuroradiology.
[15] W. Shi,et al. Potential utility of conventional MRI signs in diagnosing pseudoprogression in glioblastoma , 2011, Neurology.
[16] P. Box. Immediate post-radiotherapy changes in malignant glioma can mimic tumor progression , 2005 .
[17] K. McGraw,et al. Forming inferences about some intraclass correlation coefficients. , 1996 .
[18] J. Le Bas,et al. Perfusion magnetic resonance imaging: comparison of semiologic characteristics in first-pass perfusion of brain tumors at 1.5 and 3 Tesla. , 2012, Journal of neuroradiology. Journal de neuroradiologie.
[19] R M Weisskoff,et al. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. , 2006, AJNR. American journal of neuroradiology.
[20] J. Boxerman,et al. The Role of Preload and Leakage Correction in Gadolinium-Based Cerebral Blood Volume Estimation Determined by Comparison with MION as a Criterion Standard , 2012, American Journal of Neuroradiology.
[21] N. Otsu. A threshold selection method from gray level histograms , 1979 .
[22] Susan Chang,et al. Pseudoprogression and pseudoresponse: Challenges in brain tumor imaging , 2009, Current neurology and neuroscience reports.
[23] Hiroki Shirato,et al. Accuracy and reliability assessment of CT and MR perfusion analysis software using a digital phantom. , 2013, Radiology.
[24] Ronald L. Wolf,et al. Posttreatment recurrence of malignant brain neoplasm: accuracy of relative cerebral blood volume fraction in discriminating low from high malignant histologic volume fraction. , 2009, Radiology.
[25] 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.
[26] Ramazan Yildiz,et al. Radiation induced early necrosis in patients with malignant gliomas receiving temozolomide , 2010, Clinical Neurology and Neurosurgery.
[27] K. Schmainda,et al. Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors. , 2008, Radiology.
[28] Michael Brady,et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.