Decoding the Interdependence of Multiparametric Magnetic Resonance Imaging to Reveal Patient Subgroups Correlated with Survivals12
暂无分享,去创建一个
F. Markowetz | C. Schönlieb | S. Price | Chao Li | Shuo Wang | Pan Liu | T. Torheim | Natalie R Boonzaier | Bart Rj van Dijken
[1] Ruijiang Li,et al. Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy. , 2018, Radiology.
[2] G. Reifenberger,et al. European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. , 2017, The Lancet. Oncology.
[3] S. Price,et al. Multiparametric MR Imaging of Diffusion and Perfusion in Contrast-enhancing and Nonenhancing Components in Patients with Glioblastoma. , 2017, Radiology.
[4] Ruijiang Li,et al. Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma , 2017, European Radiology.
[5] D. Ikeda,et al. Unsupervised Clustering of Quantitative Image Phenotypes Reveals Breast Cancer Subtypes with Distinct Prognoses and Molecular Pathways , 2017, Clinical Cancer Research.
[6] Ruijiang Li,et al. Intratumor partitioning and texture analysis of dynamic contrast‐enhanced (DCE)‐MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy , 2016, Journal of magnetic resonance imaging : JMRI.
[7] Guillaume A. Rousselet,et al. A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula , 2016, bioRxiv.
[8] M. McLean,et al. Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas , 2015, Journal of magnetic resonance imaging : JMRI.
[9] Teresa Wu,et al. Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma , 2015, PloS one.
[10] Malika Charrad,et al. NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set , 2014 .
[11] B. Rosen,et al. Advanced magnetic resonance imaging of the physical processes in human glioblastoma. , 2014, Cancer research.
[12] R. Gillies,et al. Quantitative imaging in cancer evolution and ecology. , 2013, Radiology.
[13] M. Junttila,et al. Influence of tumour micro-environment heterogeneity on therapeutic response , 2013, Nature.
[14] Patrick Y Wen,et al. Application of Novel Response/Progression Measures for Surgically Delivered Therapies for Gliomas: Response Assessment in Neuro-Oncology (RANO) Working Group , 2012, Neurosurgery.
[15] J. Gillard,et al. Correlation of MR Relative Cerebral Blood Volume Measurements with Cellular Density and Proliferation in High-Grade Gliomas: An Image-Guided Biopsy Study , 2011, American Journal of Neuroradiology.
[16] 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.
[17] S. Gabriel,et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. , 2010, Cancer cell.
[18] R. Kreis. Issues of spectral quality in clinical 1H‐magnetic resonance spectroscopy and a gallery of artifacts , 2004, NMR in biomedicine.
[19] Michael Brady,et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.
[20] R. Gillies,et al. Causes and effects of heterogeneous perfusion in tumors. , 1999, Neoplasia.
[21] R. Nelsen. An Introduction to Copulas , 1998 .
[22] T Nakada,et al. Localized proton spectroscopy of focal brain pathology in humans: Significant effects of edema on spin–spin relaxation time , 1994, Magnetic resonance in medicine.
[23] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..