Publications of R. De Crevoisier sorted by first author
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, , , , , , , and , Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy (2016), in: Critical Reviews in Oncology/Hematology, 108(40-51)
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, , , , , and , Metabolic Tumor Volume and Total Lesion Glycolysis in Oropharyngeal Cancer treated with definitive radiotherapy: Which threshold is the best predictor of local control ? (2017), in: Clinical Nuclear Medicine, 42:6(e281–e285)
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, , , , , , , , , , , , and , PET-based predictive survival model after radiotherapy for head and neck cancer (2019), in: European Journal of Nuclear Medicine and Molecular Imaging, 46:3(638-649)
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| , , , , , , , , and , Valeur de la TEP au 18-FDG pour prédire la récidive dans les cancers ORL non oropharyngé traités par radio-chimiothérapie, in: Société Française de radiothérapie Oncologique, 2017 |
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, , , , , , , , , , , , and , A PET-based nomogram for oropharyngeal cancers (2017), in: European Journal of Cancer, 75(222-230)
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, , , , , , , , , and , FDG-PET/CT-based prognostic survival model after surgery for head and neck cancer (2022), in: Journal of Nuclear Medicine, 63:1
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| , , , , and , Comparing 18-FDG PET 3D texture attributes for the prediction of survival and recurrence in oropharyngeal cancers treated with radiotherapy, in: Workshop on the Prediction and Modeling of response to Molecular and External Beam Radiotherapies, Le Bono, France, 2017 |
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, , , , and , The Importance of Feature Aggregation in Radiomics: A Head and Neck Cancer Study (2020), in: Nature Scientific Reports, 10:19679
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| , , , , , , and , How to find the best radiomics features for prediction of overall survival in SBRT for hepatocellular carcinoma?, in: European SocieTy for Radiotherapy & Oncology, 2019 |
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, , , , , , , , , and , Cleaning Radiotherapy Contours for Radiomics Studies, is it Worth it? A Head and Neck Cancer Study (2022), in: Clinical and Translational Radiation Oncology, 33(153-158)
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| , , , , , and , Comparison of feature selection in radiomics for the prediction of overall survival after radiotherapy for hepatocellular carcinoma, in: IEEE Engineering in Medicine and Biology Conference, 2020 |
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