Select a category
2022
Evaluation of AI vs. Clinical Experts SBRT-Thorax Computed Tomography OARs Delineation
Stathakis, S. & Pissakas, Georgios & Alexiou, A. & Bertrand, B. & Bondiau, P.Y. & Claude, Lekunze & Cuthbert, T. & Damatopoulou, A. & Dejean, C. & Doukakis, C. & Gungor, Gorkem & Hardy, L. & Maani, E. & Martel-Lafay, Isabelle & Mavroidis, P. & Paragios, N. & Peppa, Vasiliki & Remonde, D. & Shumway, J.W. & Ozyar, Enis. (2022). Evaluation of AI vs. Clinical Experts SBRT-Thorax Computed Tomography OARs Delineation. International Journal of Radiation Oncology*Biology*Physics. 114. e102-e103. 10.1016/j.ijrobp.2022.07.897.AI surpassing human expert: a multi-centric evaluation for organ at risk delineation
Azria, D. & Boldrini, Luca & de ridder, Mark & Fenoglietto, Pascal & Gambacorta, Maria & Gevaert, Thierry & Gungor, Gorkem & Lagerwaard, F.J. & Marciscano, Ariel & Michalet, M. & Nagar, Himanshu & Pennell, R. & Serbez, I. & Vanspeybroeck, B. & Zoto, Teuta & Cafaro, Alexandre & Hardy, L. & Kandiban, S. & Oumani, A. & Ozyar, Enis. (2022). OC-0463 AI surpassing human expert: a multi-centric evaluation for organ at risk delineation. Radiotherapy and Oncology. 170. S408-S410. 10.1016/S0167-8140(22)02599-3.AI-based OAR annotation for pediatric brain radiotherapy planning
Bondiau, P. & Bolle, S. & Escande, Alexandre & Duverge, L. & Demoor, C. & Rouyar-Nicolas, A. & Bertrand, B. & Cannard, A. & Hardy, L. & Martineau-Huynh, C. & Paragios, N. & Roque, T. & Deutsch, E. & Robert, Charline. (2022). PD-0330 AI-based OAR annotation for pediatric brain radiotherapy planning. Radiotherapy and Oncology. 170. S293-S295. 10.1016/S0167-8140(22)02823-7.Statistical discrepancies in GTV delineation for H&N cancer across expert centers
Leroy, A. & Paragios, Nikos & Deutsch, E. & Grégoire, V. & Mitrea, D. & Pêtre, A. & Sun, Roger & Tao, Y.G.. (2022). MO-0476 Statistical discrepancies in GTV delineation for H&N cancer across expert centers. Radiotherapy and Oncology. 170. S426-S427. 10.1016/S0167-8140(22)02370-2.2021
To plan and deliver adjuvant breast radiotherapy over 1 week: 1-week breast workflow implementation
Louvel, G. & Milewski, C. & Auzac, Guillaume & Villaret, F. & Ung, M. & Berthelot, K. & Folino, E. & Ezra, P. & Roberti, E. & Yessoufou, I. & Cheve, M. & Fournier-Bidoz, N. & Paragios, Nikos & Deutsch, E. & Rivera, S.. (2021). PO-1099 To plan and deliver adjuvant breast radiotherapy over 1 week: 1-week breast workflow implementation. Radiotherapy and Oncology. 161. S914-S915. 10.1016/S0167-8140(21)07550-2.Improvement of a deep learning based automatic delineation model using anatomical criteria
Brion, T. & Karamouza, E. & Vitry, L. & Lombard, A. & Roque, T. & Paragios, Nikos & Auzac, Guillaume & Lamrani-Ghaouti, A. & Bonnet, N. & Limkin, Elaine & Ung, M. & Bockel, S. & Pasquier, D. & Wong, S. & trialists, H. & Achkar, S. & Rivera, S.. (2021). PD-0731 Improvement of a deep learning based automatic delineation model using anatomical criteria. Radiotherapy and Oncology. 161. S561-S563. 10.1016/S0167-8140(21)07010-9.Quality Assurance and Clinical Acceptability for AI-driven Automatic Contouring of Organs at Risk
Dissler, N. & Stathakis, S. & Lombard, A. & Paragios, Nikos & Klausner, Guillaume & Lahmi, Lucien & III, W. & Maani, E.. (2021). OC-0504 Quality Assurance and Clinical Acceptability for AI-driven Automatic Contouring of Organs at Risk. Radiotherapy and Oncology. 161. S386-S387. 10.1016/S0167-8140(21)06930-9.2020
Deep learning auto contouring of OAR for HN radiotherapy: a blinded evaluation by clinical experts
Grégoire, V. & Blanchard, Pierre & Allajbej, A. & Petit, Claire & Milhade, N. & Nguyen, F. & Bakkar, S. & Boulle, G. & Romano, E. & Zrafi, W.s & Lombard, A. & Ullmann, E. & Paragios, Nikos & Deutsch, E. & Robert, Charline. (2020). OC-0681: Deep learning auto contouring of OAR for HN radiotherapy: a blinded evaluation by clinical experts. Radiotherapy and Oncology. 152. S379-S380. 10.1016/S0167-8140(21)00703-9.Improving Radiotherapy Workflow Through Implementation of Delineation Guidelines & AI-Based Annotation
Ung, M. & Rouyar-Nicolas, A. & Limkin, Elaine & Petit, Claire & Sarrade, T. & Carre, Alexandre & Auzac, Guillaume & Lombard, A. & Ullman, E. & Bonnet, N. & Assia, L.G. & Paragios, Nikos & Huynh, C. & Deutsch, E. & Rivera, S. & Robert, Charline. (2020). Improving Radiotherapy Workflow Through Implementation of Delineation Guidelines & AI-Based Annotation. International Journal of Radiation Oncology*Biology*Physics. 108. e315. 10.1016/j.ijrobp.2020.07.753.Dosimetric impact of an AI-based delineation software satisfying international guidelines in breast cancer radiotherapy
Ung, M. & Rivera, S. & Rouyar, A. & Limkin, Elaine & Petit, C. & Sarrade, T. & Carre, Alexandre & Auzac, Guillaume & Lombard, A. & Ullmann, E. & Bonnet, N. & Lamrani-Ghaouti, A. & Paragios, Nikos & Martineau-Huynh, C. & Deutsch, E. & Robert, Charline. (2020). Dosimetric impact of an AI-based delineation software satisfying international guidelines in breast cancer radiotherapy. European Journal of Cancer. 138. S113. 10.1016/S0959-8049(20)30840-6.Full-body delineation of ROIs through anatomy-preserving deep learning ensemble networks
Lombard, A. & D’Avaucourt, L. & Ullmann, E. & Marini-Silva, R. & Bus, N. & Paragios, Nikos. (2020). PO-1753: Full-body delineation of ROIs through anatomy-preserving deep learning ensemble networks. Radiotherapy and Oncology. 152. S974-S975. 10.1016/S0167-8140(21)01771-0.AI-driven quality insurance for delineation in radiotherapy breast clinical trials
Rivera, S. & Lombard, A. & Pasquier, D. & Wong, S. & Limkin, Elaine & Auzac, Guillaume & Blanchecotte, J. & Chand-Fouché, M.E. & Lamrani-Ghaouti, A. & Bonnet, N. & Paragios, Nikos & Martineau-Huynh, C. & Ullmann, E. & Ruffier, A. & Deutsch, E.. (2020). PO-1722: AI-driven quality insurance for delineation in radiotherapy breast clinical trials. Radiotherapy and Oncology. 152. S953. 10.1016/S0167-8140(21)01740-0.2022
AI-driven combined deformable registration and image synthesis between radiology and histopathology
Leroy, A. & Lerousseau, Marvin & Henry, Théophraste & Estienne, Théo & Classe, M. & Paragios, N. & Deutsch, E. & Grégoire, V.. (2022). PO-1613 AI-driven combined deformable registration and image synthesis between radiology and histopathology. Radiotherapy and Oncology. 170. S1400-S1401. 10.1016/S0167-8140(22)03577-0.2022
Dosimetric evaluation of AI-based synthetic CTs for MRI-only brain radiotherapy
Veres, Cristina & Shrestha, K. & Roque, T. & Alvarez-Andres, E. & Gasnier, A. & Dhermain, Frédéric & Paragios, N. & Deutsch, E. & Robert, Charline. (2022). PO-1661 Dosimetric evaluation of AI-based synthetic CTs for MRI-only brain radiotherapy. Radiotherapy and Oncology. 170. S1459-S1460. 10.1016/S0167-8140(22)03625-8.Characterisation of synthetic CTs clinical quality: which gamma indices to evaluate in practice?
Andres, E. & Gasnier, A. & Veres, Cristina & Dhermain, Frédéric & Corbin, S. & Auville, F. & Biron, B. & Vatonne, A. & Henry, Théophraste & Estienne, Théo & Lerousseau, Marvin & Carre, Alexandre & Fidon, Lucas & Deutsch, E. & Paragios, N. & Robert, Charline. (2022). PO-1623 Characterisation of synthetic CTs clinical quality: which gamma indices to evaluate in practice?. Radiotherapy and Oncology. 170. S1413-S1415. 10.1016/S0167-8140(22)03587-3.Dosimetric evaluation of dose calculation uncertainties for MR-only treatments of pelvic MRgRT
Coric, I. & Shrestha, K. & Roque, T. & Paragios, N. & Zips, D. & Thorwarth, Daniela & Nachbar, Marcel. (2022). OC-0289 Dosimetric evaluation of dose calculation uncertainties for MR-only treatments of pelvic MRgRT. Radiotherapy and Oncology. 170. S250-S251. 10.1016/S0167-8140(22)02547-6.Clinical evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic MR
Fenoglietto, Pascal & Gevaert, Thierry & Boussaer, M. & Delasalles, E. & Ioannidou, D. & Shreshtha, K. & Roque, T. & Paragios, N. & Azria, D. & Ozyar, Enis & Gungor, Gorkem. (2022). MO-0648 Clinical evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic MR. Radiotherapy and Oncology. 170. S583-S584. 10.1016/S0167-8140(22)02406-9.AI-based OAR delineation in brain T1w-MRI: Overcoming Inter- and Intra-observer variability
Gungor, Gorkem & Klausner, Guillaume & Gur, Giyora & Serbez, I. & Temur, B. & Cafaro, Alexandre & Hardy, L. & Kandiban, S. & Oumani, A. & Bertrand, B. & Shreshtha, K. & Roque, T. & Atalar, Banu & Paragios, N. & Ozyar, Enis. (2022). PO-1890 AI-based OAR delineation in brain T1w-MRI: Overcoming Inter- and Intra-observer variability. Radiotherapy and Oncology. 170. S1674-S1675. 10.1016/S0167-8140(22)03853-1.Clinical evaluation of organs at risk automatic-segmentation for T2-weigthed MRI
Newman, N. & Stathakis, S. & Thorwarth, Daniela & Zips, D. & Nachbar, Marcel & Kandiban, S. & Oumani, A. & Shreshtha, K. & Roque, T. & Paragios, N. & Jones, W.E.. (2022). PD-0332 Clinical evaluation of organs at risk automatic-segmentation for T2-weigthed MRI. Radiotherapy and Oncology. 170. S296-S297. 10.1016/S0167-8140(22)02825-0.2021
Human-Level Precision Upper Abdominal OAR Contouring With Anatomically Preserving Deep Learning During Magnetic Resonance Imaging Guided Adaptive Radiotherapy (MRgRT)
Gungor, Gorkem & Michalet, M. & Lombard, A. & Roque, T. & Atalar, B. & Temur, B. & Serbez, I. & Azria, D. & Vitry, L. & Riou, Olivier & Paragios, N. & Ozyar, Enis & Fenoglietto, Pascal. (2021). Human-Level Precision Upper Abdominal OAR Contouring With Anatomically Preserving Deep Learning During Magnetic Resonance Imaging Guided Adaptive Radiotherapy (MRgRT). International Journal of Radiation Oncology*Biology*Physics. 111. S44-S45. 10.1016/j.ijrobp.2021.07.122.Synthetic-CT generation from T1w brain MRIs with a cascaded GANs ensemble approach
Lombard, A. & Shreshtha, K. & Robert, Charline & Roque, T. & Fauchon, Francois & Noël, Ge & Paragios, Nikos & Deutsch, E.. (2021). PO-1680 Synthetic-CT generation from T1w brain MRIs with a cascaded GANs ensemble approach. Radiotherapy and Oncology. 161. S1405-S1406. 10.1016/S0167-8140(21)08131-7.Automatic synthetic-CT generation from unpaired T2w pelvis MRIs using ensembled self-supervised GANs
Lombard, A. & Shreshtha, K. & Nachbach, M. & Roque, T. & Thorwarth, Daniela & Paragios, Nikos. (2021). PD-0754 Automatic synthetic-CT generation from unpaired T2w pelvis MRIs using ensembled self-supervised GANs. Radiotherapy and Oncology. 161. S585-S586. 10.1016/S0167-8140(21)07033-X.Development and quantitative evaluation of AI-based pelvic MRI autocontouring for adaptive MRgRT
Nachbar, M. & Lo Russo, M. & Boeke, S. & Wegener, D. & Boldt, J. & Butzer, S. & Roque, T. & Lombard, A. & De Vitry, L. & Paragios, Nikos. & Zips, D. & Thorwarth, D. (2021). OC-0085 Development and quantitative evaluation of AI-based pelvic MRI autocontouring for adaptive MRgRT. Radiotherapy and Oncology. 161. S58-S59. 10.1016/S0167-8140(21)06779-7.Synthetic CT from MRI with deep learning: Assessing the clinical impact of generated errors
Andres, E. & Gasnier, A. & Veres, Cristina & Dhermain, Frédéric & Corbin, S. & Auville, F. & Biron, B. & Vatonne, A. & Henry, Théophraste & Estienne, Théo & Lerousseau, Marvin & Fidon, Lucas & Deutsch, E. & Paragios, Nikos & Robert, Charline. (2021). PH-0652 Synthetic CT from MRI with deep learning: Assessing the clinical impact of generated errors. Radiotherapy and Oncology. 161. S520-S522. 10.1016/S0167-8140(21)07384-9.2020
Optimizing the generation of brain pseudo-CT from MRI based on a highly efficient 3D neural network
Andres, E. & Fidon, Lucas & Vakalopoulou, M. & Lerousseau, Marvin & Carre, Alexandre & Sun, Roger & Beaudre, A. & Deutsch, E. & Paragios, Nikos & Robert, Charline. (2020). PO-1702: Optimizing the generation of brain pseudo-CT from MRI based on a highly efficient 3D neural network. Radiotherapy and Oncology. 152. S938-S939. 10.1016/S0167-8140(21)01720-5.Assessment of the generalizability to pediatric protontherapy of a 3D network generating pseudo-CT
Andres, E. & Causse, Maelie & Fidon, Lucas & Ermeneux, Louis & Bolle, S. & Martin, V. & Paragios, Nikos & Deutsch, E. & De marzi, Ludovic & Robert, Charline. (2020). PH-0408: Assessment of the generalizability to pediatric protontherapy of a 3D network generating pseudo-CT. Radiotherapy and Oncology. 152. S219-S220. 10.1016/S0167-8140(21)00430-8.Training and validation of an AI-based MRI auto-contouring method for pelvic organs
Boeke, S & la Russo, M & Nachbar, M & Winter, J & Lombard, A & Bus, N & Paragios, N & Gani, C & Müller, A.C & Zips, D & Thorwarth, D (2020). VS09-5-jD: Training and validation of an AI-based MRI auto-contouring method for pelvic organs. Strahlenther Onkol. 196 (suppl 1), 1-230 (2020). 10.1007/s00066-020-01620-0.2019
Pseudo Computed Tomography generation using 3D deep learning – Application to brain radiotherapy
Andres, E. & Fidon, Lucas & Vakalopoulou, M. & Noël, Ge & Niyoteka, S. & Benzazon, Nathan & Deutsch, E. & Paragios, Nikos & Robert, Charlotte. (2019). PO-1002 Pseudo Computed Tomography generation using 3D deep learning – Application to brain radiotherapy. Radiotherapy and Oncology. 133. S553. 10.1016/S0167-8140(19)31422-7.Coming soon
2021
Fast Monte-Carlo dose simulation with recurrent deep learning
Martinot, S. & Bus, N. & Vakalopoulou, M. & Robert, Charline & Deutsch, E. & Paragios, Nikos. (2021). OC-0308 Fast Monte-Carlo dose simulation with recurrent deep learning. Radiotherapy and Oncology. 161. S216-S217. 10.1016/S0167-8140(21)06855-9.DeepDoseOpt: End-to-End VMAT Pelvis Dose Prediction & Treatment Planning Inference
Dedieu, J. & Shreshtha, K. & Lombard, A. & Bus, N. & Martinot, S. & Fick, R. & Paragios, Nikos. (2021). PD-0820 DeepDoseOpt: End-to-End VMAT Pelvis Dose Prediction & Treatment Planning Inference. Radiotherapy and Oncology. 161. S652-S653. 10.1016/S0167-8140(21)07099-7.Weakly supervised 3D ConvLSTMs for Monte-Carlo radiotherapy dose simulations
Martinot, S. & Bus, N. & Vakalopoulou, M. & Robert, C. & Deutsch, E. & Paragios, N. (2021). Weakly supervised 3D ConvLSTMs for Monte-Carlo radiotherapy dose simulations. Medical Imaging with Deep Learning.2020
SIMSEB: Unlocking the Dosimetric Potential of Sequential Boost Plans in VMAT Through Simultaneous Optimization
Fick, R.H.J. & Boule, T. & Pouille, A. & Lombard, A. & Bus, N. & Paragios, Nikos. (2020). SIMSEB: Unlocking the Dosimetric Potential of Sequential Boost Plans in VMAT Through Simultaneous Optimization. International Journal of Radiation Oncology*Biology*Physics. 108. e381. 10.1016/j.ijrobp.2020.07.2403.2021
Caba, B. & Lui, D. & Lombard, A. & Novikov, N. & Cafaro, A. & Bradley, D. & Battistella, E. & Fisher, E. & Franchimont, N. & Gafson, A. & Momayyez-Siahkal, P. & Karim-Aghaloo, Z. & Arnold, D. & Elliott, C. & Paragios, N. & Belachew, S. (2021) Machine learning-based classification of acute versus chronic multiple sclerosis lesions using radiomic features from unenhanced cross-sectional brain MRI. Neurology. 96 (15 Supplement) 4121. 10.1007/s00066-020-01620-0.
2021
Leroy, A. & Shreshtha, K. & Lerousseau, Marvin & Henry, Théophraste & Estienne, Théo & Classe, M. & Paragios, Nikos & Deutsch, E. & Grégoire, V.. (2021). OC-0522 Cell-Rad: Towards Histology-driven Radiation Oncology from Multi-Parametric MRI. Radiotherapy and Oncology. 161. S407-S408. 10.1016/S0167-8140(21)06948-6.