Publications

Regina Beets-Tan

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
    doi preprints paper (open access)
    Abstract
  2. Enhancing Cross-Modal Medical Image Segmentation through Compositionality
    A. Eijpe, V. Corbetta, K. Chupetlovska, R. Beets-Tan, W. Silva, 2024
    doi
    Abstract
  3. FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation
    P. Schutte, V. Corbetta, R. Beets-Tan, W. Silva, 2024
    doi
    Abstract
  4. Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms
    Lecture Notes in Computer Science
    X. Wang, T. Tan, Y. Gao, E. Marcus, L. Han, A. Portaluri, T. Zhang, C. Lu, X. Liang, R. Beets-Tan, J. Teuwen, R. Mann, 2024
    doi
    Abstract
  5. Improving Neoadjuvant Therapy Response Prediction by Integrating Longitudinal Mammogram Generation with Cross-Modal Radiological Reports: A Vision-Language Alignment-Guided Model
    Lecture Notes in Computer Science
    Y. Gao, H. Zhou, X. Wang, T. Zhang, L. Han, C. Lu, X. Liang, J. Teuwen, R. Beets-Tan, T. Tan, R. Mann, 2024
    doi
    Abstract
  6. 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
    doi
    Abstract
  7. 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
    doi paper (open access)
    Abstract
  8. 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
    doi paper (open access)
    Abstract
  9. Improving Rectal Tumor Segmentation with Anomaly Fusion Derived from Anatomical Inpainting: A Multicenter Study
    L. Cai, M. A. Abdelatty, L. Han, D. Lambregts, J. Van Griethuysen, E. Pooch, R. G. Beets-Tan, S. Benson, J. Brunekreef, J. Teuwen, 2024
    doi
    Abstract
  10. 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)
    doi paper (open access)
    Abstract
  11. 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)
    doi paper (open access)
    Abstract
  12. 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)
    doi paper (open access)
    Abstract
  13. Diagnostic performance of ADC and ADCratio in MRI-based prostate cancer assessment: A systematic review and meta-analysis
    European Radiology
    G. Agrotis, E. Pooch, M. Abdelatty, S. Benson, A. Vassiou, M. Vlychou, R. G. H. Beets-Tan, I. G. Schoots, 2024
    doi
    Abstract

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
    doi
    Abstract
  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
    doi
    Abstract
  3. Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data
    V. Corbetta, R. Beets-Tan, W. Silva, 2023
    doi
    Abstract
  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
    doi
    Abstract
  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)
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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)
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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)
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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)
    doi paper (open access)
    Abstract
  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
    doi
    Abstract
  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
    doi paper (open access)
    Abstract

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
    doi
    Abstract
  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
    doi
    Abstract
  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
    url
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi
    Abstract

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
    doi paper (open access)
    Abstract
  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
    doi
    Abstract
  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
    doi
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract