Curtise K. C. Ng
Vincent W. S. Leung- 1Curtin Medical School, Curtin University, Perth, WA, Australia
- 2Curtin Medical Research Institute (Curtin MRI), Faculty of Health Sciences, Curtin University, Perth, WA, Australia
- 3Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
Editorial on the Research Topic
Artificial intelligence in radiology and radiation oncology
Use of artificial intelligence (AI) has become popular in radiology and radiation oncology practices. Various commercial products such as image quality enhancement and computer-aided detection and diagnosis (CAD) tools in radiology, and auto-segmentation software in radiation therapy treatment planning are available nowadays (1–5). To promote ongoing development of AI in radiology and radiation oncology, this Research Topic provides a platform to facilitate the sharing of respective AI research findings and promote evidence-based practice. This Research Topic includes four distinct studies covering both machine learning [least absolute shrinkage and selection operator (LASSO)] and deep learning (DL) techniques [convolutional neural network (CNN), generative adversarial network (GAN) and Vision Transformers (ViT)] for image synthesis, registration, segmentation, classification, CAD and radiomics. We would like to thank all authors for their contributions.
The contribution by Lerch et al. demonstrates that their GAN model, DreamOn is able to synthesize images for training the ResNet-18 model for breast ultrasound image classification, resulting in more robust model performance. Their study findings are consistent with outcomes of a previous systematic review that GAN is an effective data augmentation strategy (6). For researchers who are interested in using their DreamOn model for data augmentation, the model can be obtained from https://github.com/lucle4/DreamOn.
For Dumbrique et al.'s contribution, a novel DL model based on fully CNNs (FCNNs) and ViTs is developed for CAD of pneumothorax. Their model outperforms those of previous studies by Abdella et al. (7), Hongyu et al. (8), Jakhar et al. (9), Malhotra et al. (10) and Mostayed et al. (11). This paves the way for implementation of their model in clinical practice.
Guo et al.'s study shows that their machine learning based radiomics model is effective for luminal and non-luminal breast cancer differentiation based on T2-weighted magnetic resonance images with the highest area under the receiver operating characteristic curve value of 0.873. Although this result is encouraging, manual segmentation of tumors is employed in their study. This subsequently affects its generalizability to certain extent (12).
The last contribution covered in this Research Topic is about a research platform, SenseCare developed by Wang et al. The SenseCare allows multi-institution access to a range of DL models for image registration, segmentation, classification and CAD in radiology and radiation oncology. These tools support translational research projects across different clinical domains.
It is noted that the AI applications in radiology and radiation oncology are broad. In this Research Topic, several representations of these applications are demonstrated. However, other uses such as automated structured reporting, clinical decision support, image quality enhancement, reconstruction and translation as well as optimization of examination/treatment scheduling are not included (13). Radiology and radiation oncology are leading specialties in the adoption of AI applications in healthcare. As of 2021, about 70% of the 343 United States Food and Drug Administration approved AI products were developed for these two specialties (14). According to the European Network for the Assessment of Imaging in Medicine (EuroAIM)/European Society of Medical Imaging Informatics (EuSoMII) 2024 survey with 572 responses from the European Society of Radiology (ESR) members, about half of the respondents have already used AI in clinical practice (15). Hence, we would like to encourage researchers, academics, and clinicians to continue conducting AI research in radiology and radiation oncology including safe and responsible use of AI and sharing their findings (16, 17). In this way, we can ensure continuous growth of our specialties and evidence-based practice for convincing more clinical centers to adopt AI and realizing its benefits in a wider context to improve patient outcomes.
Author contributions
CN: Conceptualization, Writing – original draft, Writing – review & editing. VL: Conceptualization, Writing – original draft, Writing – review & editing.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1. Ng CKC. Artificial intelligence for radiation dose optimization in pediatric radiology: a systematic review. Children. (2022) 9(7):1044. doi: 10.3390/children9071044
2. Ng CKC. Diagnostic performance of artificial intelligence-based computer-aided detection and diagnosis in pediatric radiology: a systematic review. Children. (2023) 10(3):525. doi: 10.3390/children10030525
3. Ng CKC. Performance of commercial deep learning-based auto-segmentation software for breast cancer radiation therapy planning: a systematic review. Multimodal Technol Interact. (2024) 8(12):114. doi: 10.3390/mti8120114
4. Ng CKC. Performance of commercial deep learning-based auto-segmentation software for prostate cancer radiation therapy planning: a systematic review. Information. (2025) 16(3):215. doi: 10.3390/info16030215
5. Ng CKC, Leung VWS, Hung RHM. Clinical evaluation of deep learning and atlas-based auto-contouring for head and neck radiation therapy. Appl Sci. (2022) 12(22):11681. doi: 10.3390/app122211681
6. Ng CKC. Generative adversarial network (generative artificial intelligence) in pediatric radiology: a systematic review. Children. (2023) 10(8):1372. doi: 10.3390/children10081372
7. Abedalla A, Abdullah M, Al-Ayyoub M, Benkhelifa E. Chest x-ray pneumothorax segmentation using u-net with efficientnet and resnet architectures. PeerJ Comput Sci. (2021) 7:e607. doi: 10.7717/peerj-cs.607
8. Wang H, Gu H, Qin P, Wang J. Chexlocnet: automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks. PLoS One. (2020) 15:e0242013. doi: 10.1371/journal.pone.0242013
9. Jakhar K, Bajaj R, Gupta R. Pneumothorax segmentation: deep learning image segmentation to predict pneumothorax. arXiv [Preprint]. (2019) arXiv:1912.07329. doi: 10.48550/arXiv.1912.07329
10. Malhotra P, Gupta S, Koundal D, Zaguia A, Kaur M, Lee HN. Deep learning-based computer-aided pneumothorax detection using chest x-ray images. Sensors. (2022) 22:2278. doi: 10.3390/s22062278
11. Mostayed A, Wee WG, Zhou X. Content-adaptive u-net architecture for medical image segmentation. 2019 International Conference on Computational Science and Computational Intelligence (CSCI) (2019). p. 698–702. doi: 10.1109/CSCI49370.2019.00131
12. Leung VWS, Ng CKC, Lam SK, Wong PT, Ng KY, Tam CH, et al. Computed tomography-based radiomics for long-term prognostication of high-risk localized prostate cancer patients received whole pelvic radiotherapy. J Pers Med. (2023) 13(12):1643. doi: 10.3390/jpm13121643
13. Tanguay W, Acar P, Fine B, Abdolell M, Gong B, Cadrin-Chênevert A, et al. Assessment of radiology artificial intelligence software: a validation and evaluation framework. Can Assoc Radiol J. (2023) 74(2):326–33. doi: 10.1177/08465371221135760
14. Davis MA, Lim N, Jordan J, Yee J, Gichoya JW, Lee R. Imaging artificial intelligence: a framework for radiologists to address health equity, from the AJR special series on DEI. Am J Roentgenol. (2023) 221(3):302–8. doi: 10.2214/AJR.22.28802
15. Zanardo M, Visser JJ, Colarieti A, Cuocolo R, Klontzas ME, Pinto Dos Santos D, et al. Impact of AI on radiology: a EuroAIM/EuSoMII 2024 survey among members of the European society of radiology. Insights Imaging. (2024) 15(1):240. doi: 10.1186/s13244-024-01801-w
16. Upadhyay U, Gradisek A, Iqbal U, Dhar E, Li YC, Syed-Abdul S. Call for the responsible artificial intelligence in the healthcare. BMJ Health Care Inform. (2023) 30(1):e100920. doi: 10.1136/bmjhci-2023-100920
Keywords: artificial intelligence, computer-aided detection, deep learning, image synthesis, medical imaging, radiation oncology, radiology, radiomics
Citation: Ng CKC and Leung VWS (2025) Editorial: Artificial intelligence in radiology and radiation oncology. Front. Radiol. 5:1657119. doi: 10.3389/fradi.2025.1657119
Received: 30 June 2025; Accepted: 11 July 2025;
Published: 23 July 2025.
Edited and Reviewed by: Yunze Yang, University of Miami, United States
Copyright: © 2025 Ng and Leung. 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.
*Correspondence: Curtise K. C. Ng, Y3VydGlzZS5uZ0BjdXJ0aW4uZWR1LmF1