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Automatic quantification of thermographic images of complex regional pain syndrome using radiomics and deep learning
Eline A. van Lange, Elisabeth J. Bijl, Cecile C. de Vos, Jacob M. Mostert, Frank J. P. M. Huygen, Martijn P. A. Starmans
Bias and Fairness in Radiomics: A Comparative Analysis of Machine Learning Models on Four Oncology Datasets
Esmeralda Ruiz Pujadas, Jerónimo Hernández-González, Vien Ngoc Dang, Marina Camacho, Karim Lekadir, Martijn P. A. Starmans
Cross-Attention Multimodal Learning for Predicting Response to Neoadjuvant Imatinib in Gastrointestinal Stromal Tumors: A Multicenter Retrospective Study
Fariba Tohidinezhad, Douwe J. Spaanderman, Natalia Oviedo Acosta, Kaouther Mouheb, Karthik Prathaban, David F. Hanff, Dirk J. Grünhagen, Cornelis Verhoef, Joris M. van Sabben, Evelyne Roets, Jette J. Slettenhaar, Hans Gelderblom, Ingrid M. E. Desar, Anna K. L. Reyners, Neeltje Steeghs, Stefan Klein, Martijn P. A. Starmans
Predicting Failure of Active Surveillance in Desmoid-Type Fibromatosis Using Radiomics: An International Multi-center Cohort Study
Stefanie N. Hakkesteegt, Douwe J. Spaanderman, Chiara Colombo, Anne-Rose W. Schut, Andrea Vanzulli, Francesco Barretta, C. Morosi, Marco Fiore, Peter Ferguson, Harini Suraweera, Anthony M. Griffin, Lawrence M. White, Joel Shapiro, Danchen Ge, Dirk J. Grünhagen, Geert J. L. H. van Leenders, David Hanff, Jacob J. Visser, Wiro J. Niessen, Stefan Klein, the Dutch Grafiti Research Group, Rebecca A. Gladdy, Alessandro Gronchi, Cornelis Verhoef, Martijn P. A. Starmans
Robust multicentre detection and classification of colorectal liver metastases on CT: application of foundation models
Shruti Atul Mali, Zohaib Salahuddin, Yumeng Zhang, Andre Aichert, Xian Zhong, Henry C. Woodruff, Maciej Bobowicz, Katrine Riklund, Juozas Kupčinskas, Lorenzo Faggioni, Roberto Francischello, Razvan L. Miclea, Philippe Lambin, on behalf of EuCanImage Working Group
AI voor betere sarcomenzorg
Stefan Klein, Fariba Tohidinezhad, Martijn P. A. Starmans
Evaluating Open-Weight Large Language Models for Structured Data Extraction from Narrative Medical Reports Across Multiple Use Cases and Languages
Great study to systematically benchmark various LLMs and prompting strategies to extract structured information from radiological and pathological reports across six use cases and multiple languages. These findings show that open-weight LLMs can extract structured data from clinical reports across diseases, languages, and institutions, offering a scalable approach for clinical data curation.
Douwe J. Spaanderman, Karthik Prathaban, Petr Zelina, Kaouther Mouheb, Lukáš Hejtmánek, Matthew Marzetti, Antonius W. Schurink, Damian Chan, Ruben Niemantsverdriet, Frederik Hartmann, Zhen Qian, Maarten G. J. Thomeer, Petr Holub, Farhan Akram, Frank J. Wolters, Meike W. Vernooij, Cornelis Verhoef, Esther E. Bron, Vít Nováček, Dirk J. Grünhagen, Wiro J. Niessen, Martijn P. A. Starmans, Stefan Klein
Evaluating Open-Weight Large Language Models for Structured Data Extraction from Narrative Medical Reports Across Multiple Use Cases and Languages
AI in Radiological Imaging of Soft-Tissue and Bone Tumours: A Systematic Review Evaluating Against CLAIM and FUTURE-AI Guidelines
Tremendous effort of our sarcoma PhD team to review over 300 (!) papers on AI for STBT, indicating substantial gaps towards implementation in clinical practice and insights how to get there!
Douwe J. Spaanderman, Matthew Marzetti, Xinyi Wan, Andrew F. Scarsbrook, Philip Robinson, Edwin H. G. Oei, Jacob J. Visser, Robert Hemke, Kirsten van Langevelde, David F. Hanff, Geert J. L. H. van Leenders, Cornelis Verhoef, Dirk J. Grünhagen, Wiro J. Niessen, Stefan Klein, Martijn P. A. Starmans
AI in Radiological Imaging of Soft-Tissue and Bone Tumours: A Systematic Review Evaluating Against CLAIM and FUTURE-AI Guidelines
A Large-Scale Multicenter Breast Cancer DCE-MRI Benchmark Dataset with Expert Segmentations
Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel, Smriti Joshi, Richard Osuala, Apostolia Tsirikoglou, Maciej Bobowicz, Javier del Riego, Alessandro Catanese, Katarzyna Gwoździewicz, Maria-Laura Cosaka, Pasant M. Abo-Elhoda, Sara W. Tantawy, Shorouq S. Sakrana, Norhan O. Shawky-Abdelfatah, Amr Muhammad Abdo Salem, Androniki Kozana, Eugen Divjak, Gordana Ivanac, Katerina Nikiforaki, Michail E. Klontzas, Rosa García-Dosdá, Meltem Gulsun-Akpinar, Oğuz Lafcı, Ritse Mann, Carlos Martín-Isla, Fred Prior, Kostas Marias, Martijn P. A. Starmans, Fredrik Strand, Oliver Díaz, Laura Igual, Karim Lekadir
FUTURE-AI: International Consensus Guideline for Trustworthy and Deployable Artificial Intelligence in Healthcare
Very proud of our first large international consensus guideline on trustworthy AI!
Karim Lekadir, Alejandro F. Frangi, Antonio R. Porras, Ben Glocker, Celia Cintas, Curtis P. Langlotz, Eva Weicken, Folkert W. Asselbergs, Fred Prior, Gary S. Collins, Georgios Kaissis, Gianna Tsakou, Irène Buvat, Jayashree Kalpathy-Cramer, John Mongan, Julia A. Schnabel, Kaisar Kushibar, Katrine Riklund, Kostas Marias, Lameck M. Amugongo, Lauren A. Fromont, Lena Maier-Hein, Leonor Cerdá Alberich, Luis Marti-Bonmati, M. Jorge Cardoso, Maciej Bobowicz, Mahsa Shabani, Manolis Tsiknakis, Maria A. Zuluaga, Marie-Christine Fritzsche, Marina Camacho, Marius George Linguraru, Markus Wenzel, Marleen De Bruijne, Martin G. Tolsgaard, Melanie Goisauf, Mónica Cano Abadía, Nikolaos Papanikolaou, Noussair Lazrak, Oriol Pujol, Richard Osuala, Sandy Napel, Sara Colantonio, Smriti Joshi, Stefan Klein, Susanna Aussó, Wendy A. Rogers, Zohaib Salahuddin, Martijn P. A. Starmans
FUTURE-AI: International Consensus Guideline for Trustworthy and Deployable Artificial Intelligence in Healthcare