Publications

2024

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 abstract
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
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
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 abstract
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 abstract
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

ArXiv abstract
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

PubMed abstract
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

doi

2023

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
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
Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data
V. Corbetta, R. Beets-Tan, W. Silva, 2023

doi abstract
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
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 abstract
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 abstract
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 abstract
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 abstract
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
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 abstract
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 abstract
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 abstract
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 abstract
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 abstract
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
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 abstract
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

doi abstract
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

doi abstract

2022

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
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
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 abstract
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
Breast imaging and deep learning: past, present, and future

Advances in Magnetic Resonance Technology and Applications
S. Eskreis-Winkler, J. Teuwen, S. Benson, 2022

doi
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

doi abstract
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

doi abstract

2021

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 abstract
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
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
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 abstract
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 abstract
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

doi abstract