Hasil Pencarian - Dimitris Visvikis

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  1. 1
    Comparison and Fusion of Machine Learning Algorithms for Prospective Validation of PET/CT Radiomic Features Prognostic Value in Stage II-III Non-Small Cell Lung Cancer

    Comparison and Fusion of Machine Learning Algorithms for Prospective Validation of PET/CT Radiomic Features Prognostic Value in Stage II-III Non-Small Cell Lung Cancer oleh Shima Sepehri, Olena Tankyevych, Taman Upadhaya, Dimitris Visvikis, Mathieu Hatt, Catherine Cheze Le Rest

    Diterbitkan 2021-04-01
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  2. 2
    A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy

    A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy oleh Nicolas Boussion, Ulrike Schick, Gurvan Dissaux, Luc Ollivier, Gaëlle Goasduff, Olivier Pradier, Antoine Valeri, Dimitris Visvikis

    Diterbitkan 2021-10-01
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  3. 3
    Correlation of intra-tumor 18F-FDG uptake heterogeneity indices with perfusion CT derived parameters in colorectal cancer.

    Correlation of intra-tumor 18F-FDG uptake heterogeneity indices with perfusion CT derived parameters in colorectal cancer. oleh Florent Tixier, Ashley M Groves, Vicky Goh, Mathieu Hatt, Pierre Ingrand, Catherine Cheze Le Rest, Dimitris Visvikis

    Diterbitkan 2014-01-01
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  4. 4
    Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion

    Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion oleh Ronrick Da-ano, Gustavo Andrade-Miranda, Olena Tankyevych, Dimitris Visvikis, Pierre-Henri Conze, Catherine Cheze Le Rest

    Diterbitkan 2024-07-01
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  5. 5
    Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images

    Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images oleh Rihab Hami, Sena Apeke, Pascal Redou, Laurent Gaubert, Ludwig J. Dubois, Philippe Lambin, Dimitris Visvikis, Nicolas Boussion

    Diterbitkan 2023-06-01
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  6. 6
    EANM guidelines for PET-CT and PET-MR routine quality control

    EANM guidelines for PET-CT and PET-MR routine quality control oleh Michel Koole, Ian Armstrong, Aron K. Krizsan, Anne Stromvall, Dimitris Visvikis, Bernhard Sattler, Stephan G. Nekolla, John Dickson

    Diterbitkan 2023-02-01
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  7. 7
    Accurate Tumor Delineation vs. Rough Volume of Interest Analysis for 18F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer

    Accurate Tumor Delineation vs. Rough Volume of Interest Analysis for 18F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer oleh Shima Sepehri, Olena Tankyevych, Olena Tankyevych, Andrei Iantsen, Dimitris Visvikis, Mathieu Hatt, Catherine Cheze Le Rest, Catherine Cheze Le Rest

    Diterbitkan 2021-10-01
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  8. 8
    A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.

    A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets. oleh Ronrick Da-Ano, François Lucia, Ingrid Masson, Ronan Abgral, Joanne Alfieri, Caroline Rousseau, Augustin Mervoyer, Caroline Reinhold, Olivier Pradier, Ulrike Schick, Dimitris Visvikis, Mathieu Hatt

    Diterbitkan 2021-01-01
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  9. 9
    MRI-Derived Radiomics to Guide Post-operative Management for High-Risk Prostate Cancer

    MRI-Derived Radiomics to Guide Post-operative Management for High-Risk Prostate Cancer oleh Vincent Bourbonne, Vincent Bourbonne, Vincent Bourbonne, Martin Vallières, Martin Vallières, François Lucia, François Lucia, François Lucia, Laurent Doucet, Dimitris Visvikis, Valentin Tissot, Olivier Pradier, Olivier Pradier, Olivier Pradier, Mathieu Hatt, Ulrike Schick, Ulrike Schick, Ulrike Schick

    Diterbitkan 2019-08-01
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  10. 10
    Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence

    Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence oleh Thomas Louis, François Lucia, François Cousin, Carole Mievis, Nicolas Jansen, Bernard Duysinx, Romain Le Pennec, Dimitris Visvikis, Malik Nebbache, Martin Rehn, Mohamed Hamya, Margaux Geier, Pierre-Yves Salaun, Ulrike Schick, Mathieu Hatt, Philippe Coucke, Pierre Lovinfosse, Roland Hustinx

    Diterbitkan 2024-04-01
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