Publicaties van Joel Castelli gesorteerd op nieuwheid
| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , en , Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT, pagina's 1-30, Springer, Cham, 2023 |
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| , , , , en , Segmentation and Classification of Head and Neck Nodal Metastases and Primary Tumors in PET/CT, in: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pagina's 4731-4735, 2022 |
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, , , , , , , , , en , 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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, , , , , , , , , en , 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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, , , , , , , , , , , , , , , , , , , , , , , , , en , Head and Neck Tumor Segmentation in PET/CT: The HECKTOR Challenge (2022), in: Medical Image Analysis, 77(102336)
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| , , , , , en , Multi-Task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer, in: 4th Workshop on PRedictive Intelligence in MEdicine, pagina's 147-156, Springer LNCS, 2021 |
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| , , , , , en , Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT, in: Multimodal Learning for Clinical Decision Support, pagina's 59-68, Springer LNCS, 2021 |
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, , , , en , The Importance of Feature Aggregation in Radiomics: A Head and Neck Cancer Study (2020), in: Nature Scientific Reports, 10:19679
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, , , , , en , Evaluation of the Prognostic Value of FDG PET/CT Parameters for Patients with Surgically Treated Head and Neck Cancer: A Systematic Review (2020), in: JAMA Otolaryngology - Head and Neck Surgery, 146:5(471-479)
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| , , , , , , , en , Automatic Segmentation of Head and Neck Tumors and Nodal Metastases in PET-CT scans, in: Medical Imaging with Deep Learning, Montréal, Canada, 2020 |
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| , , , , , , en , 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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| , , , , , en , 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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, , , , , , , , , , , , en , 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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| , , , , en , 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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| , , , , , , en , 18-FDG PET-CT parameters to predict survival and recurrence in cervical cancer patients treated with chemo-radiotherapy, in: European Society for Radiotherapy and Oncology, Vienna, 2017 |
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| , , , , , , , , en , 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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| , , , , , , , , en , Nouveaux paramètres métaboliques du FDG-PET/TDM pour prédire la récurrence et la survie des cancers du col utérin traité par radio-chimiothérapie, in: Société Française de radiothérapie Oncologique, 2017 |
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| , , , , , en , QuantImage: An Online Tool for High-Throughput 3D Radiomics Feature Extraction in PET-CT, in: Biomedical Texture Analysis: Fundamentals, Applications and Tools, pagina's 349-377, Elsevier, 2017 |
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| , , , , , en , (18F)-FDG PET/CT parameters to predict survival and recurrence in patients with locally advanced cervical cancer treated with chemoradiotherapy (2018), in: Cancer / Radiothérapie, 22:3(229-235) |
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, , , , , en , 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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, , , , , , , , , , , , en , A PET-based nomogram for oropharyngeal cancers (2017), in: European Journal of Cancer, 75(222-230)
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, , , , , , , en , 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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