Stephanie Tanadini-Lang - HES SO Valais Publications - Aigaion 2.0

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Vorname(n):	Stephanie
Nachname(n):	Tanadini-Lang

Numan Saeed, Salma Hassan, Shahad Hardan, Ahmed Aly, Darya Taratynova, Umair Nawaz, Ufaq Khan, Muhammad Ridzuan, Vincent Andrearczyk, Adrien Depeursinge, Mathieu Hatt, Yutong Xie, Thomas Eugene, Raphä El Metz, M ´ Elanie Dore, Gregory Delpon, Vijay Ram, Kumar Papineni, Kareem Wahid, Cem Dede, Alaa Mohamed, Shawky Ali, Carlos Sjogreen, Mohamed Naser, Clifton D. Fuller, Valentin Oreiller, Mario Jreige, John O. Prior, Catherine Cheze, Le Rest, Olena Tankyevych, Pierre Decazes, Su Ruan, Stephanie Tanadini-Lang, Martin Vallìères, Hesham Elhalawani, Ronan Abgral, Romain Floch, Kevin Kerleguer, Ulrike Schick, Maelle Mauguen, Arman Rahmim und Mohammad Yakub, A Multimodal and Multi-centric Head and Neck Cancer Dataset for Tumor Segmentation and Outcome Prediction (2025)

[URL]

Lorenzo Lo Faro, Hubert S Gabryś, Simon Burgermeister, Daniel Abler, Maksym Fristak, Maiwand Ahmadsei, Ciro Franzese, Adrien Depeursinge, Michel Cuendet, Stephanie Tanadini-Lang, Panagiotis Balermpas, Marta Scorsetti, Matthias Guckenberger und Sebastian M. Christ, Deep learning [18F]-FDG-PET/CT‑based algorithm for tumor burden estimation in metastatic melanoma patients under immunotherapy (2026), in: Clinical and Translational Radiation Oncology, 56(101063)

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Philip Whybra, Alex Zwanenburg, Vincent Andrearczyk, Roger Schaer, Aditya P Apte, Alexandre Ayotte, Bhakti Baheti, Spyridon Bakas, Andrea Bettinelli, Ronald Boellaard, Luca Boldrini, Irène Buvat, Gary J R Cook, Florian Dietsche, Nicola Dinapoli, Hubert S Gabryś, Vicky Goh, Matthias Guckenberger, Mathieu Hatt, Mahdi Hosseinzadeh, Aditi Iyer, Jacopo Lenkowicz, Mahdi A L Loutfi, Steffen Löck, Francesca Marturano, Olivier Morin, Christophe Nioche, Fanny Orlhac, Sarthak Pati, Arman Rahmim, Masoud Seyed, Christopher G Rookyard, Mohammad R Salmanpour, Andreas Schindele, Isaac Shiri, Emiliano Spezi, Stephanie Tanadini-Lang, Florent Tixier, Taman Upadhaya, Vincenzo Valentini, Joost J M van Griethuysen, Fereshteh Yousefirizi, Habib Zaidi, Henning Müller, Martin Vallìères und Adrien Depeursinge, The image biomarker standardization initiative: Standardized convolutional filters for reproducible radiomics and enhanced clinical insights (2024), in: Radiology, 310:2

[]: IF 2022: 19.7

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[DOI]

Alex Zwanenburg, Mahmoud A. Abdalah, Hugo Aerts, Vincent Andrearczyk, Aditya Apte, Saeed Ashrafinia, Roelof J. Beukinga, Ronald Boellaard, Marta Bogowicz, Luca Boldrini, Adrien Depeursinge, Marie-Charlotte Desseroit, Nicola Dinapoli, Cuong Viet Dinh, Sebastian Echegaray, Issam El Naqa, Andriy Y. Fedorov, Roberto Gatta, Robert J. Gillies, Matthias Guckenberger, Nils Gählert, Michael Götz, Mathieu Hatt, Fabian Isensee, Philippe Lambin, Stefan Leger, Ralph T. H. Leijenaar, Jacopo Lenkowicz, Fiona Lippert, Are Losnegård, Klaus H. Maier-Hein, Olivier Morin, Henning Müller, Sandy Napel, Arman Rahmim, Arvind U. K. Rao, Nanna M. Sijtsema, Jairo Socarras Fernandez, Emiliano Spezi, Roel J. H. M Steenbakkers, Stephanie Tanadini-Lang, Daniela Thorwarth, Esther G. C. Troost, Taman Upadhaya, Vincenzo Valentini, Martin Vallìères, Uulke A. van der Heide, Lisanne V. van Dijk, Joost van Griethuysen, Floris H. P. van Velden, Philip Whybra, Christian Richter und Steffen Löck, Standardized quantitative radiomics for high-throughput image-based phenotyping (2020), in: Radiology, 295:2(328-338)

[]: IF 2018 = 7.608

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Lorenzo Lo Faro, Hubert S Gabryś, Simon Burgmeister, Daniel Abler, Maiwand Ahmadsei, Ciro Franzese, Adrien Depeursinge, Michel Cuendet, Stephanie Tanadini-Lang, Panagiotis Balermpas, Marta Scorsetti, Matthias Guckenberger und Sebastian M. Christ, Deep learning PET/CT-based algorithm for estimating tumor burden in metastatic melanoma patients under immunotherapy, in: ESTRO 2025, 2025

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Vincent Andrearczyk, Valentin Oreiller, Moamen Abobakr, Azadeh Akhavanallaf, Panagiotis Balermpas, Sarah Boughdad, Leo Capriotti, Joel Castelli, Catherine Cheze Le Rest, Pierre Decazes, Ricardo Correia, Dina El-Habashy, Hesham Elhalawani, Clifton D. Fuller, Mario Jreige, Yomna Khamis, Agustina La Greca, Abdallah Mohamed, Mohamed Naser, John O. Prior, Su Ruan, Stephanie Tanadini-Lang, Olena Tankyevych, Yazdan Salimi, Martin Vallìères, Pierre Vera, Dimitris Visvikis, Kareem Wahid, Habib Zaidi, Mathieu Hatt und Adrien Depeursinge, Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT, Seiten 1-30, Springer, Cham, 2023

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[DOI]
[URL]

Daniel Abler, Vincent Andrearczyk, Valentin Oreiller, Javier Barranco Garcia, Diem Vuong, Stephanie Tanadini-Lang, Matthias Guckenberger, Mauricio Reyes und Adrien Depeursinge, Comparison of MR preprocessing strategies and sequences for radiomics-based MGMT prediction, in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (MICCAI/BrainLes 2021), Cham, Seiten 367–380, Springer International Publishing, 2022

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[DOI]

Javier Barranco Garcia, Daniel Abler, Mauricio Reyes, Diem Vuong, Matthias Guckenberger, Stephanie Tanadini-Lang und Adrien Depeursinge, Deep learning classifier for MGMT promoter methylation status in glioblastoma cancer, in: 2022 Annual Meeting of the European Society of Radiation Oncology (ESTRO), 2022

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Diem Vuong, Katrin Daetwyler, Marta Bogowicz, Piotr Radojewski, Raphael Meier, Mauricio Reyes, Natalia Saltybaeva, Adrien Depeursinge, Michael Bach, Marco Piccirelli, Matthias Guckenberger, Stephanie Tanadini-Lang und Roland Wiest, Impact of deep learning segmentation methods on the robustness of MR glioblastoma radiomics, in: 2022 Annual Meeting of the European Society of Radiation Oncology (ESTRO), 2022

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Daniel Abler, O. Pusterla, A. Joye-Kühnis, N. Andratschke, M. Bach, A. Bink, S. M. Christ, P. Hagmann, B. Pouymayou, E. Pravatà, P. Radojewski, M. Reyes, L. Ruinelli, R. Schaer, Bram Stieltjes, G. Treglia, W. Valenzuela, R. Wiest, S. Zoergiebel, M. Guckenberger, Stephanie Tanadini-Lang und Adrien Depeursinge, The added value for MRI radiomics and deep-learning for glioblastoma prognostication compared to clinical and molecular information, 2025

[]: submitted to Neuro-Oncology

[URL]

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