Texture Analysis of the Apparent Diffusion Coefficient Focused on Contrast-Enhancing Lesions in Predicting Survival for Bevacizumab-Treated Patients with Recurrent Glioblastoma
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J. Puig | S. Thió-Henestrosa | A. López-Rueda | L. Oleaga | C. Zwanzger | E. Pineda | I. Aldecoa | I. Valduvieco | T. Pujol | José Juan González | Javier Luis Moreno-Negrete
[1] P. Perrini,et al. Old and New Systemic Immune-Inflammation Indexes Are Associated with Overall Survival of Glioblastoma Patients Treated with Radio-Chemotherapy , 2022, Genes.
[2] I. Pesaresi,et al. Role of magnetic resonance imaging following postoperative radiotherapy in clinical decision-making of patients with high-grade glioma , 2022, La radiologia medica.
[3] I. Baranowska-Bosiacka,et al. Epidemiology of Glioblastoma Multiforme–Literature Review , 2022, Cancers.
[4] A. Hagiwara,et al. Pretreatment ADC Histogram Analysis as a Prognostic Imaging Biomarker for Patients with Recurrent Glioblastoma Treated with Bevacizumab: A Systematic Review and Meta-analysis , 2022, American Journal of Neuroradiology.
[5] J. Barnholtz-Sloan,et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014-2018. , 2021, Neuro-oncology.
[6] F. Prados,et al. Diffusion-Weighted Imaging: Recent Advances and Applications. , 2021, Seminars in ultrasound, CT, and MR.
[7] G. Reifenberger,et al. Radiomic Analysis to Predict Outcome in Recurrent Glioblastoma Based on Multi-Center MR Imaging From the Prospective DIRECTOR Trial , 2021, Frontiers in Oncology.
[8] P. Wen,et al. Assessment of tumor hypoxia and perfusion in recurrent glioblastoma following bevacizumab failure using MRI and 18F-FMISO PET , 2021, Scientific Reports.
[9] A. Oronsky,et al. A Review of Newly Diagnosed Glioblastoma , 2021, Frontiers in Oncology.
[10] Amit Agarwal,et al. Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models , 2021, The neuroradiology journal.
[11] P. Wen,et al. Validation of diffusion MRI as a biomarker for efficacy using randomized phase III trial of bevacizumab with or without VB-111 in recurrent glioblastoma , 2021, Neuro-oncology advances.
[12] H. Sharma,et al. Advanced multimodal imaging in differentiating glioma recurrence from post-radiotherapy changes. , 2020, International review of neurobiology.
[13] T. Cloughesy,et al. Diffusion Magnetic Resonance Imaging Phenotypes Predict Overall Survival Benefit From Bevacizumab or Surgery in Recurrent Glioblastoma With Large Tumor Burden. , 2020, Neurosurgery.
[14] Christos Davatzikos,et al. Histopathology‐validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo‐progression in glioblastoma , 2020, Cancer.
[15] R. Verhaak,et al. Molecular Evolution of IDH Wild-Type Glioblastomas Treated With Standard of Care Affects Survival and Design of Precision Medicine Trials: A Report From the EORTC 1542 Study. , 2019, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[16] Kazunari Yoshida,et al. The role of vascular endothelial growth factor in the hypoxic and immunosuppressive tumor microenvironment: perspectives for therapeutic implications , 2019, Medical Oncology.
[17] K. Ahn,et al. Analysis of heterogeneity of peritumoral T2 hyperintensity in patients with pretreatment glioblastoma: Prognostic value of MRI-based radiomics. , 2019, European journal of radiology.
[18] Leonard Wee,et al. Technical Note: Ontology‐guided radiomics analysis workflow (O‐RAW) , 2019, Medical physics.
[19] Yonehiro Kanemura,et al. Radiomics and MGMT promoter methylation for prognostication of newly diagnosed glioblastoma , 2019, Scientific Reports.
[20] S. Priya,et al. Texture Analysis in Cerebral Gliomas: A Review of the Literature , 2019, American Journal of Neuroradiology.
[21] Tamim Niazi,et al. Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation , 2019, Front. Oncol..
[22] J. Pallud,et al. A Meta-Analysis of Survival Outcomes Following Reoperation in Recurrent Glioblastoma: Time to Consider the Timing of Reoperation , 2019, Front. Neurol..
[23] David T. W. Jones,et al. Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma , 2018, Neuro-oncology.
[24] Lawrence H. Schwartz,et al. Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab , 2017, Neuro-oncology.
[25] Andriy Fedorov,et al. Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.
[26] Maximilian Reiser,et al. Radiomic Analysis Reveals Prognostic Information in T1-Weighted Baseline Magnetic Resonance Imaging in Patients With Glioblastoma , 2017, Investigative radiology.
[27] M. Götz,et al. Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response , 2016, Clinical Cancer Research.
[28] H. Vargas,et al. Intravoxel Incoherent Motion-derived Histogram Metrics for Assessment of Response after Combined Chemotherapy and Radiation Therapy in Rectal Cancer: Initial Experience and Comparison between Single-Section and Volumetric Analyses. , 2016, Radiology.
[29] Juan J. Martinez,et al. Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma. , 2015, Medical physics.
[30] Timothy C Ryken,et al. Toward precision medicine in glioblastoma: the promise and the challenges. , 2015, Neuro-oncology.
[31] P. Wen,et al. One size should not fit all: advancing toward personalized glioblastoma therapy. , 2015, Discovery medicine.
[32] Eric Stindel,et al. Prognostic value of multimodal MRI tumor features in Glioblastoma multiforme using textural features analysis , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).
[33] Susan M. Chang,et al. A single-institution phase II trial of radiation, temozolomide, erlotinib, and bevacizumab for initial treatment of glioblastoma. , 2014, Neuro-oncology.
[34] W. Mason,et al. Pretreatment ADC Histogram Analysis Is a Predictive Imaging Biomarker for Bevacizumab Treatment but Not Chemotherapy in Recurrent Glioblastoma , 2014, American Journal of Neuroradiology.
[35] K. Aldape,et al. A randomized trial of bevacizumab for newly diagnosed glioblastoma. , 2014, The New England journal of medicine.
[36] K. Hoang-Xuan,et al. Bevacizumab plus radiotherapy-temozolomide for newly diagnosed glioblastoma. , 2014, The New England journal of medicine.
[37] Milan Sonka,et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. , 2012, Magnetic resonance imaging.
[38] Namkug Kim,et al. Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas. , 2012, Radiology.
[39] T. Mikkelsen,et al. Apparent diffusion coefficient histogram analysis stratifies progression-free and overall survival in patients with recurrent GBM treated with bevacizumab: a multi-center study , 2012, Journal of Neuro-Oncology.
[40] T. Batchelor,et al. Antiangiogenic Therapy for Glioblastoma , 2012, Cancer journal.
[41] T. Cloughesy,et al. Graded functional diffusion map-defined characteristics of apparent diffusion coefficients predict overall survival in recurrent glioblastoma treated with bevacizumab. , 2011, Neuro-oncology.
[42] M S Brown,et al. Apparent Diffusion Coefficient Histogram Analysis Stratifies Progression-Free Survival in Newly Diagnosed Bevacizumab-Treated Glioblastoma , 2011, American Journal of Neuroradiology.
[43] P. LaViolette,et al. Volumetric analysis of functional diffusion maps is a predictive imaging biomarker for cytotoxic and anti-angiogenic treatments in malignant gliomas , 2011, Journal of Neuro-Oncology.
[44] Paul S Mischel,et al. Phase II study of bevacizumab plus temozolomide during and after radiation therapy for patients with newly diagnosed glioblastoma multiforme. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[45] T. Mikkelsen,et al. Imaging response criteria for recurrent gliomas treated with bevacizumab: Role of diffusion weighted imaging as an imaging biomarker , 2010, Journal of Neuro-Oncology.
[46] T. Mikkelsen,et al. Bevacizumab alone and in combination with irinotecan in recurrent glioblastoma. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[47] Matthew S. Brown,et al. Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. , 2009, Radiology.
[48] D. Barboriak,et al. Repeatability of quantitative parameters derived from diffusion tensor imaging in patients with glioblastoma multiforme , 2009, Journal of magnetic resonance imaging : JMRI.
[49] Maria I. Argyropoulou,et al. Glioma recurrence versus radiation necrosis: accuracy of current imaging modalities , 2009, Journal of Neuro-Oncology.
[50] S. Nelson,et al. Bevacizumab and chemotherapy for recurrent glioblastoma , 2009, Neurology.
[51] John A Butman,et al. Phase II trial of single-agent bevacizumab followed by bevacizumab plus irinotecan at tumor progression in recurrent glioblastoma. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[52] David J Collins,et al. Technology Insight: water diffusion MRI—a potential new biomarker of response to cancer therapy , 2008, Nature Clinical Practice Oncology.
[53] B. Ross,et al. Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[54] E. Tali,et al. Is There a Role for Apparent Diffusion Coefficients in the Differential Diagnosis of Brain Tumors? , 2006, The neuroradiology journal.
[55] 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.
[56] M. D. de Lemos,et al. Clinical effectiveness of bevacizumab in patients with recurrent brain tumours: A population-based evaluation , 2018, Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners.