Front. Med. Frontiers in Medicine Front. Med. 2296-858X Frontiers Media S.A. 10.3389/fmed.2025.1738811 Correction Correction: Innovative approaches in precision radiation oncology: advanced imaging technologies and challenges which shape the future of radiation therapy Yan Yue 1 2 3 * Alexander Daniel A. 1 2 Bednarz Bryan P. 4 Bronk Lawrence F. 5 Chen Huixiao 6 Gladstone David J. 1 2 3 Han Bin 7 Iannuzzi Christopher M. 8 Li Yuting 5 Nguyen Ngoc 6 Mulenga Natasha 3 Viscariello Natalie N. 9 Wang Yuenan 8 Weygand Joseph 1 2 Zlateva Yana 5 Guan Fada 5 * Department of Radiation Oncology & Applied Sciences, Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States Geisel School of Medicine, Dartmouth College, Hanover, NH, United States Thayer School of Engineering, Dartmouth College, Hanover, NH, United States Department of Medical Physics, University of Wisconsin Madison, Madison, WI, United States Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT, United States Department of Radiation Oncology, Stanford University, Stanford, CA, United States Hartford Health Care Cancer Institute, St. Vincent's Medical Center, Bridgeport, CT, United States Department of Radiation Oncology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States Correspondence: Fada Guan, fguan@mdanderson.org; Yue Yan, yue.yan@dartmouth.edu 25 11 2025 2025 12 1738811 03 11 2025 11 11 2025 Copyright © 2025 Yan, Alexander, Bednarz, Bronk, Chen, Gladstone, Han, Iannuzzi, Li, Nguyen, Mulenga, Viscariello, Wang, Weygand, Zlateva and Guan. 2025 Yan, Alexander, Bednarz, Bronk, Chen, Gladstone, Han, Iannuzzi, Li, Nguyen, Mulenga, Viscariello, Wang, Weygand, Zlateva and Guan https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. A Correction on Innovative approaches in precision radiation oncology: advanced imaging technologies and challenges which shape the future of radiation therapy by Yan, Y„ Alexander, D. A., Bednarz, B. P., Bronk, L. F., Chen, H., Gladstone, D. J., Han, B., Iannuzzi, C. M., Li, Y., Nguyen, N., Mulenga, N., Viscariello, N. N., Wang, Y., Weygand, J., Zlateva, Y., and Guan, F. (2025). Front. Med. 12:1686593. doi: 10.3389/fmed.2025.1686593 magnetic resonance imaging guided radiotherapy positron emission tomography stereoscopic imaging and surface guidance cone beam computed tomography generative image synthesis Cherenkov radiation imaging imaging innovations in proton therapy advanced quantitative imaging section-at-acceptance Nuclear Medicine

The reference in the caption of Figure 6 was erroneously cited as Maas et al. (104). It should be Ronneberger et al. (59).

The caption of Figure 6 has been updated to “Architecture of the U-Net. Reproduced with permission from Ronneberger et al. (59).”

The reference in the caption of Figure 9 was erroneously cited as Alexander et al. (227).

It should be Wang et al. (224).

The sentence “Reprinted from Alexander et al. (227).” has been corrected to “Reproduced with permission from Wang et al. (224).” in the caption of Figure 9.

Reference 224 was erroneously written as “Wang S, Chen Y, Jarvis LA, Tang Y, Gladstone DJ, Samkoe KS, et al. Robust Real-time segmentation of bio-morphological features in Human Cherenkov imaging during radiotherapy via deep learning. arXiv [Preprint]. arXiv:2409.05666v1 (2014). doi: 10.1002/mp.18002”. It should be “Wang S, Chen Y, Jarvis LA, Tang Y, Gladstone DJ, Samkoe KS, et al. Robust real-time segmentation of bio-morphological features in human Cherenkov imaging during radiotherapy via deep learning. Med Phys. (2025) 52:e18002. doi: 10.1002/mp.18002.”

Reference 1 was incorrectly cited in section 6 Image synthesis in RT, 6.1 Deep learning networks in medical images, Paragraph 2. Reference 59 should have been cited.

The sentence previously read: “One of the most well-known CNN models is the U-shaped net (U-Net) proposed by Ronneberger et al. (1) (Figure 6).”

The sentence now reads: “One of the most well-known CNN models is the U-shaped net (U-Net) proposed by Ronneberger et al. (59) (Figure 6).”

The original version of this article has been updated.

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 References Wang S Chen Y Jarvis LA Tang Y Gladstone DJ Samkoe KS . Robust real-time segmentation of bio-morphological features in human Cherenkov imaging during radiotherapy via deep learning. Med Phys. (2025) 52:e18002. doi: 10.1002/mp.1800239314506

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