Resources
Resource Library
Explore our resource library and results from clinical users worldwide offering best practices and information to support your efforts to reinvent cancer care through AI.
Unlocking the Potential of AI Auto-Contouring in Radiotherapy at HELSE Bergen
In this insightful session from the Human Bytes Academy, Lukas Hirschi from Haukeland University Hospital HELSE Bergen shared his extensive experience with AI-powered auto-contouring tool.
Clinically Relevant AI in Radiation Therapy
Presented by the clinical team from the University of Pennsylvania, Department of Radiation Oncology
Clinically-Dependent Fully Automatic Treatment Planning System via Reinforcement Learning
Dubois, P.R.F. & Fenoglietto, Pascal & Cournède, P.H. & Paragyos, N.. (2024). Clinically-Dependent Fully Automatic Treatment Planning System via Reinforcement Learning. International Journal of Radiation Oncology*Biology*Physics. 120. e122. 10.1016/j.ijrobp.2024.07.2051.
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.
Are current margins in locally advanced cervical cancers treated by tomotherapy appropriate?
Niyoteka, S. & Achkar, S. & Coric, I. & Bourdais, R. & Manea, E. & Dumas, I. & Marini-Silva, R. & Ullmann, E. & Carre, Alexandre & Paragios, Nikos & Deutsch, E. & Chargari, Cyrus & Robert, Charline. (2020). PO-1667: Are current margins in locally advanced cervical cancers treated by tomotherapy appropriate?. Radiotherapy and Oncology. 152. S915-S916. 10.1016/S0167-8140(21)01685-6.
ART-Plan Segmentation Structures
See what is supported by ART-Plan for auto contouring of 270+ structures
ART-Plan Adaptive Flyer
See the full capabilities of ART-Plan for Continuous, Automated Replan Assessment
ART-Plan Overview Flyer
See why ART-Plan is the perfect AI companion to your TPS
ART-Plan "What's New" Flyer
See what's new in the latest release of ART-Plan
Offline Adaptive Radiotherapy is Emerging in Radiation Oncology Offering the Prospect of Enhancing Cancer Treatment Precision
This approach involves adjusting treatment plans based on analyzing imaging data collected prior to each session, allowing for treatment adaptations to changes in tumor size, location and overall patient anatomy.
Experience AI-Powered Offline Adaptive Radiotherapy Planning with ART-Plan
Adaptive radiotherapy (ART),aims to correct for anatomical variations between the treatment fractions, is becoming more and more established. This approach allows for a more precise and personalized delivery of radiation, and has the potential to improve outcomes for patients.
Adaptive Radiotherapy for Head & Neck Cancer
Centre D'Oncologie Pays-Basque, Aurelien Blouet, MD
Adaptive Radiotherapy for Post-Mastectomy Breast Cancer
Centre D'Oncologie Pays-Basque, Angelique Ductiel, MD
Adaptive Radiotherapyfor Prostate Cancer
Centre D'Oncologie Pays-Basque, Caroline Genebes, MD
Adaptive Radiotherapy for Breast Cancer
Centre D'Oncologie Pays-Basque, Lena Albert Dufrois, MD
AI-Driven Replanning at Scale
Catalan Oncology Center Perpignan, Vincent Plagnol, Ph.D
AI in radiotherapy for H&N cancer
H&N cancer awareness month
TheraPanacea & The PRE-ACT Consortium collaboration: Radiotherapy Breast Cancer side effects
The prediction of radiotherapy side effects using explainable AI
Multiple Sclerosis: Classification of acute versus chronic MS lesions using machine learning
Moving forward with Multiple Sclerosis diagnosis
Meet Nikos Paragios
Discover how TheraPanacea is using AI to healthcare through the eyes of our CEO!
MR-guided radiotherapy: a new hope for pancreatic cancer?
Pancreatic cancer is the 12th most common cancer in the world, representing 3% of all cancers [1]. There were more than 495,000 new cases of pancreatic cancer in 2020. Pancreatic cancer has the highest mortality rate of all major cancers. The 5-year relative survival rate is very low at just 5 to 10 percent, which makes pancreatic cancer one of the cancers with the lowest survival rate.
AI and SBRT: strong allies for effective treatment for lung cancer
Lung cancer is the second most common cancer worldwide. It is the most common cancer in men and the second most common cancer in women. There were more than 2.2 million new cases of lung cancer in 2021.
TheraPanacea: Finalist for INPI’s Research Award
We’re proud to announce that TheraPanacea has been selected as a finalist for the INPI Research Partnership Award. A recognition that highlights our strong commitment to scientific excellence, collaboration, and innovation in healthcare.
Cosmetic assessment in the UNICANCER HypoG-01 trial: a deep learning approach
Alexandre Cafaro; Amandine Ruffier; Gabriele Bielinyte; Y. Kirova; S. Racadot; M. Benchalal; JB. Clavier; C. Charra-Brunaud; ME. Chand-Fouche; D. Argo-Leignel; K. Peignaux; A. Benyoucef; D. Pasquier; P. Guilbert; J. Blanchecotte; A. Tallet; A. Petit; G. Bernadou; X. Zasadny; C. Lemanski; J. Fourquet; E. Malaurie; H. Kouto; C. Massabeau; A. Henni; Regnault; A. Belliere; Y. Belkacemi; M. Le Blanc-Onfroy; J. Geffrelot; JB. Prevost; E. Karamouza; Stefan Michiels, Marie Bergeaud, Assia Lamrani-Ghaouti, Sami Rhomdani, Alexis Bombezin–Domino, Nikos Paragios, Sofia Rivera. 2023. Cosmetic assessment in the UNICANCER HypoG-01 trial: a deep learning approach. SABCS
Region-Guided CycleGANs for Stain Transfer in Whole Slide Images
Boyd, Joseph & Villa, Irène & Mathieu, Marie-Christine & Deutsch, Eric & Paragios, Nikos & Vakalopoulou, Maria & Christodoulidis, Stergios. (2022). Region-Guided CycleGANs for Stain Transfer in Whole Slide Images. 10.1007/978-3-031-16434-7_35.
COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures
Battistella, Enzo & Vakalopoulou, Maria & Sun, Roger & Estienne, Théo & Lerousseau, Marvin & Nikolaev, Sergey & Andres, Emilie & Carre, Alexandre & Niyoteka, Stephane & Robert, Charlotte & Paragios, Nikos & Deutsch, Eric. (2021). COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures. IEEE/ACM Transactions on Computational Biology and Bioinformatics. PP. 1-1. 10.1109/TCBB.2021.3123910.
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology
Boyd, Joseph & Liashuha, Mykola & Deutsch, Eric & Paragios, Nikos & Christodoulidis, Stergios & Vakalopoulou, Maria. (2021). Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology.
Multimodal Brain Tumor Classification
Lerousseau, Marvin & Deutsch, Eric & Paragios, Nikos. (2021). Multimodal Brain Tumor Classification. 10.1007/978-3-030-72087-2_42.
Magnetic Resonance Imaging Virtual Histopathology from Weakly Paired Data
Leroy, A. & Shreshtha, K. & Lerousseau, M. & Henry, T. & Estienne, T. & Classe, M. & Paragios, N. & Grégoire, V & Deutsch, E.. (2021). Magnetic Resonance Imaging Virtual Histopathology from Weakly Paired Data. Proceedings of the MICCAI Workshop on Computational Pathology . 156:140-150
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment
Laousy, Othmane & Chassagnon, Guillaume & Oyallon, Edouard & Paragios, Nikos & Revel, Marie-Pierre & Vakalopoulou, Maria. (2021). Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment. 10.1007/978-3-030-87589-3_33.
Deep Multi-Instance Learning Using Multi-Modal Data for Diagnosis of Lymphocytosis
Sahasrabudhe, Mihir & Sujobert, Pierre & Maurin, Eugénie & Grange, Beatrice & Jallades, Laurent & Paragios, Nikos & Vakalopoulou, Maria. (2020). Deep Multi-Instance Learning Using Multi-Modal Data for Diagnosis of Lymphocytosis. IEEE Journal of Biomedical and Health Informatics. PP. 1-1. 10.1109/JBHI.2020.3038889.
Deep learning for lung disease segmentation on CT: Which reconstruction kernel should be used?
TN, Hoang & Vakalopoulou, Maria & Christodoulidis, Stergios & Paragios, Nikos & Revel, Marie-Pierre & Chassagnon, Guillaume. (2021). Deep learning for lung disease segmentation on CT: Which reconstruction kernel should be used?. Diagnostic and interventional imaging. 102. 10.1016/j.diii.2021.10.001.
Deep Learning for Image Matching and Co‐registration
Vakalopoulou, Maria & Christodoulidis, Stergios & Sahasrabudhe, Mihir & Paragios, Nikos. (2021). Deep Learning for Image Matching and Co‐registration. 10.1002/9781119646181.ch9.
Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge Solution
Henry, Théophraste & Carre, Alexandre & Lerousseau, Marvin & Estienne, Théo & Robert, Charlotte & Paragios, Nikos & Deutsch, Eric. (2021). Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge Solution. 10.1007/978-3-030-72084-1_30.
AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia
Chassagnon, Guillaume & Vakalopoulou, Maria & Battistella, Enzo & Christodoulidis, Stergios & TN, Hoang & Dangeard, Severine & Deutsch, Eric & Andre, Fabrice & Guillo, Enora & Halm, Nara & Hajj, Stefany & Bompard, Florian & Neveu, Sophie & Hani, Chahinez & Saab, Ines & Campredon, Aliénor & Koulakian, Hasmik & Bennani, Souhail & Freche, Gael & Paragios, Nikos. (2020). AI-Driven quantification, staging and outcome prediction of COVID-19 pneumonia. Medical Image Analysis. 10.1016/j.media.2020.101860.
Weakly Supervised Multiple Instance Learning Histopathological Tumor Segmentation
Lerousseau, Marvin & Vakalopoulou, Maria & Classe, Marion & Adam, Julien & Battistella, Enzo & Carre, Alexandre & Estienne, Théo & Henry, Théophraste & Deutsch, Eric & Paragios, Nikos. (2020). Weakly Supervised Multiple Instance Learning Histopathological Tumor Segmentation. 10.1007/978-3-030-59722-1_45.
Self-supervised Nuclei Segmentation in Histopathological Images Using Attention
Sahasrabudhe, Mihir & Christodoulidis, Stergios & Salgado, Roberto & Michiels, Stefan & Loi, Sherene & Andre, Fabrice & Paragios, Nikos & Vakalopoulou, Maria. (2020). Self-supervised Nuclei Segmentation in Histopathological Images Using Attention. 10.1007/978-3-030-59722-1_38.
Deep Learning–based Approach for Automated Assessment of Interstitial Lung Disease in Systemic Sclerosis on CT Images
Chassagnon, Guillaume & Vakalopoulou, Maria & Regent, Alexis & Aviram, Galit & Martin, Charlotte & Marini, Rafael & Bus, Norbert & Jerjir, Naïm & Arsene, Mekinian & Hua-Huy, Thong & Monnier-Cholley, Laurence & Benmostefa, Nouria & Mouthon, Luc & Dinh-Xuan, Anh Tuan & Paragios, Nikos & Revel, Marie-Pierre. (2020). Deep Learning–based Approach for Automated Assessment of Interstitial Lung Disease in Systemic Sclerosis on CT Images. Radiology: Artificial Intelligence. 2. e190006. 10.1148/ryai.2020190006.
Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation
Estienne, Théo & Lerousseau, Marvin & Vakalopoulou, Maria & Andres, Emilie & Battistella, Enzo & Carre, Alexandre & Chandra, Siddhartha & Christodoulidis, Stergios & Sahasrabudhe, Mihir & Sun, Roger & Robert, Charlotte & Talbot, Hugues & Paragios, Nikos & Deutsch, Eric. (2020). Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation. Frontiers in Computational Neuroscience. 14. 10.3389/fncom.2020.00017.
Use of Elastic Registration in Pulmonary MRI for the Assessment of Pulmonary Fibrosis in Patients with Systemic Sclerosis
Chassagnon, Guillaume & Martin, Charlotte & Marini, Rafael & Vakalopolou, Maria & Regent, Alexis & Mouthon, Luc & Paragios, Nikos & Revel, Marie-Pierre. (2019). Use of Elastic Registration in Pulmonary MRI for the Assessment of Pulmonary Fibrosis in Patients with Systemic Sclerosis. Radiology. 291. 182099. 10.1148/radiol.2019182099.
U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets
Estienne, Théo & Vakalopoulou, Maria & Christodoulidis, Stergios & Battistella, Enzo & Lerousseau, Marvin & Carre, Alexandre & Klausner, Guillaume & Sun, Roger & Robert, Charlotte & Mougiakakou, Stavroula & Paragios, Nikos & Deutsch, Eric. (2019). U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets. 10.1007/978-3-030-32248-9_35.
Tighter continuous relaxations for MAP inference in discrete MRFs: A survey
Kannan, Hariprasad & Komodakis, Nikos & Paragios, Nikos. (2019). Tighter continuous relaxations for MAP inference in discrete MRFs: A survey. 10.1016/bs.hna.2019.06.001.
Gene Expression High-Dimensional Clustering Towards a Novel, Robust, Clinically Relevant and Highly Compact Cancer Signature
Battistella, Enzo & Vakalopoulou, Maria & Estienne, Théo & Lerousseau, Marvin & Sun, Roger & Robert, Charlotte & Paragios, Nikos & Deutsch, Eric. (2019). Gene Expression High-Dimensional Clustering Towards a Novel, Robust, Clinically Relevant and Highly Compact Cancer Signature. 10.1007/978-3-030-17938-0_41.
Context Aware 3D CNNs for Brain Tumor Segmentation
Chandra, S. & Vakalopoulou, M. & Fidon, L. & Battistella, E. & Estienne, T. & Sun, R. & Robert, C. & Deutsch, E. & Paragios, N. (2019). Context Aware 3D CNNs for Brain Tumor Segmentation. Lecture Notes in Computer Science, 11384, 10.1007/978-3-030-11726-9_27
Radiomics analysis and metastatic patients: can we really continue to sidestep intra-patient tumor heterogeneity?
Henry, Théophraste & Sun, Roger & Lerousseau, Marvin & Estienne, Théo & Robert, Charlotte & Besse, Benjamin & Robert, Caroline & Paragios, Nikos & Deutsch, Eric. (2022). Radiomics analysis and metastatic patients: can we really continue to sidestep intra-patient tumor heterogeneity ?. 10.21203/rs.3.rs-1775619/v1.
Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies
Henry, Théophraste & Sun, Roger & Lerousseau, Marvin & Estienne, Théo & Robert, Charline & Besse, B. & Paragios, N. & Deutsch, E.. (2022). Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies. Scientific Reports. 12. 10.1038/s41598-022-20931-z.
Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives
Dercle, L. & Henry, T. & Carré, A. & Paragios, N. & Deutsch, E. & Robert, C. (2021). Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives. Methods, 188, 44-60, 10.1016/j.ymeth.2020.07.003
Radiomics to predict outcomes and abscopal response of patients with cancer treated with immunotherapy combined with radiotherapy using a validated signature of CD8 cells
Sun, Roger & Sundahl, Nora & Hecht, Markus & Putz, Florian & Lancia, Andrea & Rouyar, Angela & Milic, Marina & Carre, Alexandre & Battistella, Enzo & Andres, Emilie & Niyoteka, Stéphane & Romano, Edouard & Louvel, G. & Durand-Labrunie, Jérôme & Bockel, Sophie & Bahleda, Rastislav & Robert, Charlotte & Boutros, Celine & Vakalopoulou, Maria & Deutsch, Eric. (2020). Radiomics to predict outcomes and abscopal response of patients with cancer treated with immunotherapy combined with radiotherapy using a validated signature of CD8 cells. Journal for ImmunoTherapy of Cancer. 8. e001429. 10.1136/jitc-2020-001429.
Radiomics for selection of patients treated with immuno-radiotherapy: pooled analysis from 6 studies
Sun, Roger & Sundahl, Nora & Hecht, Markus & Putz, Florian & Lancia, Andrea & Milic, M. & Carre, Alexandre & Lerousseau, Marvin & Theo, E. & Battistella, Enzo & Andres, E. & Louvel, G. & Durand-Labrunie, Jérôme & Bockel, S. & Bahleda, R. & Robert, Charline & Boutros, Celine & Vakalopoulou, M. & Paragios, Nikos & Deutsch, E.. (2020). PD-0425: Radiomics for selection of patients treated with immuno-radiotherapy: pooled analysis from 6 studies. Radiotherapy and Oncology. 152. S231-S232. 10.1016/S0167-8140(21)00447-3.
Quantification of Cystic Fibrosis Lung Disease with Radiomics-based CT Scores
Chassagnon, Guillaume & Bommart, Sébastien & Burgel, Pierre-Régis & Chiron, Raphael & Dangeard, Severine & Paragios, Nikos & Martin, Clémence & Revel, Marie-Pierre. (2020). Quantification of Cystic Fibrosis Lung Disease with Radiomics-based CT Scores. Radiology: Cardiothoracic Imaging. 2. e200022. 10.1148/ryct.2020200022.
Radiomics to predict response to immunotherapy, bridging the gap from proof of concept to clinical applicability?
Deutsch, E & Paragios, N. (2019). Radiomics to predict response to immunotherapy, bridging the gap from proof of concept to clinical applicability?. Annals of oncology : official journal of the European Society for Medical Oncology. 30. 879-881. 10.1093/annonc/mdz150.
Evaluation of a radiomic signature of CD8 cells in patients treated with immunotherapy-radiotherapy in three clinical trials
Sun, Roger & Lancia, Andrea & Sundahl, Nora & Milic, M. & Carre, Alexandre & Lerousseau, Marvin & Estienne, Théo & Battistella, Enzo & Klausner, Guillaume & Bahleda, R. & Alvarez-Andres, E. & Robert, Charline & Boutros, Celine & Vakalopoulou, M. & Paragios, Nikos & Ost, Piet & Massard, C. & Deutsch, E.. (2019). Evaluation of a radiomic signature of CD8 cells in patients treated with immunotherapy-radiotherapy in three clinical trials. Annals of Oncology. 30. v43. 10.1093/annonc/mdz239.047.
Quantitative and qualitative evaluation of an automatic GTV contouring tool in pre-radiotherapy MRI in glioblastoma treatment
Satragno, C. & Bourbonne, V. & Clavier, J.B. & Green, W. & Theodoridis, T. & Balia, M. & Bockel, S. & Hachemi, T. & Veres, Cristina & Mrissa, Linda & Yuste, C. & Romdhani, S. & Iandolo, R. & Bombezin-Domino, A. & McBeth, R. & Teo, K. & Deutsch, E. & Paragyos, N. & Robert, C. & Maingon, P.. (2024). Quantitative and Qualitative Evaluation of an Automatic and Manual GTV Contouring Tool in Pre-Radiotherapy MRI in Glioblastoma Treatment. International Journal of Radiation Oncology*Biology*Physics. 120. e653. 10.1016/j.ijrobp.2024.07.1434.
Single-timepoint Low-dimensional Characterization and Classification of Acute versus Chronic Multiple Sclerosis Lesions using Machine Learning
Caba, Bastien & Cafaro, Alexandre & Lombard, Aurélien & Arnold, Douglas & Elliott, Colm & Liu, Dawei & Jiang, Xiaotong & Gafson, Arie & Fisher, Elizabeth & Belachew, Shibeshih & Paragios, Nikos. (2022). Single-timepoint Low-dimensional Characterization and Classification of Acute versus Chronic Multiple Sclerosis Lesions using Machine Learning. NeuroImage. 265. 119787. 10.1016/j.neuroimage.2022.119787.
Quantitative and Qualitative Evaluation of an Automated Planning Solution for Prostate Radiotherapy
Green, W. & McBeth, R. & Güngör, G. & Moll, M. & Cozzi, S. & Gregoire, V.G. & Ungun, B. & Costea, M. & Bus, N. & Paragyos, N. & Teo, K. & Fenoglietto, Pascal. (2024). Quantitative and Qualitative Evaluation of an Automated Planning Solution for Prostate Radiotherapy. International Journal of Radiation Oncology*Biology*Physics. 120. e627. 10.1016/j.ijrobp.2024.07.1379.
Dose Predictions for Head and Neck Cancers Using Hybrid Structure Sets Containing Manual and Automated Contours
Buatti, J.S. & Stathakis, S. & Kirby, N. & Li, R. & Oliveira, M. & Kabat, C. & Papanikolaou, N. & Paragios, N.. (2022). Dose Predictions for Head and Neck Cancers Using Hybrid Structure Sets Containing Manual and Automated Contours. International Journal of Radiation Oncology*Biology*Physics. 114. e95. 10.1016/j.ijrobp.2022.07.881.
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.
High-particle simulation of Monte-Carlo dose distribution with 3D ConvLSTMs
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.
End-to-end Treatment Planning Optimization through Dose/Anatomy-based Metric-learning kNN Embeddings
Vitry, L. & Fick, R. & Bus, N. & Dedieu, J. & Lombard, A. & Paragios, Nikos. (2021). PO-1839 End-to-end Treatment Planning Optimization through Dose/Anatomy-based Metric-learning kNN Embeddings. Radiotherapy and Oncology. 161. S1568-S1569. 10.1016/S0167-8140(21)08290-6.
Weakly supervised 3D ConvLSTMs for Monte-Carlo radiotherapy dose simulations
Martinot, Sonia & Bus, Norbert & Vakalopoulou, Maria & Robert, Charlotte & Deutsch, Eric & Paragios, Nikos. (2021). High-Particle Simulation of Monte-Carlo Dose Distribution with 3D ConvLSTMs. 10.1007/978-3-030-87202-1_48.
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.
Al powered decision making process for RT re-planning
Leclercq, Bastien & Romain-Vilboux, Blandine & Costea, Madalina-Liana & Colombo, Lorenzo & Romdhani, Sami & Bus, Norbert & Teboul, Olivier & Paragios, Nikos. (2024). 1419: AI powered decision making process for RT re-planning. Radiotherapy and Oncology. 194. S4037-S4039. 10.1016/S0167-8140(24)01819-X.
Fast Tracking MR only Adaptive Radiotherapy; Automatic Segmentation on Planning, Daily MRI and the synthetic CT
Amjad, Asma & Geoffrey, R. & Genz, D. & Horache, S. & Colombo, L. & Chen, Xigang & Paulson, E.S.. (2024). Fast Tracking MR Only Adaptive Radiotherapy; Automatic Segmentation on Planning, Daily MRI and Synthetic CT. International Journal of Radiation Oncology*Biology*Physics. 120. S158-S159. 10.1016/j.ijrobp.2024.07.2180. .
Evaluation of an Artificial Intelligence-Based Software for Adaptative Radiotherapy in Head and Neck
P. Maury, C. Berthold, P. Blanchard, T. V. F. Nguyen, R. Sun, Y. Tao, E. Deutsch, C. Robert,
and L. Calmels; International Journal of Radiation Oncology, Biology, Physics, Volume 120, Issue 2, e558 – e559; doi: 10.1016/j.ijrobp.2024.07.1237
Quantitative and qualitative evaluation of an automated solution for prostate radiotherapy
Costea, Madalina-Liana & Ungun, Baris & Vauclin, Rémi & Delasalles, Edouard & Mengin, Elie & Bus, Norbert & Gungor, Gorkem & Moll, Matthias & Cozzi, Salvatore & Gregoire, Vincent & Fenoglietto, Pascal & Paragios, Nikos. (2024). 1411: Quantitative and qualitative evaluation of an automated planning solution for prostate radiotherapy. Radiotherapy and Oncology. 194. S3565-S3567. 10.1016/S0167-8140(24)01813-9.
End-to-end automatic treatment planning for prostate radiotherapy
Vauclin, Rémi & Ungun, Baris & Delasalles, Edouard & Mengin, Elie & Bus, Norbert & Costea, Madalina-Liana & Gungor, Gorkem & Gassa, Frederic & Gregoire, Vincent & Maury, Pauline & Robert, Charlotte & Fenoglietto, Pascal & Paragios, Nikos. (2024). 1401: End-to-end automatic treatment planning for prostate radiotherapy. Radiotherapy and Oncology. 194. S3562-S3565. 10.1016/S0167-8140(24)01806-1.
Self-Supervised GAN Based Synthetic CT Generation From Head and Neck CBCT
Colombo, Lorenzo & Oumani, Ayoub & Schmidt-Mengin, Marius & Horache, Sofiane & Romdhani, Sami & Kandiban, Sanmady & Romain, Blandine & Temiz, Gizem & Teboul, Olivier & Paragios, Nikos & Fenoglietto, Pascal. (2024). 1399: Self-Supervised GAN Based Synthetic CT Generation From Head and Neck CBCT. Radiotherapy and Oncology. 194. S1288-S1290. 10.1016/S0167-8140(24)01804-8.
Self-Supervised GAN Based Synthetic CT Generation From Thorax CBCT
Colombo, Lorenzo & Oumani, Ayoub & Schmidt-Mengin, Marius & Horache, Sofiane & Romdhani, Sami & Kandiban, Sanmady & Romain, Blandine & Temiz, Gizem & Teboul, Olivier & Paragios, Nikos & Fenoglietto, Pascal. (2024). 1391: Self-Supervised GAN Based Synthetic-CT Generation From Thorax CBCT. Radiotherapy and Oncology. 194. S1659-S1661. 10.1016/S0167-8140(24)01797-3.
Dose Prediction for Prostate Radiotherapy Planning
Delasalles, Edouard & Vauclin, Rémi & Mengin, Elie & Ungun, Baris & Costea, Madalina-Liana & Bus, Norbert & Komodakis, Nikos & Fenoglietto, Pascal & Gungor, Gorkem & Paragios, Nikos. (2024). 1377: Dose Prediction for Prostate Radiotherapy Planning. Radiotherapy and Oncology. 194. S3560-S3562. 10.1016/S0167-8140(24)01785-7.
Self-supervised GAN based synthetic-CT generation from breast CBCT
Colombo, Lorenzo & Oumani, Ayoub & Schmidt-Mengin, Marius & Horache, Sofiane & Romdhani, Sami & Kandiban, Sanmady & Romain, Blandine & Temiz, Gizem & Teboul, Olivier & Paragios, Nikos & Fenoglietto, Pascal. (2024). 1375: Self-Supervised GAN Based Synthetic CT Generation From Breast CBCT. Radiotherapy and Oncology. 194. S545-S547. 10.1016/S0167-8140(24)01784-5.
Use of synthetic cone beam CT in head and neck image guided volumetric modulated radiation therapy
Chalkia, Marina & Psarras, Michalis & Romdhani, Sami & Patatoukas, George & Stroubinis, Theodoros & Stasinou, Despoina & Kollaros, Nikolaos & Protopapa, Maria & Paragios, Nikos & Kouloulias, Vassilis & Platoni, Kalliopi. (2024). 2734: Use of synthetic cone beam CT in head and neck image guided volumetric modulated radiation therapy. Radiotherapy and Oncology. 194. S1425-S1427. 10.1016/S0167-8140(24)02892-5.
A proof of concept for MR-only workflow in CyberKnife intracranial radiosurgery
Pantelis, Evaggelos & Moutsatsos, Argyris & Archontakis, Panagiotis & Romdhani, Sami & Stergioula, Anastasia & Papagiannis, Panagiotis & Paragios, Nikos. (2024). 2146: A proof of concept for MR-only workflow in CyberKnife intracranial radiosurgery. Radiotherapy and Oncology. 194. S4522-S4524. 10.1016/S0167-8140(24)02404-6.
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, Charlotte. (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.
Dosimetric Evaluation of Dose Calculation Uncertainties for MR-Only Approaches in Prostate MR-Guided Radiotherapy
Coric, Ivan & Shreshtha, Kumar & Roque, Thais & Paragios, Nikos & Gani, Cihan & Zips, Daniel & Thorwarth, Daniela & Nachbar, Marcel. (2022). Dosimetric Evaluation of Dose Calculation Uncertainties for MR-Only Approaches in Prostate MR-Guided Radiotherapy. Frontiers in Physics. 10. 897710. 10.3389/fphy.2022.897710
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, Charlotte. (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 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.
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.
A Multi-Centric Evaluation of AI-Driven Synthetic CT Generation Form Low Field Magnetic Resonance Imaging
Gungor, Gorkem & Azria, D. & Balermpas, Panagiotis & Boldrini, Luca & Chuong, Michael & de ridder, Mark & Gevaert, Thierry & Hardy, L. & Kandiban, S. & Maingon, P. & Mittauer, K.E. & Ozyar, Enis & Paragios, N. & Pennell, R. & Placidi, L. & Shreshtha, K. & Speiser, M.P. & Tanadini-Lang, Stephanie & Valdes, S. & Fenoglietto, Pascal. (2022). A Multi-Centric Evaluation of AI-Driven Synthetic CT Generation Form Low Field Magnetic Resonance Imaging. International Journal of Radiation Oncology*Biology*Physics. 114. S163. 10.1016/j.ijrobp.2022.07.655.
A Multi-Centric Evaluation of AI-Driven OARs Low Field MRgRT Pelvic /Abdomen Contouring
Azria, D. & Andratschke, Nicolaus & Balermpas, Panagiotis & Boldrini, Luca & Bourdais, R. & Bruynzeel, Anna & Chuong, Michael & de ridder, Mark & Fenoglietto, Pascal & Gevaert, Thierry & Gungor, Gorkem & Hardy, L. & Kandiban, S. & Lagerwaard, Frank & Maingon, P. & Marciscano, Ariel & Mittauer, K.E. & Nagar, Himanshu & Paragios, N. & Ozyar, Enis. (2022). A Multi-Centric Evaluation of AI-Driven OARs Low Field MRgRT Pelvic /Abdomen Contouring. International Journal of Radiation Oncology*Biology*Physics. 114. e103. 10.1016/j.ijrobp.2022.07.898.
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, Charlotte & 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, Charlotte. (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.
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, Charlotte. (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, Charlotte. (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.
Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning
Andres, Emilie & Fidon, Lucas & Vakalopoulou, Maria & Lerousseau, Marvin & Carre, Alexandre & Sun, Roger & Klausner, Guillaume & Ammari, S. & Benzazon, Nathan & Reuzé, Sylvain & Estienne, Théo & Niyoteka, Stéphane & Battistella, Enzo & Rouyar, Angéla & Noël, Ge & Beaudre, Anne & Dhermain, Frédéric & Deutsch, Eric & Paragios, Nikos & Robert, Charlotte. (2020). Dosimetry-driven quality measure of brain pseudo Computed Tomography generated from deep learning for MRI-only radiotherapy treatment planning. International Journal of Radiation Oncology*Biology*Physics. 108. 10.1016/j.ijrobp.2020.05.006.
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.
Assessing the impact of key preprocessing concepts on the pseudo CT generation
Andres, E. & Fidon, Lucas & Vakalopoulou, M. & Noël, Ge & Beaudre, A. & Niyoteka, S. & Benzazon, Nathan & Lefkopoulos, D. & Deutsch, E. & Paragios, Nikos & Robert, Charlotte. (2019). 44 Assessing the impact of key preprocessing concepts on the pseudo CT generation. Physica Medica. 68. 27. 10.1016/j.ejmp.2019.09.125.
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.
Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels
Estienne, Théo & Vakalopoulou, Maria & Battistella, Enzo & Carre, Alexandre & Henry, Théophraste & Lerousseau, Marvin & Robert, Charlotte & Paragios, Nikos & Deutsch, Eric. (2021). Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels. 10.1007/978-3-030-71827-5_11.
Elastic Registration–driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT
Chassagnon, Guillaume & Vakalopoulou, Maria & Regent, Alexis & Sahasrabudhe, Mihir & Marini, Rafael & TN, Hoang & Dinh-Xuan, Anh Tuan & Dunogué, Bertrand & Mouthon, Luc & Paragios, Nikos & Revel, Marie-Pierre. (2020). Elastic Registration–driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT. Radiology. 298. 200319. 10.1148/radiol.2020200319.
Exploring Deep Registration Latent Spaces
Estienne, Théo & Vakalopoulou, Maria & Christodoulidis, Stergios & Battistella, Enzo & Henry, Théophraste & Lerousseau, Marvin & Leroy, Amaury & Chassagnon, Guillaume & Revel, Marie-Pierre & Paragios, Nikos & Deutsch, Eric. (2021). Exploring Deep Registration Latent Spaces. 10.1007/978-3-030-87722-4_11.
Image Registration of Satellite Imagery with Deep Convolutional Neural Networks
Vakalopoulou, Maria & Christodoulidis, Stergios & Sahasrabudhe, Mihir & Mougiakakou, Stavroula & Paragios, Nikos. (2019). Image Registration of Satellite Imagery with Deep Convolutional Neural Networks. 4939-4942. 10.1109/IGARSS.2019.8898220.
Weakly Supervised Learning of Metric Aggregations for Deformable Image Registration
Ferrante, Enzo & Dokania, Puneet & Silva, Rafael & Paragios, Nikos. (2018). Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration. IEEE journal of biomedical and health informatics. PP. 10.1109/JBHI.2018.2869700.
Evaluation of ART-Plan autocontouring software for head and neck radiotherapy: A blinded assessment
Young, Tom & Butterworth, Victoria & Misson-Yates, Sarah & Lei, Mary & Kong, Anthony & Petkar, Imran & Reis Ferreira, Miguel & Adjogatse, Delali & King, Andrew & Urbano, Teresa. (2024). 1862: Evaluation of ART-Plan™ autocontouring software for head and neck radiotherapy: A blinded assessment. Radiotherapy and Oncology. 194. S1348-S1351. 10.1016/S0167-8140(24)02178-9.
Guidelines-based automatic segmentation improvement
Perennec, Tanguy & Costea, Madalina-Liana & Colombo, Lorenzo & Temiz, Gizem & Rogé, Maximilien & Supiot, Stephane & Romdhani, Sami & Teboul, Olivier & Paragios, Nikos. (2024). 2601: Guidelines-based automatic segmentation improvements. Radiotherapy and Oncology. 194. S3115-S3118. 10.1016/S0167-8140(24)02777-4. .
Breast Cancer annotation across genders
Costea, Madalina-Liana & Colombo, Lorenzo & Gungor, Gorkem & Klausner, Guillaume & Clavier, J-B & Leduc, Nicolas & Bourgier, Celine & Robert, Charlotte & Herve, Chloe & Ozyar, Enis & Romdhani, Sami & Teboul, Olivier & Temiz, Gizem & Paragios, Nikos. (2024). 1587: Breast cancer annotation across genders. Radiotherapy and Oncology. 194. S564-S567. 10.1016/S0167-8140(24)01956-X.
Multi-Institutional qualitative evaluation of automatic and manual segmentations of organs at risk on PRE ACT breast cancer cohorts
Verhoeven, K. & Brion, T. & Green, W.R. & Balia, M. & Webb, Adam & Rattay, Tim & Liang, Y. & Assia, L.G. & Hafsa, I. & Romdhani, S. & Iandolo, R. & Bombezin-Domino, A. & Teo, K. & McBeth, R. & Koutsopoulos, I. & Talbot, Christopher & Paragyos, N. & Rivera, S.. (2024). Multi-Institutional Qualitative Evaluation of Automatic and Manual Segmentations of Organs at Risk on PRE ACT Breast Cancer Cohorts. International Journal of Radiation Oncology*Biology*Physics. 120. e660-e661. 10.1016/j.ijrobp.2024.07.1450.
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-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, Charlotte. (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.
AI-based cardiac annotation for radiotherapy planning
Botticella, A. & Loap, P. & De Marzi, L. & Lévy, A. & Martin, V. & Moukasse, Y. & Bolle, S. & Rouyar-Nicolas, A. & Le péchoux, C. & Luo, C. & Colame, S. & Martineau-Huynh, C. & Oumani, A. & Roque, T. & Deutsch, E. & Robert, C. & Rivera, S. & Kirova, Y. (2022). AI-based cardiac annotation for radiotherapy planning. SFRO
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.
Weakly Supervised Pan-Cancer Segmentation Tool
Lerousseau, Marvin & Classe, Marion & Battistella, Enzo & Estienne, Théo & Henry, Théophraste & Leroy, Amaury & Sun, Roger & Vakalopoulou, Maria & Scoazec, Jean-Yves & Deutsch, Eric & Paragios, Nikos. (2021). Weakly Supervised Pan-Cancer Segmentation Tool. 10.1007/978-3-030-87237-3_24.
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, Charlotte. (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 2
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, Charlotte. (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.
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, Charlotte. (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
Full-body delineation of ROIs through anatomy-preserving deep learning ensemble networks