Redefining the role of radiation oncologists in the AI era

References

[1] Wang L, Chen X, Zhang L, et al. Artificial intelligence in clinical decision support systems for oncology. Int J Med Sci. 2023; 20(1):79-86. doi: 10.7150/ijms.77205

[2] Nafees A, Khan M, Chow R, et al. Evaluation of clinical decision support systems in oncology: An updated systematic review. Crit Rev Oncol Hematol. 2023;192:104143. doi: 10.1016/j.critrevonc.2023.104143

[3] Erden MB, Cansiz S, Caki O, et al. FourierLoss: Shape- aware l function with Fourier descriptors. Neurocomputing. 2025;638:130155. doi: 10.1016/j.neucom.2025.130155

[4] Chen M, Wu S, Zhao W, Zhou Y, Zhou Y, Wang G. Application of deep learning to auto-delineation of target volumes and organs at risk in radiotherapy. Cancer Radiother. 2022; 26(3):494-501. doi: 10.1016/j.canrad.2021.08.020

[5] Matoska T, Patel M, Liu H, Beriwal S. Review of deep learning based autosegmentation for clinical target volume: Current status and future directions. Adv Radiat Oncol. 2024; 9(5):101470. doi: 10.1016/j.adro.2024.101470

[6] Wang TW, Hong JS, Huang JW, Liao CY, Lu CF, Wu YT. Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation. Radiother Oncol. 2024;197:110344. doi: 10.1016/j.radonc.2024.110344

[7] Mackay K, Bernstein D, Glocker B, Kamnitsas K, Taylor A. A review of the metrics used to assess auto- contouring systems in radiotherapy. Clin Oncol (R Coll Radiol). 2023; 35(6):354-369. doi: 10.1016/j.clon.2023.01.016

[8] Bahloul MA, Jabeen S, Benoumhani S, Alsaleh HA, Belkhatir Z, Al-Wabil A. Advancements in synthetic CT generation from MRI: A review of techniques, and trends in radiation therapy planning. J Appl Clin Med Phys. 2024; 25(11):e14499. doi: 10.1002/acm2.14499

[9] Giraud P, Bibault JE. Artificial intelligence in radiotherapy: Current applications and future trends. Diagn Interv Imaging. 2024; 105(12):475-480. doi: 10.1016/j.diii.2024.06.001

[10] Byrne M, Archibald-Heeren B, Hu Y, et al. Varian ethos online adaptive radiotherapy for prostate cancer: Early results of contouring accuracy, treatment plan quality, and treatment time. J Appl Clin Med Phys. 2022; 23(1):e13479. doi: 10.1002/acm2.13479

[11] Zwanenburg A, Price G, Löck S. Artificial intelligence for response prediction and personalisation in radiation oncology. Strahlenther Onkol. 2025; 201(3):266-273. doi: 10.1007/s00066-024-02281-z

[12] Akcay M, Etiz D, Celik O. Prediction of survival and recurrence patterns by machine learning in gastric cancer cases undergoing radiation therapy and chemotherapy. Adv Radiat Oncol. 2020; 5(6):1179-1187. doi: 10.1016/j.adro.2020.07.007

[13] Kraus KM, Oreshko M, Schnabel JA, Bernhardt D, Combs SE, Peeken JC. Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation. Lung Cancer. 2024;189:107507. doi: 10.1016/j.lungcan.2024.107507

[14] Isaksson LJ, Pepa M, Zaffaroni M, et al. Machine learning- based models for prediction of toxicity outcomes in radiotherapy. Front Oncol. 2020;10:790. doi: 10.3389/fonc.2020.00790

[15] Bitterman DS, Miller TA, Mak RH, Savova GK. Clinical natural language processing for radiation oncology: A review and practical primer. Int J Radiat Oncol Biol Phys. 2021; 110(3):641-655. doi: 10.1016/j.ijrobp.2021.01.044

[16] Naik N, Hameed BMZ, Shetty DK, et al. Legal and ethical consideration in artificial intelligence in healthcare: Who takes responsibility? Front Surg. 2022;9:862322. doi: 10.3389/fsurg.2022.862322

[17] Lahmi L, Mamzer MF, Burgun A, Durdux C, Bibault JE. Ethical aspects of artificial intelligence in radiation oncology. Semin Radiat Oncol. 2022; 32(4):442-448. doi: 10.1016/j.semradonc.2022.06.013