Publications

2026

Papers in international journals

1. S. Scharm, C. Schaefer-Prokop, A. Schreuder, J. Ehmig, A. Hunkemöller, J. Fuge, B. Seeliger, J. Schupp, F. Wacker and H. Shin, "Extent of alveolar collapse in expiratory CT as a prognostic marker in idiopathic pulmonary fibrosis", PLOS One, 2026;21:e0345308.
   Abstract DOI PMID
2. D. Zhong, G. Sidorenkov, M. Greuter, C. Jacobs, P. de Jong, H. Gietema, H. Groen, F. Mohamed Hoesein, N. Antonissen, R. Stadhouders, H. Lancaster, M. Heuvelmans, R. Vliegenthart and G. de Bock, "Improving Lung Cancer Screening Selection: A Comparative Analysis of Risk Models and Traditional Criteria in a Western European General Population", Cancers, 2026;18:724.
   Abstract DOI PMID
3. F. Wilting, J. Douwes, A. Patel, F. Schreuder, R. Dammers, G. Hannink, W. Jolink, S. Pegge, L. Sondag, M. Wermer, H. van der Worp, F. Meijer and C. Klijn, "Deep learning-based automated segmentation of intracerebral haemorrhage, intraventricular haemorrhage and perihaematomal oedema on non-contrast CT", European Stroke Journal, 2026;11.
   Abstract DOI PMID
4. S. Shojaei, D. Yakar, N. Vellinga, V. Bozgo, T. Kwee, H. Huisman and J. Mifsud Bonnici, "The AI Act and the MDR post-market requirements for semiautonomous AI SaMD: a radiology case study in prostate cancer", Abdominal Radiology, 2026.
   Abstract DOI PMID
5. E. Munari, P. Antonini, L. Cima, R. Polati, A. Caliò, S. Gobbo, M. Colecchia, G. Netto, A. Antonelli, R. Bertolo, C. Grisi, G. Litjens and M. Brunelli, "The evolution of prostate cancer grading: from Gleason score to risk taxonomy and the artificial intelligence revolution", Virchows Archiv, 2026.
   Abstract DOI
6. Q. van Lohuizen, S. Fransen, H. Huisman, J. Wolterink, T. Kwee, D. Yakar and F. Simonis, "Aleatoric uncertainty in accelerated prostate MRI reconstruction: echo-train dropout versus Gaussian noise Monte Carlo sampling", Magnetic Resonance Materials in Physics, Biology and Medicine, 2026.
   Abstract DOI
7. S. Bunk, E. Bennink, G. Sidorenkov, M. Heuvelmans, H. Groen, H. Gietema, M. Prokop, J. Aerts, C. Jacobs, G. de Bock, P. de Jong, R. Vliegenthart, F. Mohamed Hoesein, J. Aerts, R. Cornelissen, R. Stadhouders, J. van Rooij, L. Trap, M. Prokop, C. Schaefer-Prokop, C. Jacobs, G. de Bock, M. Heuvelmans, G. Sidorenkov, D. Zhong, H. Groen, R. Vliegenthart, P. de Jong, F. Mohamed Hoesein, S. Bunk and G. Downward, "CT-based body composition and its change through time in relation to outcomes in participants screened for lung cancer", eBioMedicine, 2026;127:106276.
   DOI
8. F. Khoraminia, M. Olislagers, F. de Jong, F. Akram, A. Nakauma Gonzalez, D. Lichtenberg, A. Stubbs, J. Costello, L. Rijstenberg, G. van Leenders, A. Vrieling, K. Aben, L. Kiemeney, R. Hoedemaeker, C. Bangma, S. Vermeulen, G. Litjens, N. Khalili and T. Zuiverloon, "Predicting bladder cancer molecular subtypes linked to bacillus Calmette-Guerin response from histology images using deep learning", Preprint, 2026.
   Abstract DOI
9. Q. van Lohuizen, S. Fransen, G. Yiasemis, J. Twilt, C. Roest, Y. Arita, J. Borstlap, J. Fütterer, M. de Rooij, D. Rouw, I. Schoots, B. Turkbey, S. Withey, F. Simonis, H. Huisman, T. Kwee, J. Teuwen and D. Yakar, "Diagnostic assessment of artificial intelligence reconstruction on accelerated prostate MRI: a retrospective, paired, multi-reader multi-case study", European Radiology, 2026.
   Abstract DOI
10. N. Antonissen, S. Schalekamp, H. Hahn, K. van Leeuwen and C. Jacobs, "Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence", European Radiology, 2026.
    Abstract DOI
11. J. Twilt, A. Saha, J.S. Bosma, G. Giannarini, A. Padhani, D. Yakar, M. Elschot, J. Veltman, J. Fütterer, H. Huisman, M. de Rooij, F. the Consortium, A. Saha, J.S. Bosma, J. Twilt, B. van Ginneken, C. Noordman, I. Slootweg, C. Roest, S. Fransen, M. Sunoqrot, T. Bathen, D. Rouw, J. Immerzeel, J. Geerdink, C. van Run, M. Groeneveld, J. Meakin, D. Yakar, M. Elschot, J. Veltman, J. Fütterer, M. de Rooij, H. Huisman, A. Bjartell, A. Padhani, D. Bonekamp, G. Villeirs, G. Salomon, G. Giannarini, H. Huisman, J. Kalpathy-Cramer, J. Barentsz, K. Maier-Hein, M. Elschot, M. Rusu, N. Obuchowski, O. Rouviere, R. van den Bergh, V. Panebianco, V. Kasivisvanathan, A. Karagöz, A. Bône, A. Routier, A. Marcoux, C. Abi-Nader, C. Li, D. Feng, D. Alis, E. Karaarslan, E. Ahn, F. Nicolas, G. Sonn, I. Bhattacharya, J. Kim, J. Shi, H. Jahanandish, H. An, H. Kan, I. Oksuz, L. Qiao, M. Rohé, M. Yergin, M. Rusu, M. Khadra, M. Seker, M. Kartal, N. Debs, R. Fan, S. Saunders, S. Soerensen, S. Moroianu, S. Vesal, Y. Yuan, A. Malakoti-Fard, A. Mačiunien, A. Kawashima, A. Machadov, A. Moreira, A. Ponsiglione, A. Rappaport, A. Stanzione, A. Ciuvasovas, B. Turkbey, B. De Keyzer, B. Pedersen, B. Eijlers, C. Chen, C. Riccardo, D. Alis, E. Courrech Staal, F. Jäderling, F. Langkilde, G. Aringhieri, G. Brembilla, H. Son, H. Vanderlelij, H. Raat, I. Pikuniene, I. Macova, I. Schoots, I. Caglic, J. Zawaideh, J. Wallström, L. Bittencourt, M. Khurram, M. Choi, N. Takahashi, N. Tan, O. Rouvière, P. Franco, P. Gutierrez, P. Thimansson, P. Hanus, P. Puech, P. Rau, P. De Visschere, R. Guillaume, R. Cuocolo, R. Falcão, R. van Stiphout, R. Girometti, R. Briediene, R. Grigiene, S. Gitau, S. Withey, S. Ghai, T. Penzkofer, T. Barrett, V. Panebianco, V. Tammisetti, V. L\ogager , V. Černý, W. Venderink, Y. Law and Y. Lee, "Evaluating an AI-driven Triaging Workflow for MRI-based Clinically Significant Prostate Cancer Diagnosis: A Simulation Study", Radiology: Imaging Cancer, 2026;8.
    Abstract DOI
12. T. Perik, G. Litjens, N. Alves, E. Smit, M. Stommel, E. van Geenen, H. Huisman and J. Hermans, "Quantitative CT Perfusion as a prognostic biomarker for chemotherapy response in patients with pancreatic ductal adenocarcinoma", Abdominal Radiology, 2026.
    Abstract DOI
13. D. Peeters, B. Obreja, N. Antonissen, Z. Saghir, U. Pastorino, M. Silva, G. de Bock, H. Gietema, F. Gleeson, M. Heuvelmans, S. Lam, G. Litjens, F. Mohamed Hoesein, C. Schaefer-Prokop, E. Scholten, A. Snoeckx, E. van der Heijden, R. Vliegenthart, M. Prokop, C. Jacobs and O. behalf of the Consortium, "Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge", Radiology: Artificial Intelligence, 2026.
    Abstract DOI
14. J. Spronck, L. van Eekelen, D. van Midden, J. Bogaerts, L. Tessier, V. Dechering, M. Demirel-Andishmand, G. de Souza, R. Nemeth, E. Munari, G. Bogina, I. Girolami, A. Eccher, B. Acs, C. Boyaci, N. Klubickova, M. Looijen-Salamon, S. Vos and F. Ciompi, "A tissue and cell-level annotated H&E and PD-L1 histopathology image dataset in non-small cell lung cancer", IEEE Journal of Biomedical and Health Informatics, 2026;():1-11.
    DOI
15. A. Hunkemöller, T. Werncke, J. Dittrich, C. Schaefer-Prokop, F. Söbbeler, M. Avsar, J. Salman, A. Ruhparwar, R. Blasczyk, S. Besli, C. Figueiredo, A. Enzig-Strohm, F. Wacker and H. Shin, "Photon-counting CT for dynamic lung perfusion: validation of a low-dose protocol in a porcine lung transplantation model", European Radiology Experimental, 2026;10.
    Abstract DOI
16. S. Jarkman, M. Lindvall, C. Lundström, D. Treanor and J. van der Laak, "Designing AI Tools for Pathology: A Mixed-Method Study on User Interface Design for Breast Cancer Lymph Node Metastases Detection", Intelligence-Based Medicine, 2026:100396.
    DOI
17. M. Sappia, B. van Ginneken, C. de Korte, J. van Dillen and K. Murphy, "Assessment of modifications to a blind-sweep ultrasound protocol for improved lower-uterus imaging by novice operators", Scientific Reports, 2026.
    Abstract DOI
18. A. Schipper, P. Belgers, R. O'Connor, L. van de Wouw, L. Builtjes, J.S. Bosma, R. Kusters, S. Kurstjens, M. Rutten and B. van Ginneken, "Large Language Model Automated Extraction of Clinical Signs and Symptoms From Emergency Department Reports for Machine Learning Prediction Models: Development and Validation Study", JMIR Medical Informatics, 2026;14:e81500-e81500.
    Abstract DOI
19. D. Schouten, J. van der Laak, D. Somford, H. Küsters-Vandevelde, N. Khalili and G. Litjens, "Three-dimensional reconstruction of gigapixel whole-mount histopathology specimens with RAPID", Scientific Reports, 2026.
    Abstract DOI
20. J. van der Zande, L. Alvarez-Florez, R. Volleberg, C. Brás, D. Karkalousos, R. Nijveldt, N. van Royen, T. Leiner, N. Khalili, G. Litjens, J. Thannhauser and I. Isgum, "Deep Learning for Cardiac Image Analysis", JACC: Cardiovascular Imaging, 2026.
    DOI
21. M. Rijthoven, W. Aswolinskiy, L. Tessier, R. Salgado, J. van der Laak, F. Ciompi and TIGER consortium, "Analysis of computational tumor-infiltrating lymphocytes in breast cancer from the results of the TIGER challenge", Nature Communications, 2026.
    Abstract DOI
22. C. van den Berg, J. Dittrich, S. Scharm, C. Schaefer-Prokop, S. Dettmer, A. Hunkemoeller, J. Eckstein, J. Glandorf, F. Wacker, G. Pöhler and H. Shin, "CT-based lung ventilation metrics: reference ranges and pulmonary function test correlations in healthy individuals", European Radiology, 2026.
    Abstract DOI
23. S. Gatidis, F. Peisen, A. Wagner, P. Choudja, A. Othman, A. Sanner, N. Grauhan, S. Kim, D. Graafen, L. Müller, T. Lo\ssau , J. Moltz, T. Kohlbrandt, A. Hering, C. La Fougère, K. Nikolaou and T. Küstner, "A longitudinal whole-body CT dataset with manually annotated tumor lesions", Scientific Data, 2026.
    Abstract DOI

Preprints

1. M. Stegeman, L. Philipp, F. van der Graaf, M. D'Amato, C. Grisi, L. Builtjes, J.S. Bosma, J. Lefkes, R. Weber, J. Meakin, T. Koopman, A. Mickan, M. Prokop, E. Smit, G. Litjens, J. van der Laak, B. van Ginneken, M. de Rooij, H. Huisman, C. Jacobs, F. Ciompi and A. Hering, "Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language", arXiv:2603.02790, 2026.
   Abstract DOI arXiv
2. C. Grisi, K. Faryna, N. Uysal, V. Agosti, E. Munari, S. Kammerer-Jacquet, P. Salles, Y. Tolkach, R. Büttner, S. Semko, M. Pikul, A. Heidenreich, J. van der Laak and G. Litjens, "Deep Learning From Routine Histology Improves Risk Stratification for Biochemical Recurrence in Prostate Cancer", arXiv:2603.14187, 2026.
   Abstract DOI arXiv

Papers in conference proceedings

1. R. Weber, N. Rocholl, M. de Grauw, M. Prokop, E. Smit and A. Hering, "ULS+: Data-driven Model Adaptation Enhances Lesion Segmentation", 2026.
   arXiv
2. L. Heil, R. van der Waerden, R. Volleberg, J. Thannhauser, J. van der Zande, T. Luttikholt, P. Cancian, X. Gu, B. van Ginneken, C. Gutierrez, I. Isgum, N. van Royen and S. Saitta, "Uncertainty Analysis in Intravascular Oct Segmentation", 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI), 2026:1-5.
   DOI
3. J. Dusseljee, S. de Boer and A. Hering, "Kidney Cancer Detection Using 3D-Based Latent Diffusion Models", 2026.
   arXiv
4. A. Arab, V. Garcia, S. Kahaki, M. van Rijthoven, R. Salgado, B. Gallas, F. Ciompi, N. Petrick and W. Chen, "Assessment of AI segmentation models in histopathology whole slide images: the effect of the unit of analysis", Medical Imaging 2026: Digital and Computational Pathology, 2026:31.
   DOI
5. M. van Lente, K. Verdonschot, G. Laimer, S. van der Lei, M. Meijerink, R. Bale, H. Huisman, J. Fütterer and C. Overduin, "Deep learning-based ablation margin assessment in thermal ablation of colorectal liver metastasis: preliminary results of an automatic segmentation workflow", Medical Imaging 2026: Image-Guided Procedures, Robotic Interventions, and Modeling, 2026:38.
   DOI
6. J. Tagscherer, S. de Boer, L. Philipp, F. van der Graaf, D. Peeters, J. Bosma, L. Leijten, B. Obreja, E. Smit and A. Hering, "EvalBlocks: A Modular Pipeline for Rapidly Evaluating Foundation Models in Medical Imaging", 2026.
   arXiv

Abstracts

1. L. Leijten, E. van der Heijden, E. Aarntzen, R. Verhoeven and C. Jacobs, "Deep-learning based malignancy risk estimation of pulmonary nodules in PET/CT imaging", European Congress of Radiology, 2026.
   Abstract
2. R. Dinnessen, N. Antonissen, D. Peeters, H. Gietema, F. Mohamed Hoesein, E. Scholten, C. Schaefer-Prokop and C. Jacobs, "Performance and generalisability of a screening-trained deep learning model for pulmonary nodule malignancy risk estimation on a multicentre dataset of incidental nodules", European Congress of Radiology, 2026.
   Abstract
3. A. Cerrato Nieto, E. Scholten, S. Schalekamp, M. Prokop and C. Jacobs, "Benchmarking lung tumour segmentation models: stratified performance of deep learning models across tumour sizes and cancer stages", European Congress of Radiology, 2026.
   Abstract
4. M. Vitale, M. Vegter, C. Jacobs and M. Boenink, "Principles for AI-enabled population screening", European Congress of Radiology, 2026.
   Abstract
5. N. Antonissen, S. Schalekamp, H. Hahn, K. van Leeuwen and C. Jacobs, "Commercially available AI products for CT-based lung cancer screening: capabilities, clinical evidence, and alignment with international screening frameworks", European Congress of Radiology, 2026.
   Abstract
6. D. Peeters, B. Obreja, N. Antonissen, Z. Saghir, U. Pastorino, G. De Bock, R. Vliegenthart, M. Prokop and C. Jacobs, "Benchmarking of Artificial Intelligence and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: Results of the LUNA25 Challenge", European Congress of Radiology, 2026.
   Abstract
7. M. Vitale, M. Vegter, C. Jacobs and M. Boenink, "Algorithmic Fairness unfolded: collaborative ethnography within a medical imaging AI lab for Lung Cancer Screening", European Congress of Radiology, 2026.
   Abstract

PhD theses

1. J. Twilt, "Artificial Intelligence and Biparametric MRI in Prostate Cancer Detection", PhD thesis, 2026.
   Abstract Url
2. A. Saha, "Artificial Intelligence x Prostate Cancer Detection on MRI", PhD thesis, 2026.
   Abstract Url
3. P. Venditelli, "Learning from histopathology images: AI-driven biomarkers for pancreatic ductal adenocarcinoma", PhD thesis, 2026.
   Abstract Url

Other publications

1. B. Abrahamsen, J.S. Bosma, H. Huisman and M. Elschot, "A Federated Benchmark for Clinical Natural Language Processing (FedDRAGON)", Studies in Health Technology and Informatics, 2026.
   Abstract DOI