50 citations · 166 across the 8 of their papers we have counts for
8 papers
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 +14
Background: Response to neoadjuvant imatinib in gastrointestinal stromal tumors (GISTs) is highly variable and cannot be reliably predicted using current clinical or molecular mark…
Evaluating Open-Weight Large Language Models for Structured Data Extraction from Narrative Medical Reports Across Multiple Use Cases and Languages
Douwe J. Spaanderman, Karthik Prathaban, Petr Zelina +20
Large language models (LLMs) are increasingly used to extract structured information from free-text clinical records, but prior work often focuses on single tasks, limited models,…
AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines
Douwe J. Spaanderman, Matthew Marzetti, Xinyi Wan +13
Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an over…
A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel +30
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a…
Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning
Douwe J. Spaanderman, Martijn P. A. Starmans, Gonnie C. M. van Erp +9
Segmentations are crucial in medical imaging to obtain morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in the radiologist's cl…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
An automated machine learning framework to optimize radiomics model construction validated on twelve clinical applications
Martijn P. A. Starmans, Sebastian R. van der Voort, Thomas Phil +43
Predicting clinical outcomes from medical images using quantitative features (``radiomics'') requires many method design choices, Currently, in new clinical applications, finding t…
Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach
Martijn P. A. Starmans, Milea J. M. Timbergen, Melissa Vos +10
Distinguishing gastrointestinal stromal tumors (GISTs) from other intra-abdominal tumors and GISTs molecular analysis is necessary for treatment planning, but challenging due to it…