Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma.
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Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation SoftwareA quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome.Advanced MRI Techniques in the Monitoring of Treatment of Gliomas.Immunotherapy in glioblastoma: emerging options in precision medicineComputer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility StudyMRI features predict survival and molecular markers in diffuse lower-grade gliomas.Multiple-response regression analysis links magnetic resonance imaging features to de-regulated protein expression and pathway activity in lower grade glioma."Radio-oncomics" : The potential of radiomics in radiation oncology.Radiomic model for predicting mutations in the isocitrate dehydrogenase gene in glioblastomas.Quantitative Imaging Biomarkers for Risk Stratification of Patients with Recurrent Glioblastoma Treated with Bevacizumab.Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival.Tumor image-derived texture features are associated with CD3 T-cell infiltration status in glioblastoma.Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma.MR textural analysis on T2 FLAIR images for the prediction of true oligodendroglioma by the 2016 WHO genetic classification.2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution.Radiogenomics to characterize regional genetic heterogeneity in glioblastoma.Radiomic subtyping improves disease stratification beyond key molecular, clinical and standard imaging characteristics in patients with glioblastoma.The crucial role of multiomic approach in cancer research and clinically relevant outcomes.Noninvasive Glioblastoma Testing: Multimodal Approach to Monitoring and Predicting Treatment Response.Haralick textural features on T2 -weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer.
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P2860
Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma.
description
2015 nî lūn-bûn
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2015年の論文
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2015年学术文章
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2015年学术文章
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2015年学术文章
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2015年学术文章
@zh-my
2015年学术文章
@zh-sg
2015年學術文章
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2015年學術文章
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2015年學術文章
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name
Evaluation of tumor-derived MR ...... rvival status in glioblastoma.
@en
Evaluation of tumor-derived MR ...... rvival status in glioblastoma.
@nl
type
label
Evaluation of tumor-derived MR ...... rvival status in glioblastoma.
@en
Evaluation of tumor-derived MR ...... rvival status in glioblastoma.
@nl
prefLabel
Evaluation of tumor-derived MR ...... rvival status in glioblastoma.
@en
Evaluation of tumor-derived MR ...... rvival status in glioblastoma.
@nl
P2093
P2860
P356
P1433
P1476
Evaluation of tumor-derived MR ...... rvival status in glioblastoma.
@en
P2093
Arvind Rao
Ashok Veeraraghavan
Ganesh Rao
Juan Martinez
P2860
P304
P356
10.1118/1.4934373
P407
P577
2015-11-01T00:00:00Z