A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy
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BACKGROUND AND PURPOSE: Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap. METHODS AND MATERIALS: A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended. RESULTS: The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated. CONCLUSION: A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.
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Current acceptance testing of AI auto contouring algorithms relies on area- and distance-based metrics that measure overall contour agreement but say nothing about where the model is uncertain.
“Acceptance testing of AI auto contouring algorithms typically involves both area‐based metrics (e.g., Dice Similarity Coefficient, Intersection over Union (IoU)) and distance‐based metrics (e.g., Hausdorff Distance) evaluated against reference standard contours. 11 , 12 , 13 , 14 These metrics are well established for assessing contour agreement, but they say nothing about where the model is confi”
From Uncertainty quantification of U‐Net based segmentation tool using conformal prediction (2026) · cites this paper as [11]
In the Results section on volume segmentation (page unavailable), the guideline recommends reporting Dice Similarity Coefficient and Hausdorff Distance against a ground-truth contour, and says simple geometric validation is insufficient to predict clinical usefulness. It does not state that these metrics reveal nothing about where an auto-contouring model is uncertain; its discussion of model uncertainty concerns dose prediction, not segmentation. (LLM, conf=0.84)
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. (2000) · Statistics for Engineering and Information Science · doi:10.1007/978-1-4757-3264-1 · Cited by 46,387 · see citing works
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine · doi:10.7326/m18-1376 · Cited by 2,984 · see citing works
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers
Mongan, J., Moy, L., & Kahn, C. E. (2020) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2020200029 · Cited by 1,443 · see citing works
Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01772-9 · Cited by 1,039 · see citing works
Self-citationCollins, G. S., Dhiman, P., Andaur Navarro, C. L., Ma, J., Hooft, L., Reitsma, J. B., Logullo, P., Beam, A. L., Peng, L., Van Calster, B., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-048008 · Cited by 852 · see citing works
Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Darzi, A., Holmes, C., Yau, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1037-7 · Cited by 748 · see citing works
Reporting of artificial intelligence prediction models
Collins, G. S., & Moons, K. G. M. (2019) · The Lancet · doi:10.1016/s0140-6736(19)30037-6 · Cited by 701 · see citing works
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist.
Norgeot, B., Quer, G., Beaulieu-Jones, B. K., Torkamani, A., Dias, R., Gianfrancesco, M., Arnaout, R., Kohane, I. S., Saria, S., Topol, E., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1041-y · Cited by 632 · see citing works
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., Ashrafian, H., Beam, A. L., Chan, A.-W., Collins, G. S., Deeks, A. D. J., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30218-1 · Cited by 555 · see citing works
Vollmer, S. J., Mateen, B. A., Bohner, G., Király, F. J., Ghani, R., Jonsson, P. F., Cumbers, S., Jonas, A., McAllister, K., Myles, P. R., et al. (2020) · BMJ · doi:10.1136/bmj.l6927 · Cited by 554 · see citing works
Langendijk, J. A., Lambin, P., Ruysscher, D. D., Widder, J., Bos, M., & Verheij, M. (2013) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2013.05.007 · Cited by 553 · see citing works
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Annals of Internal Medicine · doi:10.7326/l15-5093-2 · Cited by 493 · see citing works
(1996) · Journal of Clinical Oncology · doi:10.1200/jco.1996.14.2.671 · Cited by 430 · see citing works
Hernandez-Boussard, T., Bozkurt, S., Ioannidis, J. P. A., & Shah, N. H. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa088 · Cited by 410 · see citing works
Rapid-Learning System for Cancer Care
Abernethy, A. P., Etheredge, L. M., Ganz, P. A., Wallace, P., German, R. R., Neti, C., Bach, P. B., & Murphy, S. B. (2010) · Journal of Clinical Oncology · doi:10.1200/jco.2010.28.5478 · Cited by 366 · see citing works
Vandewinckele, L., Claessens, M., Dinkla, A. M., Brouwer, C. L., Crijns, W., Verellen, D., & van Elmpt, W. (2020) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2020.09.008 · Cited by 326 · see citing works
MR fingerprinting Deep RecOnstruction NEtwork (DRONE)
Cohen, O., Zhu, B., & Rosen, M. S. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27198 · Cited by 313 · see citing works
Human–machine partnership with artificial intelligence for chest radiograph diagnosis
Patel, B. N., Rosenberg, L., Willcox, G., Baltaxe, D., Lyons, M., Irvin, J., Rajpurkar, P., Amrhein, T., Gupta, R., Halabi, S., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0189-7 · Cited by 254 · see citing works
ESTRO-EANO guideline on target delineation and radiotherapy details for glioblastoma
Niyazi, M., Andratschke, N. H., Bendszus, M., Chalmers, A. J., Erridge, S., Galldiks, N., Lagerwaard, F. J., Navarria, P., af Rosenschöld, P. M., Ricardi, U., et al. (2023) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2023.109663 · Cited by 237 · see citing works
Mayo, C. S., Moran, J. M., Bosch, W. R., Xiao, Y., McNutt, T., Popple, R. A., Michalski, J. M., Feng, M. U.-S., Marks, L. B., Fuller, C. D., et al. (2017) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2017.12.013 · Cited by 213 · see citing works
Self-citationHernández, V., Hansen, C. R., Widesott, L., Bäck, A., Canters, R. A. M., Fusella, M., Götstedt, J., Jurado‐Bruggeman, D., Mukumoto, N., Kaplan, L. P., et al. (2020) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2020.09.038 · Cited by 210 · see citing works
Deep learning in head & neck cancer outcome prediction
Diamant, A., Chatterjee, A., Vallières, M., Shenouda, G., & Seuntjens, J. P. (2019) · Scientific Reports · doi:10.1038/s41598-019-39206-1 · Cited by 202 · see citing works
Self-citationMcIntosh, C., Conroy, L., Tjong, M. C., Craig, T., Bayley, A., Catton, C., Gospodarowicz, M. K., Helou, J. A., Isfahanian, N., Kong, V., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01359-w · Cited by 184 · see citing works
4π Non-Coplanar Liver SBRT: A Novel Delivery Technique
Dong, P., Lee, P. P., Ruan, D., Long, T., Romeijn, H. E., Yang, Y., Low, D. A., Kupelian, P. A., & Sheng, K. (2012) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2012.09.028 · Cited by 165 · see citing works
Ahmed, K. A., Caudell, J. J., El-Haddad, G. E., Berglund, A., Welsh, E. A., Yue, B., Hoffe, S. E., Naghavi, A. O., Abuodeh, Y. A., Frakes, J. M., et al. (2016) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2016.03.050 · Cited by 162 · see citing works
Introduction to machine and deep learning for medical physicists
Cui, S., Tseng, H., Pakela, J. M., Haken, R. K. T., & Naqa, I. E. (2020) · Medical Physics · doi:10.1002/mp.14140 · Cited by 131 · see citing works
A review of explainable and interpretable AI with applications in COVID‐19 imaging
Fuhrman, J. D., Gorre, N., Hu, Q., Li, H., Naqa, I. E., & Giger, M. L. (2021) · Medical Physics · doi:10.1002/mp.15359 · Cited by 120 · see citing works
Luo, Y., Tseng, H.-H., Cui, S., Wei, L., Haken, R. K. T., & Naqa, I. E. (2019) · BJR|Open · doi:10.1259/bjro.20190021 · Cited by 118 · see citing works
Automated Contouring and Planning in Radiation Therapy: What Is ‘Clinically Acceptable’?
Baroudi, H., Brock, K. K., Cao, W., Chen, X., Chung, C. D., Court, L. E., Basha, M. D. E., Farhat, M., Gay, S., Gronberg, M., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13040667 · Cited by 112 · see citing works
Self-citationRequirements and reliability of AI in the medical context
Balagurunathan, Y., Mitchell, R., & Naqa, I. E. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.024 · Cited by 91 · see citing works
Rusanov, B., Hassan, G. M., Reynolds, M. A., Sabet, M., Kendrick, J., Rowshanfarzad, P., & Ebert, M. A. (2022) · Medical Physics · doi:10.1002/mp.15840 · Cited by 89 · see citing works
AI and machine learning ethics, law, diversity, and global impact
Drabiak, K., Kyzer, S., Nemov, V., & El Naqa, I. (2023) · The British Journal of Radiology · doi:10.1259/bjr.20220934 · Cited by 84 · see citing works
A machine learning approach to the accurate prediction of multi-leaf collimator positional errors
Carlson, J., Park, J. M., Park, S. Y., Park, J. M., Choi, Y., & Ye, S. (2016) · Physics in Medicine and Biology · doi:10.1088/0031-9155/61/6/2514 · Cited by 83 · see citing works
Hosny, A., Bitterman, D. S., Guthier, C. V., Qian, J. M., Roberts, H. J., Perni, S., Saraf, A., Peng, L. C., Pashtan, I. M., Ye, Z., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00129-7 · Cited by 74 · see citing works
Li, X., Zhang, J., Sheng, Y., Chang, Y., Yin, F.-F., Ge, Y., Wu, Q., & Wang, C. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aba5eb · Cited by 68 · see citing works
Scott, J. G., Sedor, G., Scarborough, J. A., Kattan, M. W., Peacock, J. S., Grass, G. D., Mellon, E. A., Thapa, R., Schell, M. J., Waller, A. F., et al. (2020) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2020.11.008 · Cited by 63 · see citing works
Deep learning in magnetic resonance image reconstruction
Chandra, S. S., Lorenzana, M. B., Liu, X., Liu, S., Bollmann, S., & Crozier, . (2021) · Journal of Medical Imaging and Radiation Oncology · doi:10.1111/1754-9485.13276 · Cited by 60 · see citing works
Wootton, L. S., Nyflot, M. J., Chaovalitwongse, W. A., & Ford, E. (2018) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2018.05.033 · Cited by 59 · see citing works
Applications of machine and deep learning to patient‐specific IMRT/VMAT quality assurance
Osman, A. F. I., & Maalej, N. M. (2021) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.13375 · Cited by 59 · see citing works
Artificial Intelligence for Optimization and Interpretation of PET/CT and PET/MR Images
Zaharchuk, G., & Davidzon, G. (2020) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2020.10.001 · Cited by 57 · see citing works
Kawula, M., Hadi, I., Nierer, L., Vagni, M., Cusumano, D., Boldrini, L., Placidi, L., Corradini, S., Belka, C., Landry, G., et al. (2022) · Medical Physics · doi:10.1002/mp.16056 · Cited by 55 · see citing works
Self-citationNguyen, D. T. D., Barkousaraie, A. S., Bohara, G., Balagopal, A., McBeth, R., Lin, M., & Jiang, S. B. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abe04f · Cited by 54 · see citing works
Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects
Lombardo, E., Dhont, J., Page, D., Garibaldi, C., Künzel, L. A., Hurkmans, C., Tijssen, R. H. N., Paganelli, C., Liu, P. Z. Y., Keall, P., et al. (2023) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2023.109970 · Cited by 52 · see citing works
Self-citationKiser, K. J., Barman, A., Stieb, S., Fuller, C. D., & Giancardo, L. (2021) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00460-3 · Cited by 50 · see citing works
Brouwer, C. L., Djamal Boukerroui, A., Oliveira, J., Looney, P., Steenbakkers, R. J. H. M., Langendijk, J. A., Both, S., & Gooding, M. J. (2020) · Physics and Imaging in Radiation Oncology · doi:10.1016/j.phro.2020.10.001 · Cited by 50 · see citing works
Advances in Automated Treatment Planning
Nguyen, D. T. D., Lin, M., Sher, D. J., Lu, W., Jia, X., & Jiang, S. B. (2022) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2022.06.004 · Cited by 49 · see citing works
Brady, A. P., Allen, B., Chong, J., Kotter, E., Kottler, N. E., Mongan, J., Oakden‐Rayner, L., dos Santos, D. P., Tang, A., Wald, C., et al. (2024) · Journal of Medical Imaging and Radiation Oncology · doi:10.1111/1754-9485.13612 · Cited by 47 · see citing works
Patient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy
Li, Z., Zhang, W., Li, B., Zhu, J., Peng, Y., Li, C., Zhu, J. Y., Zhou, Q., & Yin, Y. (2022) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2022.11.004 · Cited by 47 · see citing works
Fast contour propagation for MR‐guided prostate radiotherapy using convolutional neural networks
Eppenhof, K. A. J., Maspero, M., Savenije, M. H. F., de Boer, J. C. J., van Zyp, J. R. N. V. D. V., Raaymakers, B. W., Raaijmakers, A. J. E., Veta, M., van den Berg, C. A. T., & Pluim, J. P. W. (2019) · Medical Physics · doi:10.1002/mp.13994 · Cited by 46 · see citing works
Gugliandolo, S. G., Pepa, M., Isaksson, L. J., Marvaso, G., Raimondi, S., Botta, F., Gandini, S., Ciardo, D., Volpe, S., Riva, G., et al. (2020) · European Radiology · doi:10.1007/s00330-020-07105-z · Cited by 44 · see citing works
Self-citationAI in medical physics: guidelines for publication
Naqa, I. E., Boone, J. M., Benedict, S., Goodsitt, M. M., Chan, H., Drukker, K., Hadjiiski, L. M., Ruan, D., & Sahiner, B. (2021) · Medical Physics · doi:10.1002/mp.15170 · Cited by 43 · see citing works
Jabbarpour, A., Mahdavi, S. R., Sadr, A. V., Esmaili, G., Shiri, I., & Zaidi, H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105277 · Cited by 43 · see citing works
Mayo, C. S., Feng, M. U.-S., Brock, K. K., Kudner, R., Balter, P. A., Buchsbaum, J. C., Caissie, A. L., Covington, E. L., Daugherty, E. C., Dekker, A. L. A. J., et al. (2023) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2023.05.033 · Cited by 43 · see citing works
Self-citationChuang, K., Giles, W., & Adamson, J. D. (2020) · Medical Physics · doi:10.1002/mp.14670 · Cited by 37 · see citing works
Machine and Deep Learning in Oncology, Medical Physics and Radiology
(2022) · Springer eBooks · doi:10.1007/978-3-030-83047-2 · Cited by 35 · see citing works
Chen, X., Ma, X., Yan, X., Luo, F., Yang, S., Wang, Z., Wu, R., Wang, J., Lu, N., Bi, N., et al. (2022) · Medical Physics · doi:10.1002/mp.15793 · Cited by 34 · see citing works
Barkousaraie, A. S., Ogunmolu, O., Jiang, S. B., & Nguyen, D. T. D. (2019) · Medical Physics · doi:10.1002/mp.13986 · Cited by 33 · see citing works
Kimura, Y., Kadoya, N., Oku, Y., Kajikawa, T., Tomori, S., & Jingu, K. (2021) · Medical Physics · doi:10.1002/mp.15031 · Cited by 33 · see citing works
HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting
Song, P., Eldar, Y. C., Mazor, G., & Rodrigues, M. (2019) · Medical Physics (Lancaster) · doi:10.1002/mp.13727 · Cited by 32 · see citing works
Kang, J., Thompson, R. F., Aneja, S., Lehman, C., Trister, A., Zou, J., Obcemea, C., & El Naqa, I. (2021) · Practical Radiation Oncology · doi:10.1016/j.prro.2020.06.001 · Cited by 30 · see citing works
A review of artificial intelligence applications for motion tracking in radiotherapy
Mylonas, A., Booth, J., & Nguyen, D. T. (2021) · Journal of Medical Imaging and Radiation Oncology · doi:10.1111/1754-9485.13285 · Cited by 29 · see citing works
Lessons learned in transitioning to AI in the medical imaging of COVID-19
Naqa, I. M. E., Hu, Q., Chen, W., Li, H., Fuhrman, J. D., Gorre, N., & Giger, M. L. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.s1.010902 · Cited by 29 · see citing works
Fransson, S., Tilly, D., & Strand, R. (2022) · Physics and Imaging in Radiation Oncology · doi:10.1016/j.phro.2022.06.001 · Cited by 28 · see citing works
Deep learning driven predictive treatment planning for adaptive radiotherapy of lung cancer
Lee, D., Hu, Y., Kuo, L., Alam, S. R., Yorke, E. D., Li, A., Rimner, A., & Zhang, P. (2022) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2022.02.013 · Cited by 28 · see citing works
Eriksson, O., & Zhang, T. (2022) · Medical Physics · doi:10.1002/mp.15622 · Cited by 28 · see citing works
Yuan, Z., Wang, Y., Hu, P., Zhang, D., Yan, B., Lu, H., Zhang, H., & Yang, Y. (2022) · Medical Physics · doi:10.1002/mp.15530 · Cited by 24 · see citing works
Uncertainty-guided man–machine integrated patient-specific quality assurance
Yang, X., Li, S., Shao, Q., Cao, Y. S., Yang, Z., & Zhao, Y. (2022) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2022.05.016 · Cited by 24 · see citing works
ESTRO ACROP guideline on prostate bed delineation for postoperative radiotherapy in prostate cancer
Pra, A. D., Dirix, P. R., Khoo, V. S., Carrié, C., Cozzarini, C., Fonteyne, V., Ghadjar, P., Iturriaga, A. G., Panebianco, V., Zapatero, A., et al. (2023) · Clinical and Translational Radiation Oncology · doi:10.1016/j.ctro.2023.100638 · Cited by 23 · see citing works
Prospective clinical deployment of machine learning in radiation oncology
Naqa, I. E. (2021) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-021-00541-w · Cited by 22 · see citing works
Clinical evaluation of two AI models for automated breast cancer plan generation
Kneepkens, E., Bakx, N., van der Sangen, M., Theuws, J. C. M., van der Toorn, P.-P., Rijkaart, D., van der Leer, J., van Nunen, T., Hagelaar, E., Bluemink, H., et al. (2022) · Radiation Oncology · doi:10.1186/s13014-022-01993-9 · Cited by 21 · see citing works
Self-citationA comprehensive survey on deep learning techniques in CT image quality improvement
Li, D., Ma, L., Li, J., Qi, S., Yao, Y., & Teng, Y. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02631-y · Cited by 19 · see citing works
Huang, Y., Pi, Y., Ma, K., Miao, X., Fu, S., Zhu, Z., Cheng, Y., Zhang, Z., Chen, H., Wang, H., et al. (2022) · Technology in Cancer Research & Treatment · doi:10.1177/15330338221104881 · Cited by 19 · see citing works
Luo, Y., Jolly, S., Palma, D. A., Lawrence, T. S., Tseng, H.-H., Valdés, G., McShan, D. L., Haken, R. K. T., & Naqa, I. E. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.05.032 · Cited by 19 · see citing works
Hussein, M., Clementel, E., Eaton, D. J., Greer, P. B., Haworth, A., Ishikura, S., Kry, S. F., Lehmann, J., Lye, J., Monti, A. F., et al. (2017) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2017.10.012 · Cited by 18 · see citing works
Self-citationMayo, C. S., Mierzwa, M. L., Yalamanchi, P., Evans, J. R., Worden, F. P., Medlin, R. P., Schipper, M. J., Schonewolf, C. A., Shah, J. L., Spector, M. E., et al. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00037 · Cited by 12 · see citing works
Self-citationThor, M., Oh, J. H., Apte, A., & Deasy, J. O. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00978 · Cited by 10 · see citing works
Gong, C., Zhu, K., Lin, C., Han, C., Lu, Z., Chen, Y., Yu, C., Hou, L., Zhou, Y., Yi, J., et al. (2022) · Medical Physics · doi:10.1002/mp.16010 · Cited by 9 · see citing works
Chen, A., Chen, F., Li, X., Zhang, Y., Chen, L., Chen, L., & Zhu, J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.908903 · Cited by 8 · see citing works
Ni, Y., Chen, S., Hibbard, L. S., & Voet, P. W. J. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac80e5 · Cited by 8 · see citing works
Matsuura, T., Kawahara, D., Saito, A., Miura, H., Yamada, K., Ozawa, S., & Nagata, Y. (2022) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-022-01172-w · Cited by 6 · see citing works
Wolfs, C. J. A., Varfalvy, N., Canters, R. A. M., Nijsten, S. M. J. J. G., Hattu, D., Archambault, L., & Verhaegen, F. (2020) · Medical Physics · doi:10.1002/mp.14385 · Cited by 6 · see citing works
Bakx, N., van der Sangen, M., Theuws, J. C. M., Bluemink, H., & Hurkmans, C. (2023) · Technical Innovations & Patient Support in Radiation Oncology · doi:10.1016/j.tipsro.2023.100209 · Cited by 5 · see citing works
Self-citationChun, J., Park, J. C., Olberg, S., Zhang, Y., Nguyen, D. T. D., Wang, J., Kim, J. S., & Jiang, S. B. (2021) · Medical Physics · doi:10.1002/mp.15352 · Cited by 4 · see citing works
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Research works printed but not linked: 4
- [41]41. Holmes, J. ∙ Liu, Z. ∙ Zhang, L. ... Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics. Front Oncol. 2023 Jul; 17:1219326 Crossref Scopus (94) Google Scholar
- [59]59. Liu, Z, Wang, P, Li, Y, Holmes, J, Shu, P, Zhang, L, Liu, C, Liu, N, Zhu, D, Li, X, Li, Q, Patel, S. H., Sio, T. T, Liu, T, Liu, W. RadOnc-GPT: A Large Language Model for Radiation Oncology. arXiv:2309.10160 webpage visited 6-11-2023. Google Scholar · arXiv
- [77]77. Prunaretty, J. ∙ Güngör, G. ∙ Gevaert, T. ... A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging. Front Oncol. 2023; 13:1245054 Crossref Scopus (7) Google Scholar
- [85]85. van de, Sande D. ∙ Sharabiani, M. ∙ Bluemink, H. ... Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer. Phys Imaging Radiat Oncol. 2021; 20:111-116 Full Text Full Text (PDF) Scopus (37) Google Scholar
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Azevedo, T. A., Abrantes, A. M., & Carvalho, J. (2025) · Technology in Cancer Research & Treatment · doi:10.1177/15330338251350909 · Cited by 2 · see citing works
Delopoulos, N., Marschner, S. N., Lombardo, E., Ribeiro, M. F., Rogowski, P., Losert, C., Winderl, T., Albarqouni, S., Belka, C., Corradini, S., et al. (2025) · Physics and Imaging in Radiation Oncology · doi:10.1016/j.phro.2025.100819 · Cited by 1 · see citing works
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Beddok, A., Cohen, E., Raymond, G., Rabenjason, S., Donnarieix, D., & Delporte, L. (2026) · Journal of Cancer Education · doi:10.1007/s13187-026-02836-x
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Sun, H., Li, J., Liu, Y., Meng, F., An, Z., Cui, Y., Lv, B., Chai, G., Jia, L., Shi, Z., et al. (2026) · BMC Medical Imaging · doi:10.1186/s12880-026-02400-5
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Malone, C., Callens, D., Forde, E., Leech, M., Cardenas, C. E., Gooding, M. J., Ryan, S. M., Thirion, P. G., Fitzpatrick, C., O’Donovan, T., et al. (2026) · Clinical and Translational Radiation Oncology · doi:10.1016/j.ctro.2026.101210
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Optimizing the delivery of radiotherapy with artificial intelligence
Katsoulakis, E., & Naqa, I. E. (2026) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-026-01204-4
Mondal, K., Vijay, A., Mandal, A., & Patel, G. (2026) · Journal of Medical Physics · doi:10.4103/jmp.jmp_105_26
- Authors
- Hurkmans, C., Bibault, J.-E., Brock, K. K., van Elmpt, W., Feng, M., David Fuller, C., Jereczek-Fossa, B. A., Korreman, S., Landry, G., Madesta, F., Mayo, C., McWilliam, A., Moura, F., Muren, L. P., El Naqa, I., Seuntjens, J., Valentini, V., & Velec, M.
- Affiliations
- Catharina Ziekenhuis (NL)
- Hôpital Européen Georges-Pompidou (FR)
- The University of Texas MD Anderson Cancer Center (US)
- University of California, San Francisco (US)
- Aarhus University (DK)
- LMU Klinikum (DE)
- University Medical Center Hamburg-Eppendorf (DE)
- University of Michigan (US)
- University of Manchester (GB)
- University Health Network (CA)
- Università Cattolica del Sacro Cuore (IT)
- University of Toronto (CA)
- Corresponding author
- Hurkmans, Coen
- ORCID
- Venue
- Radiotherapy and Oncology
- Publisher
- Elsevier BV
- Published
- 2024-08
- Volume
- 197
- Pages
- 110345
- ISSN
- 0167-8140 (ISSN-L)
- OpenAlex
- W4399327635
- Cited by
- 110
- References
- 85
- Citations by year
- 2026: 56 · 2025: 46 · 2024: 7 · 2023: 1
- FWCI
- 10.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Radiotherapy and Oncology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- TU/e Research Portal (publishedVersion, cc-by)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- Research Publications (Maastricht University) (submittedVersion, cc-by)
- Funders
- European SocieTy for Radiotherapy and Oncology
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence, radiotherapy, treatment planning, adaptive radiotherapy, image segmentation, patient-specific quality assurance, outcome prediction, AI ethics, clinical adoption of AI, Delphi consensus
- MeSH terms
- Algorithms, Artificial Intelligence, Delphi Technique, Humans, Radiotherapy, Radiotherapy Planning, Computer-Assisted, Radiation Oncology
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