ResearcherRadboud University Medical CenterActive in the last 2 years
Natália Alves
Matched on ORCID iD 0000-0002-7034-0137; OpenAlex author ID A5075748419, A5127823155. Sources: OpenAlex.
Their work
- Papers
- 8 (first author 3, last author 0)
- Trials
- 0
- Grants
- 0
- Labs led
- 0
- Collaborators
- 15
2022: 2 papers · 2023: 1 paper · 2024: 1 paper · 2025: 1 paper · 2026: 3 papers
Papers 8
- Quantitative CT Perfusion as a prognostic biomarker for chemotherapy response in patients with pancreatic ductal adenocarcinoma. Co-author DOI 10.1007/s00261-026-05420-5↗
- Artificial intelligence and radiologists in pancreatic cancer detection using standard of care CT scans (PANORAMA): an international, paired, non-inferiority, … Co-author DOI 10.1016/s1470-2045(25)00567-4↗
- AI-assisted screening for pancreatic cancer – Authors' reply Co-author DOI 10.1016/s1470-2045(26)00054-9↗
- End-to-end prognostication in pancreatic cancer by multimodal deep learning: a retrospective, multicenter study. Co-author DOI 10.1007/s00330-025-11694-y↗
- Automated Quantitative Analysis of CT Perfusion to Classify Vascular Phenotypes of Pancreatic Ductal Adenocarcinoma. First author DOI 10.3390/cancers16030577↗
- Artificial Intelligence in Pancreatic Ductal Adenocarcinoma Imaging: A Commentary on Potential Future Applications. First author DOI 10.1053/j.gastro.2023.04.003↗
- Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography. Co-author DOI 10.3390/cancers14020376↗
- Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. First author DOI 10.3390/cancers14143498↗
Closest collaborators
The 15 strongest of 15 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- HenkJan J. Huisman · 7 shared papers, 2022–2026 · strength 1.86
- Paper AI-assisted screening for pancreatic cancer – Authors' reply DOI 10.1016/s1470-2045(26)00054-9↗
- Paper Quantitative CT Perfusion as a prognostic biomarker for chemotherapy response in patients with pancreatic ductal adenoc… DOI 10.1007/s00261-026-05420-5↗
- Paper Automated Quantitative Analysis of CT Perfusion to Classify Vascular Phenotypes of Pancreatic Ductal Adenocarcinoma. DOI 10.3390/cancers16030577↗
- Megan Schuurmans · 5 shared papers, 2022–2026 · strength 1.23
- Paper AI-assisted screening for pancreatic cancer – Authors' reply DOI 10.1016/s1470-2045(26)00054-9↗
- Paper End-to-end prognostication in pancreatic cancer by multimodal deep learning: a retrospective, multicenter study. DOI 10.1007/s00330-025-11694-y↗
- Paper Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. DOI 10.3390/cancers14143498↗
- John J. Hermans · 5 shared papers, 2022–2025 · strength 1.11
- Paper Automated Quantitative Analysis of CT Perfusion to Classify Vascular Phenotypes of Pancreatic Ductal Adenocarcinoma. DOI 10.3390/cancers16030577↗
- Paper End-to-end prognostication in pancreatic cancer by multimodal deep learning: a retrospective, multicenter study. DOI 10.1007/s00330-025-11694-y↗
- Paper Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. DOI 10.3390/cancers14143498↗
- John J. Hermans · 2 shared papers, 2026 · strength 0.75
- T. H. Perik · 2 shared papers, 2024–2026 · strength 0.63
- Paper Quantitative CT Perfusion as a prognostic biomarker for chemotherapy response in patients with pancreatic ductal adenoc… DOI 10.1007/s00261-026-05420-5↗
- Paper Automated Quantitative Analysis of CT Perfusion to Classify Vascular Phenotypes of Pancreatic Ductal Adenocarcinoma. DOI 10.3390/cancers16030577↗
- Pierpaolo Vendittelli · 3 shared papers, 2022–2025 · strength 0.62
- Paper End-to-end prognostication in pancreatic cancer by multimodal deep learning: a retrospective, multicenter study. DOI 10.1007/s00330-025-11694-y↗
- Paper Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. DOI 10.3390/cancers14143498↗
- Paper Artificial Intelligence in Pancreatic Ductal Adenocarcinoma Imaging: A Commentary on Potential Future Applications. DOI 10.1053/j.gastro.2023.04.003↗
- Geke Litjens · 2 shared papers, 2022–2026 · strength 0.50
- Paper Quantitative CT Perfusion as a prognostic biomarker for chemotherapy response in patients with pancreatic ductal adenoc… DOI 10.1007/s00261-026-05420-5↗
- Paper Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography. DOI 10.3390/cancers14020376↗
- Geert J. S. Litjens · 2 shared papers, 2023–2025 · strength 0.41
- Paper End-to-end prognostication in pancreatic cancer by multimodal deep learning: a retrospective, multicenter study. DOI 10.1007/s00330-025-11694-y↗
- Paper Artificial Intelligence in Pancreatic Ductal Adenocarcinoma Imaging: A Commentary on Potential Future Applications. DOI 10.1053/j.gastro.2023.04.003↗
- Núria Malats · 2 shared papers, 2023–2025 · strength 0.41
- Paper End-to-end prognostication in pancreatic cancer by multimodal deep learning: a retrospective, multicenter study. DOI 10.1007/s00330-025-11694-y↗
- Paper Artificial Intelligence in Pancreatic Ductal Adenocarcinoma Imaging: A Commentary on Potential Future Applications. DOI 10.1053/j.gastro.2023.04.003↗
- Erwin J. M. van Geenen · 1 shared paper, 2026 · strength 0.32
- Martijn W.J. Stommel · 1 shared paper, 2026 · strength 0.32
- Nannan Xue · 1 shared paper, 2025 · strength 0.24
- Matthias J. Löhr · 1 shared paper, 2023 · strength 0.18
- Caroline Sophie Verbeke · 1 shared paper, 2023 · strength 0.18
- David Kuang-Fu Chang · 1 shared paper, 2023 · strength 0.18
How to reach this person
A chain of introductions through people who have published, run trials or held grants together (warm_path@1: fewest links first, then the strongest). Built from public records only; no contact details are kept.