Publications

2024

  1. IMPORTANT-Net: Integrated MRI multi-parametric increment fusion generator with attention network for synthesizing absent data
    Information Fusion
    T. Zhang, T. Tan, L. Han, X. Wang, Y. Gao, J. Van Dijk, A. Portaluri, A. Gonzalez-Huete, A. D’Angelo, C. Lu, J. Teuwen, R. Beets-Tan, Y. Sun, R. Mann, 2024, 108
    Abstract
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  2. Overcoming data scarcity in radiomics/radiogenomics using synthetic radiomic features
    Computers in Biology and Medicine
    M. Ahmadian, Z. Bodalal, H. J. Van Der Hulst, C. Vens, L. Karssemakers, N. Bogveradze, F. Castagnoli, F. Landolfi, E. K. Hong, N. Gennaro, A. D. Pizzi, R. G. Beets-Tan, M. W. Van Den Brekel, J. A. Castelijns, 2024
    Abstract
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  3. Reproducing RECIST lesion selection via machine learning: Insights into intra and inter-radiologist variation
    European Journal of Radiology Open
    T. M. Tareco Bucho, L. Petrychenko, M. A. Abdelatty, N. Bogveradze, Z. Bodalal, R. G. Beets-Tan, S. Trebeschi, 2024, 12
    Abstract
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  4. Multi-sequence MRI radiomics of colorectal liver metastases: Which features are reproducible across readers?
    European Journal of Radiology
    D. J. Van Der Reijd, K. Chupetlovska, E. Van Dijk, B. Westerink, M. A. Monraats, J. J. Van Griethuysen, D. M. Lambregts, R. Tissier, R. G. Beets-Tan, S. Benson, M. Maas, 2024, 172
    Abstract
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  5. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII
    Insights into Imaging
    B. Kocak, T. Akinci D’Antonoli, N. Mercaldo, A. Alberich-Bayarri, B. Baessler, I. Ambrosini, A. E. Andreychenko, S. Bakas, R. G. H. Beets-Tan, K. Bressem, I. Buvat, R. Cannella, L. A. Cappellini, A. U. Cavallo, L. L. Chepelev, L. C. H. Chu, A. Demircioglu, N. M. Desouza, M. Dietzel, S. C. Fanni, A. Fedorov, L. S. Fournier, V. Giannini, R. Girometti, K. B. W. Groot Lipman, G. Kalarakis, B. S. Kelly, M. E. Klontzas, D. Koh, E. Kotter, H. Y. Lee, M. Maas, L. Marti-Bonmati, H. Müller, N. Obuchowski, F. Orlhac, N. Papanikolaou, E. Petrash, E. Pfaehler, D. Pinto Dos Santos, A. Ponsiglione, S. Sabater, F. Sardanelli, P. Seeböck, N. M. Sijtsema, A. Stanzione, A. Traverso, L. Ugga, M. Vallières, L. V. Van Dijk, J. J. M. Van Griethuysen, R. W. Van Hamersvelt, P. Van Ooijen, F. Vernuccio, A. Wang, S. Williams, J. Witowski, Z. Zhang, A. Zwanenburg, R. Cuocolo, 2024, 15;(1)
    Abstract
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  6. An automated deep learning pipeline for EMVI classification and response prediction of rectal cancer using baseline MRI: a multi-centre study
    npj Precision Oncology
    L. Cai, D. M. J. Lambregts, G. L. Beets, M. Mass, E. H. P. Pooch, C. Guérendel, R. G. H. Beets-Tan, S. Benson, 2024, 8;(1)
    Abstract
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  7. Can blood-based markers predict RECIST progression in non-small cell lung cancer treated with immunotherapy?
    Journal of Cancer Research and Clinical Oncology
    M. Yeghaian, T. M. Tareco Bucho, M. De Bruin, A. Schmitz, Z. Bodalal, E. F. Smit, R. G. H. Beets-Tan, D. Van Den Broek, S. Trebeschi, 2024, 150;(6)
    Abstract
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  8. To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
    L. Han, T. Tan, T. Zhang, Y. Gao, X. Wang, V. Longo, S. Ventura-Díaz, A. D'Angelo, J. Teuwen, R. Mann, 2024
    Abstract
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  9. AI Applications to Breast MRI: Today and Tomorrow.
    Journal of magnetic resonance imaging: JMRI
    R. Lo Gullo, J. Brunekreef, E. Marcus, L. K. Han, S. Eskreis-Winkler, S. B. Thakur, R. Mann, K. Groot Lipman, J. Teuwen, K. Pinker, 2024
    Abstract
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  10. Large retrorectal spindle cell sarcoma: A case report and brief review of the literature
    Radiology Case Reports
    C. L. Petersen, M. R. Byriel, J. Shkurti, S. R. Rafaelsen, 2024, 19;(7):2684-2688
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2023

  1. Predicting up to 10 year breast cancer risk using longitudinal mammographic screening history
    X. Wang, T. Tan, Y. Gao, R. Su, T. Zhang, L. Han, J. Teuwen, A. D’Angelo, C. A. Drukker, M. K. Schmidt, R. Beets-Tan, N. Karssemeijer, R. Mann, 2023
    Abstract
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  2. Synthesis of Contrast-Enhanced Breast MRI Using T1- and Multi-b-Value DWI-Based Hierarchical Fusion Network with Attention Mechanism
    Lecture Notes in Computer Science
    T. Zhang, L. Han, A. D’Angelo, X. Wang, Y. Gao, C. Lu, J. Teuwen, R. Beets-Tan, T. Tan, R. Mann, 2023
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  3. Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data
    V. Corbetta, R. Beets-Tan, W. Silva, 2023
    Abstract
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  4. Attention-Based Regularisation for Improved Generalisability in Medical Multi-Centre Data
    2023 International Conference on Machine Learning and Applications (ICMLA)
    D. Silva, G. Agrotis, R. Beets-Tan, L. F. Teixeira, W. Silva, 2023
    Abstract
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  5. How to 19F MRI: applications, technique, and getting started
    BJR|Open
    O. Maxouri, Z. Bodalal, M. Daal, S. Rostami, I. Rodriguez, L. Akkari, M. Srinivas, R. Bernards, R. Beets-Tan, 2023, 5;(1)
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  6. RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease
    Cell Reports Medicine
    T. Zhang, T. Tan, X. Wang, Y. Gao, L. Han, L. Balkenende, A. D’Angelo, L. Bao, H. M. Horlings, J. Teuwen, R. G. Beets-Tan, R. M. Mann, 2023
    Abstract
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  7. Artificial Intelligence–based Quantification of Pleural Plaque Volume and Association With Lung Function in Asbestos-exposed Patients
    Journal of Thoracic Imaging
    K. B. Groot Lipman, T. N. Boellaard, C. J. De Gooijer, N. Bogveradze, E. K. Hong, F. Landolfi, F. Castagnoli, N. Vakhidova, I. Smesseim, F. Van Der Heijden, R. G. Beets-Tan, R. Wittenberg, Z. Bodalal, J. A. Burgers, S. Trebeschi, 2023
    Abstract
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  8. How Does Target Lesion Selection Affect RECIST? A Computer Simulation Study
    Investigative Radiology
    T. M. Tareco Bucho, R. L. Tissier, K. B. Groot Lipman, Z. Bodalal, A. Delli Pizzi, T. D. L. Nguyen-Kim, R. G. Beets-Tan, S. Trebeschi, 2023
    Abstract
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  9. Diagnostic accuracy of CT for local staging of colon cancer: A nationwide study in the Netherlands
    European Journal of Cancer
    J. Shkurti, K. Van Den Berg, F. N. Van Erning, M. J. Lahaye, R. G. Beets-Tan, J. Nederend, 2023, 193
    Abstract
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  10. Predicting breast cancer types on and beyond molecular level in a multi-modal fashion
    npj Breast Cancer
    T. Zhang, T. Tan, L. Han, L. Appelman, J. Veltman, R. Wessels, K. M. Duvivier, C. Loo, Y. Gao, X. Wang, H. M. Horlings, R. G. H. Beets-Tan, R. M. Mann, 2023, 9;(1)
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  11. Is the generalizability of a developed artificial intelligence algorithm for COVID-19 on chest CT sufficient for clinical use? Results from the International Consortium for COVID-19 Imaging AI (ICOVAI)
    European Radiology
    L. Topff, K. B. W. Groot Lipman, F. Guffens, R. Wittenberg, A. Bartels-Rutten, G. Van Veenendaal, M. Hess, K. Lamerigts, J. Wakkie, E. Ranschaert, S. Trebeschi, J. J. Visser, R. G. H. Beets-Tan, J. Guiot, A. Snoeckx, P. Kint, L. Van Hoe, C. C. Quattrocchi, D. Dieckens, S. Lounis, E. Schulze, A. E. Sjer, N. Van Vucht, J. A. Tielbeek, F. Raat, D. Eijspaart, A. Abbas, 2023, 33;(6):4249-4258
    Abstract
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  12. Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastases
    Insights into Imaging
    Z. Bodalal, N. Bogveradze, L. C. Ter Beek, J. G. Van Den Berg, J. Sanders, I. Hofland, S. Trebeschi, K. B. W. Groot Lipman, K. Storck, E. K. Hong, N. Lebedyeva, M. Maas, R. G. H. Beets-Tan, F. M. Gomez, I. Kurilova, 2023, 14;(1)
    Abstract
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  13. Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablation
    European Radiology
    D. J. Van Der Reijd, C. Guerendel, F. C. R. Staal, M. P. Busard, M. De Oliveira Taveira, E. G. Klompenhouwer, K. F. D. Kuhlmann, A. Moelker, C. Verhoef, M. P. A. Starmans, D. M. J. Lambregts, R. G. H. Beets-Tan, S. Benson, M. Maas, 2023
    Abstract
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  14. A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph Nodes
    Diagnostics
    T. C. Sluckin, M. Hekhuis, S. Q. Kol, J. Nederend, K. Horsthuis, R. G. H. Beets-Tan, G. L. Beets, J. W. A. Burger, J. B. Tuynman, H. J. T. Rutten, M. Kusters, S. Benson, 2023, 13;(19):3099
    Abstract
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  15. Sense and non-sense of imaging in the era of organ preservation for rectal cancer
    The British Journal of Radiology
    X. Ou, D. J. Van Der Reijd, D. M. Lambregts, B. A. Grotenhuis, B. Van Triest, G. L. Beets, R. G. Beets-Tan, M. Maas, 2023, 96;(1151)
    Abstract
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  16. The diagnostic accuracy of local staging in colon cancer based on computed tomography (CT): evaluating the role of extramural venous invasion and tumour deposits
    Abdominal Radiology
    K. Van Den Berg, S. Wang, J. M. W. E. Willems, G. J. Creemers, J. M. L. Roodhart, J. Shkurti, J. W. A. Burger, H. J. T. Rutten, R. G. H. Beets-Tan, J. Nederend, 2023, 49;(2):365-374
    Abstract
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  17. Whole‐body MRI with diffusion‐weighted imaging as an adjunct to18F‐fluorodeoxyglucose positron emission tomography and CT in patients with suspected recurrent colorectal cancer
    Colorectal Disease
    J. R. J. Willemse, M. J. Lahaye, N. F. M. Kok, B. A. Grotenhuis, A. G. J. Aalbers, G. L. Beets, C. Rijsemus, M. Maas, L. W. Van Golen, R. G. H. Beets‐Tan, D. M. J. Lambregts, 2023, 26;(2):290-299
    Abstract
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  18. CIRSE Position Paper on Artificial Intelligence in Interventional Radiology
    CardioVascular and Interventional Radiology
    A. Najafi, R. L. Cazzato, B. C. Meyer, P. L. Pereira, A. Alberich, A. López, M. Ronot, J. Fritz, M. Maas, S. Benson, P. Haage, F. Gomez Munoz, 2023, 46;(10):1303-1307
    Abstract
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  19. Visualize what you learn: a well-explainable joint-learning framework based on multi-view mammograms and associated reports
    Y. Gao, H. Zhou, X. Wang, T. Zhang, R. Tan, L. Han, L. Estacio, A. D’Angelo, J. Teuwen, R. Mann, T. Tan, 2023
    Abstract
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  20. Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI
    Medical Image Analysis
    L. Han, T. Tan, T. Zhang, Y. Huang, X. Wang, Y. Gao, J. Teuwen, R. Mann, 2023
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2022

  1. Imaging of colorectal nodal disease
    The Lymphatic System in Colorectal Cancer
    L. Cai, Z. Bodalal, S. Trebeschi, S. Waktola, T. C. Sluckin, M. Kusters, M. Maas, R. Beets-Tan, S. Benson, 2022
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  2. CNN-based tumor progression prediction after thermal ablation with CT imaging
    Medical Imaging 2022: Computer-Aided Diagnosis
    M. Taghavi, M. Maas, F. Staal, R. Beets-Tan, S. Benson, 2022
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  3. Federated learning enables big data for rare cancer boundary detection.
    Nature communications
    S. Pati, U. Baid, B. Edwards, M. Sheller, S. Wang, G. A. Reina, P. Foley, A. Gruzdev, D. Karkada, C. Davatzikos, C. Sako, S. Ghodasara, M. Bilello, S. Mohan, P. Vollmuth, G. Brugnara, C. J. Preetha, F. Sahm, K. Maier-Hein, M. Zenk, M. Bendszus, W. Wick, E. Calabrese, J. Rudie, J. Villanueva-Meyer, S. Cha, M. Ingalhalikar, M. Jadhav, U. Pandey, J. Saini, J. Garrett, M. Larson, R. Jeraj, S. Currie, R. Frood, K. Fatania, R. Y. Huang, K. Chang, C. Balaña, J. Capellades, J. Puig, J. Trenkler, J. Pichler, G. Necker, A. Haunschmidt, S. Meckel, G. Shukla, S. Liem, G. S. Alexander, J. Lombardo, J. D. Palmer, A. E. Flanders, A. P. Dicker, H. I. Sair, C. K. Jones, A. Venkataraman, M. Jiang, T. Y. So, C. Chen, P. A. Heng, Q. Dou, M. Kozubek, F. Lux, J. Michálek, P. Matula, M. Keřkovský, T. Kopřivová, M. Dostál, V. Vybíhal, M. A. Vogelbaum, J. R. Mitchell, J. Farinhas, J. A. Maldjian, C. G. B. Yogananda, M. C. Pinho, D. Reddy, J. Holcomb, B. C. Wagner, B. M. Ellingson, T. F. Cloughesy, C. Raymond, T. Oughourlian, A. Hagiwara, C. Wang, M. To, S. Bhardwaj, C. Chong, M. Agzarian, A. X. Falcão, S. B. Martins, B. C. A. Teixeira, F. Sprenger, D. Menotti, D. R. Lucio, P. Lamontagne, D. Marcus, B. Wiestler, F. Kofler, I. Ezhov, M. Metz, R. Jain, M. Lee, Y. W. Lui, R. Mckinley, J. Slotboom, P. Radojewski, R. Meier, R. Wiest, D. Murcia, E. Fu, R. Haas, J. Thompson, D. R. Ormond, C. Badve, A. E. Sloan, V. Vadmal, K. Waite, R. R. Colen, L. Pei, M. Ak, A. Srinivasan, J. R. Bapuraj, A. Rao, N. Wang, O. Yoshiaki, T. Moritani, S. Turk, J. Lee, S. Prabhudesai, F. Morón, J. Mandel, K. Kamnitsas, B. Glocker, L. V. M. Dixon, M. Williams, P. Zampakis, V. Panagiotopoulos, P. Tsiganos, S. Alexiou, I. Haliassos, E. I. Zacharaki, K. Moustakas, C. Kalogeropoulou, D. M. Kardamakis, Y. S. Choi, S. Lee, J. H. Chang, S. S. Ahn, B. Luo, L. Poisson, N. Wen, P. Tiwari, R. Verma, R. Bareja, I. Yadav, J. Chen, N. Kumar, M. Smits, S. R. Van Der Voort, A. Alafandi, F. Incekara, M. M. J. Wijnenga, G. Kapsas, R. Gahrmann, J. W. Schouten, H. J. Dubbink, A. J. P. E. Vincent, M. J. Van Den Bent, P. J. French, S. Klein, Y. Yuan, S. Sharma, T. Tseng, S. Adabi, S. P. Niclou, O. Keunen, A. Hau, M. Vallières, D. Fortin, M. Lepage, B. Landman, K. Ramadass, K. Xu, S. Chotai, L. B. Chambless, A. Mistry, R. C. Thompson, Y. Gusev, K. Bhuvaneshwar, A. Sayah, C. Bencheqroun, A. Belouali, S. Madhavan, T. C. Booth, A. Chelliah, M. Modat, H. Shuaib, C. Dragos, A. Abayazeed, K. Kolodziej, M. Hill, A. Abbassy, S. Gamal, M. Mekhaimar, M. Qayati, M. Reyes, J. E. Park, J. Yun, H. S. Kim, A. Mahajan, M. Muzi, S. Benson, R. G. H. Beets-Tan, J. Teuwen, A. Herrera-Trujillo, M. Trujillo, W. Escobar, A. Abello, J. Bernal, J. Gómez, J. Choi, S. Baek, Y. Kim, H. Ismael, B. Allen, J. M. Buatti, A. Kotrotsou, H. Li, T. Weiss, M. Weller, A. Bink, B. Pouymayou, H. F. Shaykh, J. Saltz, P. Prasanna, S. Shrestha, K. M. Mani, D. Payne, T. Kurc, E. Pelaez, H. Franco-Maldonado, F. Loayza, S. Quevedo, P. Guevara, E. Torche, C. Mendoza, F. Vera, E. Ríos, E. López, S. A. Velastin, G. Ogbole, M. Soneye, D. Oyekunle, O. Odafe-Oyibotha, B. Osobu, M. Shu'Aibu, A. Dorcas, F. Dako, A. L. Simpson, M. Hamghalam, J. J. Peoples, R. Hu, A. Tran, D. Cutler, F. Y. Moraes, M. A. Boss, J. Gimpel, D. K. Veettil, K. Schmidt, B. Bialecki, S. Marella, C. Price, L. Cimino, C. Apgar, P. Shah, B. Menze, J. S. Barnholtz-Sloan, J. Martin, S. Bakas, 2022, 13;(1):7346
    Abstract
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  4. Artificial intelligence-based diagnosis of asbestosis: analysis of a database with applicants for asbestosis state aid
    European Radiology
    K. B. W. Groot Lipman, C. J. De Gooijer, T. N. Boellaard, F. Van Der Heijden, R. G. H. Beets-Tan, Z. Bodalal, S. Trebeschi, J. A. Burgers, 2022, 33;(5):3557-3565
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  5. The Future of Artificial Intelligence Applied to Immunotherapy Trials
    Neoadjuvant Immunotherapy Treatment of Localized Genitourinary Cancers
    Z. Bodalal, S. Trebeschi, I. Wamelink, K. G. Lipman, T. Bucho, N. Van Dijk, T. Boellaard, S. Waktola, R. G. H. Beets-Tan, 2022
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  6. Breast imaging and deep learning: past, present, and future
    Advances in Magnetic Resonance Technology and Applications
    S. Eskreis-Winkler, J. Teuwen, S. Benson, 2022
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  7. Breast imaging and deep learning: past, present, and future
    Advances in Magnetic Resonance Technology and Applications
    S. Eskreis-Winkler, J. Teuwen, S. Benson, 2022
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  8. Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative
    European Radiology
    A. Ponsiglione, A. Stanzione, G. Spadarella, A. Baran, L. A. Cappellini, K. G. Lipman, P. Van Ooijen, R. Cuocolo, 2022, 33;(3):2239-2247
    Abstract
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  9. Intracerebral Hemorrhage Segmentation on Noncontrast Computed Tomography Using a Masked Loss Function U-Net Approach
    Journal of Computer Assisted Tomography
    N. A. Coorens, K. G. Lipman, S. P. Krishnam, C. O. Tan, L. Alic, R. Gupta, 2022, 47;(1):93-101
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2021

  1. An improved automatic system for aiding the detection of colon polyps using deep learning
    2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
    L. Cai, R. Beets-Tan, S. Benson, 2021
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  2. Artificial intelligence-mediated diagnosis of asbestosis
    ILD / DPLD of known origin
    K. B. W. G. Lipman, C. J. De Gooijer, T. N. Boellaard, F. Van Der Heijden, R. G. H. Beets-Tan, Z. Bodalal, S. Trebeschi, S. Burgers, 2021
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  3. Pleural plaque volume correlation to lung function and artificial intelligence-driven pleural plaque quantification
    ILD / DPLD of known origin
    K. B. W. G. Lipman, T. N. Boellaard, C. J. De Gooijer, N. Bogveradze, E. K. Hong, F. Landolfi, F. Castagnoli, L. C. Cavallo, N. Lebedyeva, F. Van Der Heijden, R. G. H. Beets-Tan, Z. Bodalal, S. Burgers, S. Trebeschi, 2021
    Abstract
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  4. Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy
    Frontiers in Oncology
    S. Trebeschi, Z. Bodalal, T. N. Boellaard, T. M. Tareco Bucho, S. G. Drago, I. Kurilova, A. M. Calin-Vainak, A. Delli Pizzi, M. Muller, K. Hummelink, K. J. Hartemink, T. D. L. Nguyen-Kim, E. F. Smit, H. J. W. L. Aerts, R. G. H. Beets-Tan, 2021, 11
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  5. Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy
    Frontiers in Oncology
    S. Trebeschi, Z. Bodalal, N. Van Dijk, T. N. Boellaard, P. Apfaltrer, T. M. Tareco Bucho, T. D. L. Nguyen-Kim, M. S. Van Der Heijden, H. J. W. L. Aerts, R. G. H. Beets-Tan, 2021, 11
    Abstract
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  6. The use of deep learning on endoscopic images to assess the response of rectal cancer after chemoradiation
    Surgical Endoscopy
    H. E. Haak, X. Gao, M. Maas, S. Waktola, S. Benson, R. G. H. Beets-Tan, G. L. Beets, M. Van Leerdam, J. Melenhorst, 2021, 36;(5):3592-3600
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  7. Novel Breast Specimen Orientation Approach through 3D Visualizations for Relocating Inadequate Margins based on the Surgical Clips: Feasibility Study.
    R. F. V. Doremalen, K. B. G. Lipman, E. V. '. Riet, H. Torrenga, M. M. Smits, F. V. D. Heijden, 2021
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