scholarly article | Q13442814 |
P50 | author | Penghao Liu | Q92432687 |
P2093 | author name string | Yu Wang | |
Xin Cheng | |||
Wenbin Ma | |||
Shi Feng | |||
Yaning Wang | |||
Dachun Zhao | |||
Wenlin Chen | |||
Jiatong Li | |||
Ziren Kong | |||
Chendan Jiang | |||
Ruizhe Zhu | |||
P2860 | cites work | The Design of SimpleITK | Q28660957 |
pROC: an open-source package for R and S+ to analyze and compare ROC curves | Q30050695 | ||
A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research | Q30368733 | ||
Non-invasive metabolic imaging of brain tumours in the era of precision medicine | Q31115540 | ||
A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome | Q36174403 | ||
Classification of cerebral lymphomas and glioblastomas featuring luminance distribution analysis | Q36948495 | ||
Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer | Q38679084 | ||
Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study | Q38841400 | ||
Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma | Q38975001 | ||
Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis | Q39389025 | ||
Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non-Small Cell Lung Cancer | Q40559241 | ||
18F-FDG PET is an independent outcome predictor in primary central nervous system lymphoma | Q45406205 | ||
Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning. | Q45947583 | ||
Evaluation of tumor FDG transport and metabolism in primary central nervous system lymphoma using [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) kinetic analysis | Q46917929 | ||
Computational Radiomics System to Decode the Radiographic Phenotype | Q47623355 | ||
Radiomics: the bridge between medical imaging and personalized medicine | Q47869134 | ||
CBTRUS Statistical Report: Primary brain and other central nervous system tumors diagnosed in the United States in 2010-2014. | Q47979443 | ||
Common malignant brain tumors: can 18F-FDG PET/CT aid in differentiation? | Q48107925 | ||
Advances in the management of primary central nervous system lymphoma. | Q49823622 | ||
Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation. | Q50041531 | ||
MRI features predict p53 status in lower-grade gliomas via a machine-learning approach. | Q50419924 | ||
Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach. | Q52601610 | ||
18F-FDG PET radiomics approaches: comparing and clustering features in cervical cancer. | Q52936358 | ||
The diagnostic role of (18)F-FDG PET for primary central nervous system lymphoma. | Q53069155 | ||
FDG-PET/CT in the management of lymphomas: current status and future directions | Q57142031 | ||
A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas | Q58557979 | ||
Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy | Q64124983 | ||
Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas. | Q64899298 | ||
Robustness versus disease differentiation when varying parameter settings in radiomics features: application to nasopharyngeal PET/CT | Q88008357 | ||
18F-FDG PET/CT in immunocompetent patients with primary central nervous system lymphoma: Differentiation from glioblastoma and correlation with DWI | Q88953563 | ||
Prediction of survival with multi-scale radiomic analysis in glioblastoma patients | Q89145868 | ||
Radiomics in Oncological PET/CT: Clinical Applications | Q89244750 | ||
Novel Radiomic Features Based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time | Q89556450 | ||
Primary brain tumours in adults | Q90643353 | ||
Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI | Q91556689 | ||
P4510 | describes a project that uses | scikit-image | Q22442795 |
P921 | main subject | central nervous system lymphoma | Q136312 |
glioblastoma | Q282142 | ||
primary central nervous system lymphoma | Q3246818 | ||
P304 | page(s) | 101912 | |
P577 | publication date | 2019-06-27 | |
P1433 | published in | NeuroImage: Clinical | Q22907265 |
P1476 | title | 18F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma | |
P478 | volume | 23 |
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