The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping is a research paper published in Radiology (2020). On theSindex it has a DataRank of 1.2. It has been cited 3,976 times.
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Base Score Contribution
1.2
From this paper's citation signal
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0
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Grant: C1519/A16463
Seventh Framework Programme
Grant: ARTFORCE 257144
Engineering and Physical Sciences Research Council
Grant: EP/M507842/1
DTP - Cardiff University
European Research Council
Grant: ERC AdG-2015: 694812-Hypoximmuno
Eurostars
Grant: DART 10116
Horizon 2020 Framework Program
Grant: BD2Decide PHC-30?689715
Innovative Medicines Initiative
Grant: IMI JU QuIC-ConCePT 115151
Interreg V-A Euregio Meuse-Rhine
Grant: Euradiomics
National Cancer Institute
Grant: P30CA008748
National Institute of Neurologic Disorders and Stroke
Grant: R01NS042645
National Institutes of Health
Grant: R01CA198121
Wellcome Trust
Grant: WT203148/Z/16/Z
European Research Council
Grant: 694812
Tackling the Achilles Heel of Immunotherapy: Validating imaging biomarkers and targeting the immunological niche of tumour hypoxia
NIH HHS
Grant: S10 OD023495
NCI NIH HHS
Grant: U01 CA190234
NCI NIH HHS
Grant: U24 CA189523
NCI NIH HHS
Grant: U01 CA187947
National Institute for Health Research (NIHR)
Grant: 09/22/49
NCI NIH HHS
Grant: U01 CA143062
NCI NIH HHS
Grant: U24 CA194354
NCI NIH HHS
Grant: U24 CA180918
Swiss National Science Foundation
Grant: 10696
Banque de données régionale pour la sauvegarde des bois préhistoriques
National Institutes of Health
Grant: 2P30CA008748-43
MOUSE GENETICS
National Institutes of Health
Grant: 5U01CA187947-04
Computing, Optimizing, and Evaluating Quantitative Cancer Imaging Biomarkers
Swiss National Science Foundation
Grant: 173303
Radiomics as biomarker in multi-modality treatment of locally advanced non-small cell lung cancer
National Institutes of Health
Grant: 5U24CA189523-05
Cancer imaging phenomics software suite: application to brain and breast cancer
National Institutes of Health
Grant: 5U01CA190234-04
Genotype and Imaging Phenotype Biomarkers in Lung Cancer
National Institutes of Health
Grant: 5U24CA194354-04
Quantitative Radiomics System Decoding the Tumor Phenotype
National Institutes of Health
Grant: 5U24CA180918-02
Quantitative Image Informatics for Cancer Research (QIICR)
National Institutes of Health
Grant: 7R01NS042645-02
Modeling/Estimating brain deformation in tumor patients
French National Research Agency (ANR)
Grant: ANR-11-IDEX-0003
European Commission
Grant: 257144
Adaptive and innovative Radiation Treatment FOR improving Cancer patients treatment outcomE
Wellcome Trust
Grant: unidentified
unidentified
Swiss National Science Foundation
Grant: 154891
Highly Adaptive Computational Models of Biomedical Tissue in Radiological Images: Digital Tissue Atlases and Correlation with Genomics (MAGE)
European Commission
Grant: 335367
Biologically individualized, model-based radiotherapy on the basis of multi-parametric molecular tumour profiling
National Institutes of Health
Grant: 5U01CA143062-07
Radiomics of NSCLC
European Commission
Grant: 733008
Clinical proof of concept through a randomised phase II study: a combination of immunotherapy and stereotactic ablative radiotherapy as a curative treatment for limited metastatic lung cancer
National Institutes of Health
Grant: 5R01CA198121-02
Dose-distribution radiomics to predict morbidity risk in radiotherapy
European Commission
Grant: 689715
Big Data and models for personalized Head and Neck Cancer Decision support
European Commission
Grant: 673780
Radiomics of lung cancer (RAIL): non-invasive stratification of tumour heterogeneity for personalised cancer therapy
Wellcome Trust
Grant: 203148
King's College London Medical Engineering Centre of Research Excellence
European Commission
Grant: 115151
QUantitative Imaging in Cancer: CONnecting CEllular Processes with Therapy
European Commission
Grant: 601826
Validating predictive models of radiotherapy toxicity to improve quality-of-life and reduce side-effects in cancer survivors
Cancer Research UK
Engineering and Physical Sciences Research Council
Wellcome Trust
Wellcome Trust
FWCI
205.04
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of Correlations between baseline 18F-FDG PET tumour parameters and circulating DNA in diffuse large B cell lymphoma and Hodgkin lymphoma
Additional file 1 of Correlations between baseline 18F-FDG PET tumour parameters and circulating DNA in diffuse large B cell lymphoma and Hodgkin lymphoma
Additional file 2 of Correlations between baseline 18F-FDG PET tumour parameters and circulating DNA in diffuse large B cell lymphoma and Hodgkin lymphoma
Additional file 2 of Correlations between baseline 18F-FDG PET tumour parameters and circulating DNA in diffuse large B cell lymphoma and Hodgkin lymphoma
Additional file 3 of Correlations between baseline 18F-FDG PET tumour parameters and circulating DNA in diffuse large B cell lymphoma and Hodgkin lymphoma
Additional file 3 of Correlations between baseline 18F-FDG PET tumour parameters and circulating DNA in diffuse large B cell lymphoma and Hodgkin lymphoma
Additional file 4 of Correlations between baseline 18F-FDG PET tumour parameters and circulating DNA in diffuse large B cell lymphoma and Hodgkin lymphoma
Additional file 4 of Correlations between baseline 18F-FDG PET tumour parameters and circulating DNA in diffuse large B cell lymphoma and Hodgkin lymphoma
Additional file 1 of Asphericity of tumor FDG uptake in non-small cell lung cancer: reproducibility and implications for harmonization in multicenter studies
Additional file 1 of Asphericity of tumor FDG uptake in non-small cell lung cancer: reproducibility and implications for harmonization in multicenter studies
Additional file 2 of Asphericity of tumor FDG uptake in non-small cell lung cancer: reproducibility and implications for harmonization in multicenter studies
Additional file 2 of Asphericity of tumor FDG uptake in non-small cell lung cancer: reproducibility and implications for harmonization in multicenter studies
Additional file 1 of Tumor response prediction in 90Y radioembolization with PET-based radiomics features and absorbed dose metrics
Additional file 1 of Tumor response prediction in 90Y radioembolization with PET-based radiomics features and absorbed dose metrics
Additional file 1 of Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer
Additional file 1 of Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer
Additional file 2 of Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer
Additional file 2 of Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer
Additional file 3 of Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer
Additional file 3 of Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer