L. Builtjes, J. Bosma, M. Prokop, B. van Ginneken and A. Hering, "Leveraging open-source large language models for clinical information extraction in resource-constrained settings",
JAMIA Open,
2025;8.
C. Noordman, L. te Molder, M. Maas, C. Overduin, J. Fütterer and H. Huisman, "Real-Time Deep-Learning Image Reconstruction and Instrument Tracking in MR-Guided Biopsies",
Journal of Magnetic Resonance Imaging,
2025.
L. Stam, S. Linden, R. Aquarius, A. Hering, L. Oostveen, F. Meijer and H. Boogaarts, "In-vivo cerebral artery pulsation assessment with Dynamic computed tomography angiography",
European Journal of Radiology,
2025;182:111828.
E. de la Rosa, M. Reyes, S. Liew, A. Hutton, R. Wiest, J. Kaesmacher, U. Hanning, A. Hakim, R. Zubal, W. Valenzuela, D. Robben, D. Sima, V. Anania, A. Brys, J. Meakin, A. Mickan, G. Broocks, C. Heitkamp, S. Gao, K. Liang, Z. Zhang, M. Rahman Siddiquee, A. Myronenko, P. Ashtari, S. Van Huffel, H. Jeong, C. Yoon, C. Kim, J. Huo, S. Ourselin, R. Sparks, A. Clèrigues, A. Oliver, X. Lladó, L. Chalcroft, I. Pappas, J. Bertels, E. Heylen, J. Moreau, N. Hatami, C. Frindel, A. Qayyum, M. Mazher, D. Puig, S. Lin, C. Juan, T. Hu, L. Boone, M. Goubran, Y. Liu, S. Wegener, F. Kofler, I. Ezhov, S. Shit, M. Hernandez Petzsche, M. Müller, B. Menze, J. Kirschke and B. Wiestler, "DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge",
Nature Communications,
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H. Häntze, L. Xu, M. Rattunde, L. Donle, F. Dorfner, A. Hering, J. Nawabi, L. Adams and K. Bressem, "MRI annotation using an inversion-based preprocessing for CT model adaptation",
European Radiology Experimental,
2025;9.
R. Lomans, V. Angerilli, J. Spronck, L. Kodach, I. Gullo, F. Carneiro, R. van der Post and F. Ciompi, "Deep learning for multiclass tumor cell detection in histopathology slides of hereditary diffuse gastric cancer",
iScience,
2025;28:113064.
N. Antonissen, O. Tryfonos, I. Houben, C. Jacobs, M. de Rooij and K. van Leeuwen, "Artificial intelligence in radiology: 173 commercially available products and their scientific evidence",
European Radiology,
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R. Dinnessen, D. Peeters, N. Antonissen, F. Mohamed Hoesein, H. Gietema, E. Scholten, C. Schaefer-Prokop and C. Jacobs, "Performance of a screening-trained DL model for pulmonary nodule malignancy estimation of incidental clinical nodules",
European Radiology,
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A. Snoeckx, M. Silva, H. Prosch, J. Biederer, T. Frauenfelder, F. Gleeson, C. Jacobs, H. Kauczor, A. Parkar, C. Schaefer-Prokop, M. Prokop and M. Revel, "Lung cancer screening with low-dose CT: definition of positive, indeterminate, and negative screen results. A nodule management recommendation from the European Society of Thoracic Imaging",
European Radiology,
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M. Prokop, C. Schaefer-Prokop, C. Jacobs, A. Snoeckx, J. Biederer, T. Frauenfelder, F. Gleeson, H. Kauczor, A. Parkar, R. Vliegenthart, M. Revel, M. Silva and H. Prosch, "Aggressiveness-guided nodule management for lung cancer screening in Europe--justification for follow-up intervals and definition of growth",
European Radiology,
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E. van der Heijden, M. Snoeren and C. Jacobs, "Incidentele longnoduli op CT-scan: wat te doen? [Incidental pulmonary nodules on CT imaging: what to do?]",
Nederlands Tijdschrift voor Geneeskunde,
2025;169:D8431.
M. Sappia, C. de Korte, B. van Ginneken, D. Ninalga, S. Kondo, S. Kasai, K. Hirasawa, T. Akumu, C. Martín-Isla, K. Lekadir, V. Campello, J. Fabila, A. Beverdam, J. van Dillen, C. Neff and K. Murphy, "ACOUSLIC-AI challenge report: Fetal abdominal circumference measurement on blind-sweep ultrasound data from low-income countries",
Medical Image Analysis,
2025;105:103640.
J. Twilt, A. Saha, J.S. Bosma, A. Padhani, D. Bonekamp, G. Giannarini, R. van den Bergh, V. Kasivisvanathan, N. Obuchowski, D. Yakar, M. Elschot, J. Veltman, J. Fütterer, H. Huisman, M. de Rooij, P. Consortium, J. Twilt, A. Saha, J.S. Bosma, 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, J. Kalpathy-Cramer, J. Barentsz, K. Maier-Hein, M. Rusu, N. Obuchowski, O. Rouviere, R. van den Bergh, V. Panebianco, V. Kasivisvanathan, A. Malakoti-Fard, A. Dehghanpour, A. Moreira, A. Cazzato, A. Ponsiglione, A. Stanzione, B. de Keyzer, B. Pedersen, C. Page, C. Mai, D. Alis, D. Versteegden, E. Camisassa, E. Staal, F. Martini, F. Alessandrino, F. Jäderling, G. Agrotis, G. Avesani, G. Brembilla, G. Francese, H. Raat, H. Sahin, I. Schoots, I. Caglic, J. Zawaideh, L. Bittencourt, L. Mannacio, M. Gonçalves, M. Özdemir, M. Nahouraii, M. da Silva, M. Khurram, M. Choi, P. Franco, P. Correia, P. Riesenberger, P. Hanus, P. de Visschere, R. Guillaume, R. Cuocolo, R. Falcão, R. van Stiphout, R. Girometti, R. Gabriele, R. Briediene, R. Grigiene, S. Withey, S. Durmaz, S. Santos, T. Russo, T. Barrett, V. Forte, V. Tammisetti, V. Obmann, W. Weston, Y. Law, Y. Yuruk, Y. Chang and Y. Arita, "AI-Assisted vs Unassisted Identification of Prostate Cancer in Magnetic Resonance Images",
JAMA Network Open,
2025;8:e2515672.
S. Fransen, J.S. Bosma, Q. van Lohuizen, C. Roest, F. Simonis, T. Kwee, D. Yakar and H. Huisman, "Simulating workload reduction with an AI-based prostate cancer detection pathway using a prediction uncertainty metric",
European Radiology,
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C. Noordman, S. Borgers, M. Boomsma, T. Kwee, M. van der Lees, C. Overduin, M. de Rooij, D. Yakar, J. Fütterer and H. Huisman, "Deep learning-based temporal MR image reconstruction for accelerated interventional imaging during in-bore biopsies",
Journal of Medical Imaging,
2025;12.
M. Tran, P. Schmidle, R. Guo, S. Wagner, V. Koch, V. Lupperger, B. Novotny, D. Murphree, H. Hardway, M. D'Amato, J. Lefkes, D. Geijs, A. Feuchtinger, A. Böhner, R. Kaczmarczyk, T. Biedermann, A. Amir, A. Mooyaart, F. Ciompi, G. Litjens, C. Wang, N. Comfere, K. Eyerich, S. Braun, C. Marr and T. Peng, "Generating dermatopathology reports from gigapixel whole slide images with HistoGPT",
Nature Communications,
2025;16.
D. van Midden, L. Studer, M. Hermsen, E. Steenbergen, J. Kers, N. Kozakowski, Z. Kikic, L. Hilbrands and J. van der Laak, "Deep learning-based histopathologic segmentation of peritubular capillaries in kidney transplant biopsies",
Computers in biology and medicine,
2025;193:110395.
M. Schuurmans, A. Saha, N. Alves, P. Vendittelli, D. Yakar, S. Sabroso-Lasa, N. Xue, N. Malats, H. Huisman, J. Hermans and G. Litjens, "End-to-end prognostication in pancreatic cancer by multimodal deep learning: a retrospective, multicenter study",
European Radiology,
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R. Volleberg, R. van der Waerden, T. Luttikholt, J. van der Zande, P. Cancian, X. Gu, J. Mol, S. Quax, M. Prokop, C. Sánchez, B. van Ginneken, I. Isgum, J. Thannhauser, S. Saitta, K. Nishimiya, T. Roleder and N. van Royen, "Comprehensive full-vessel segmentation and volumetric plaque quantification for intracoronary optical coherence tomography using deep learning",
European Heart Journal - Digital Health,
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Scientific Reports,
2025;15.
N. Rodrigues, J. de Almeida, A. Verde, A. Gaivão, C. Bireiro, I. Santiago, J. Ip, S. Belião, C. Matos, L. Vanneschi, M. Tsiknakis, K. Marias, D. Regge, S. Silva, T. Consortium, M. Tsiknakis, K. Marias, S. Sfakianakis, V. Kalokyri, E. Trivizakis, G. Kalliatakis, A. Dimitriadis, D. Fotiadis, N. Tachos, E. Mylona, D. Zaridis, C. Kalantzopoulos, N. Papanikolaou, J. de Almeida, A. Verde, A. Rodrigues, N. Rodrigues, M. Chambel, H. Huisman, M. Rooij, A. Saha, J. Twilt, J. Futterer, L. Martí-Bonmatí, L. Cerdá-Alberich, G. Ribas, S. Navarro, M. Marfil, E. Neri, G. Aringhieri, L. Tumminello, V. Mendola, D. Akata, M. Özmen, A. Karaosmanoglu, F. Atak, M. Karcaaltincaba, J. Vilanova, J. Usinskiene, R. Briediene, A. Untanas, K. Slidevska, K. Vasilis, G. Georgios, D. Koh, R. Emsley, S. Vit, A. Ribeiro, S. Doran, T. Jacobs, G. García-Martí, D. Regge, V. Giannini, S. Mazzetti, G. Cappello, G. Maimone, V. Napolitano, S. Colantonio, M. Pascali, E. Pachetti, G. Corso, D. Germanese, A. Berti, G. Carloni, J. Kalpathy-Cramer, C. Bridge, J. Correia, W. Hernandez, Z. Giavri, C. Pollalis, D. Agraniotis, A. Pastor, J. Mora, C. Saillant, T. Henne, R. Marquez and N. Papanikolaou, "Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector",
Scientific Reports,
2025;15.
B. Turkbey, H. Huisman, A. Fedorov, K. Macura, D. Margolis, V. Panebianco, A. Oto, I. Schoots, M. Siddiqui, C. Moore, O. Rouvière, L. Bittencourt, A. Padhani, C. Tempany and M. Haider, "Requirements for AI Development and Reporting for MRI Prostate Cancer Detection in Biopsy-Naive Men: PI-RADS Steering Committee, Version 1.0",
Radiology,
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T. Luttikholt, J. Thannhauser and N. van Royen, "Detection of large areas of thin-cap fibroatheroma in a recurrent STEMI patient using a novel artificial intelligence algorithm: moving from 2D to 3D",
European Heart Journal,
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A. Farris, J. van der Laak and D. van Midden, "Artificial intelligence-enhanced interpretation of kidney transplant biopsy: focus on rejection",
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W. Hendrix, N. Hendrix, E. Scholten, B. van Ginneken, M. Prokop, M. Rutten and C. Jacobs, "Artificial intelligence for the detection of airway nodules in chest CT scans",
European Radiology,
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C. Roest, T. Kwee, I. de Jong, I. Schoots, P. van Leeuwen, S. Heijmink, H. van der Poel, S. Fransen, A. Saha, H. Huisman and D. Yakar, "Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression",
Radiology: Imaging Cancer,
2025;7.
D. Peeters, K. Venkadesh, R. Dinnessen, Z. Saghir, E. Scholten, R. Vliegenthart, M. Prokop and C. Jacobs, "Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection",
Computers in Biology and Medicine,
2025;186:109633.
B. Obreja, J. Bosma, K. Venkadesh, Z. Saghir, M. Prokop and C. Jacobs, "Characterizing the Impact of Training Data on Generalizability: Application in Deep Learning to Estimate Lung Nodule Malignancy Risk",
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A. Frei, A. Khan, R. Oberson, S. Reinhard, Y. Banz, F. Meeuwsen, A. Janowczyk, R. Grobholz, H. Dawson, A. Lugli, M. Ilié, J. van der Laak and I. Zlobec, "Computer-aided tumor cell fraction (TCF) estimation by medical students, residents, and pathologists improves inter-observer agreement while highlighting the risk of automation bias",
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L. Cai, A. Pfob, R. Barr, V. Duda, Z. Alwafai, C. Balleyguier, D. Clevert, S. Fastner, C. Gomez, M. Goncalo, I. Gruber, M. Hahn, P. Kapetas, J. Nees, R. Ohlinger, F. Riedel, M. Rutten, A. Stieber, R. Togawa, C. Sidey-Gibbons, M. Tozaki, S. Wojcinski, J. Heil and M. Golatta, "Deep Learning Model for Breast Shear Wave Elastography to Improve Breast Cancer Diagnosis (INSPiRED 006): An International, Multicenter Analysis",
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R. van Herten, I. Lagogiannis, J. Wolterink, S. Bruns, E. Meulendijks, D. Dey, J. de Groot, J. Henriques, R. Planken, S. Saitta and I. Isgum, "World of Forms: Deformable geometric templates for one-shot surface meshing in coronary CT angiography",
Medical Image Analysis,
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H. Long, L. Hu, L. Wei, L. Dai, C. Fu, Y. Lu, C. Xu, Z. Hu, L. Wang, Z. Xu, R. Grimm, H. Busch, T. Benkert, A. Kamen, B. Lou, H. Huisman, A. Tong, T. Penzkofer, M. Choi, I. Shabunin, D. Winkel, P. Xing, D. Szolar, F. Coakley, S. Shea, E. Szurowska, W. Zhu and J. Zhao, "Improved prostate cancer diagnosis: upgraded prostate imaging reporting and data system (PI-RADS) scores by zoomed diffusion-weighted imaging enhance deep-learning-based computer-aided diagnosis accuracy",
Quantitative Imaging in Medicine and Surgery,
2025;15:2132-2145.
Q. Lohuizen, S. Fransen, G. Yiasemis, C. Roest, F. Simonis, T. Kwee, D. Yakar and H. Huisman, "Deep learning reconstruction for prostate MRI: Impact of field-of-view selection on global and regional image quality",
European Journal of Radiology Artificial Intelligence,
2025;2:100017.
N. Antonissen, K. Venkadesh, R. Dinnessen, E. Scholten, Z. Saghir, M. Silva, U. Pastorino, G. Sidorenkov, M. Heuvelmans, G. de Bock, F. Mohamed Hoesein, P. de Jong, H. Groen, R. Vliegenthart, H. Gietema, M. Prokop, C. Schaefer-Prokop, C. Jacobs, F. the consortium, J. Aerts, R. Cornelissen, R. Stadhouders, J. van Rooij, L. Trap, K. Nackaerts, W. de Wever, H. Gietema, M. Prokop, C. Jacobs, N. Antonissen, G. de Bock, M. Heuvelmans, G. Sidorenkov, D. Zhong, H. Groen, R. Vliegenthart, N. van der Velden, P. de Jong, F. Mohamed Hoesein, S. Bunk, G. Downward and R. Vermeulen, "External Test of a Deep Learning Algorithm for Pulmonary Nodule Malignancy Risk Stratification Using European Screening Data",
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A. Moradi, F. Zerka, J. Bosma, M. Sunoqrot, B. Abrahamsen, D. Yakar, J. Geerdink, H. Huisman, T. Bathen and M. Elschot, "Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study",
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M. Vitale, "Beyond ‘artificial intelligence’: against anthropomorphizing algorithmic systems for screening",
Tijdschrift voor Geneeskunde en Ethiek (TGE),
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T. Gootzen, A. Bouwmeester, J. de Hullu, J. Piek, J. van der Laak, M. Simons and M. Steenbeek, "Pathogenesis of peritoneal high-grade serous carcinoma after risk-reducing surgery: a systematic review",
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R. van den Elshout, J. Schoenmakers, A. Veltien, L. Boer, B. Küsters, G. Litjens, F. Meijer, A. van der Kolk, T. Scheenen, M. Wiesmann and D. Henssen, "Post-mortem 11.7 T DTI validation of myeloarchitectural changes in glioblastoma infiltration: Correlation with histology and PLI",
Brain Research Bulletin,
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M. Revel, J. Biederer, A. Nair, M. Silva, C. Jacobs, A. Snoeckx, M. Prokop, H. Prosch, A. Parkar, T. Frauenfelder and A. Larici, "ESR Essentials: lung cancer screening with low-dose CT--practice recommendations by the European Society of Thoracic Imaging",
European Radiology,
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M. Klontzas, K. Groot Lipman, T. D' Akinci Antonoli, A. Andreychenko, R. Cuocolo, M. Dietzel, S. Gitto, H. Huisman, J. Santinha, F. Vernuccio, J. Visser and M. Huisman, "ESR Essentials: common performance metrics in AI--practice recommendations by the European Society of Medical Imaging Informatics",
European Radiology,
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N. Billingy, C. Verberkt, I. Bahce, M. Hassing, J. Schoorlemmer, M. Sarioglu, S. Senan, E. Aarntzen, E. Comans, W. Kievit, S. Teerenstra, C. Jacobs, A. Keijser, M. Heuvel, A. Becker-Commissaris and I. Walraven, "Beneficial value of [18F]FDG PET/CT in the follow-up of patients with stage III non-small cell lung cancer (NVALT31-PET study): study protocol of a multicentre randomised controlled trial",
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W. Aswolinskiy, R. van der Post, M. Campora, C. Baronchelli, L. Ardighieri, S. Vatrano, J. van der Laak, E. Munari, M. Simons, I. Nagtegaal and F. Ciompi, "Attention-Based Whole-Slide Image Compression Achieves Pathologist-Level Prescreening of Multiorgan Routine Histopathology Biopsies",
Modern Pathology,
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C. Grisi, K. Kartasalo, M. Eklund, L. Egevad, J. van der Laak and G. Litjens, "Hierarchical Vision Transformers for prostate biopsy grading: Towards bridging the generalization gap",
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L. Thijssen, M. de Rooij and H. Huisman, "External validation of automated prostate MR T2-weighted image quality assessment on multi-centre multi-vendor data",
European Journal of Radiology Artificial Intelligence,
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I. Slootweg, N. García-De-La-Puente, G. Litjens and S. Dammak, "Self-supervised large-scale kidney abnormality detection in drug safety assessment studies",
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N. Rocholl, E. Smit, M. Prokop and A. Hering, "Unstable Prompts, Unreliable Segmentations: A Challenge for Longitudinal Lesion Analysis",
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R. Dinnessen, A. Antonissen, D. Peeters, H. Gietema, E. Scholten, C. Schaefer-Prokop and C. Jacobs, "Exploring AI-enabled nodule management for incidentally detected pulmonary nodules on CT.",
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European Congress of Radiology,
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