Perspectives on artificial intelligence-generated chronic disease risk predictions in early breast cancer: an international survey among radiation oncologists
DOI:
https://doi.org/10.2340/1651-226X.2026.45901Keywords:
Radiotherapy, Breast Cancer, Artificial Intelligence, Radiation Oncologists, AttitudesDownloads
References
OECD/European Commission. EU country cancer profiles synthesis report 2025. Paris: OECD Publishing; 2025.
Ng HS, Vitry A, Koczwara B, Roder D, McBride ML. Patterns of comorbidities in women with breast cancer: a Canadian population-based study. Cancer Causes Control. 2019;30(9):931–41. DOI: https://doi.org/10.1007/s10552-019-01203-0
Greenlee H, Iribarran C, Rana JS, Cheng R, Nguyen-Huynh M, Rillamas-Sun E, et al. Risk of cardiovascular disease in women with and without breast cancer: the pathways heart study. J Clin Oncol. 2022;40(15):1647–58. DOI: https://doi.org/10.1200/JCO.21.01736
Rey D, Touzani R, Bouhnik AD, Rousseau F, Monet A, Préau M, et al. Evolution of physical activity and body weight changes in breast cancer survivors 5 years after diagnosis – VICAN 2 & 5 French national surveys. Breast. 2021;59:248–55. DOI: https://doi.org/10.1016/j.breast.2021.07.012
Bodelon C, Masters M, Bloodworth DE, Briggs PJ, Rees-Punia E, McCullough LE, et al. Physical health decline after chemotherapy or endocrine therapy in breast cancer survivors. JAMA Netw Open. 2025;8(2):e2462365. DOI: https://doi.org/10.1001/jamanetworkopen.2024.62365
Chien AJ, Goss PE. Aromatase inhibitors and bone health in women with breast cancer. J Clin Oncol. 2006;24(33):5305–12. DOI: https://doi.org/10.1200/JCO.2006.07.5382
Bostany G, Chen Y, Francisco L, Dai C, Meng Q, Sparks J, et al. Cardiac dysfunction among breast cancer survivors: role of cardiotoxic therapy and cardiovascular risk factors. J Clin Oncol. 2025;43(1):32–45. DOI: https://doi.org/10.1200/JCO.23.01779
Lund M, Corn G, Jensen M, Petersen T, Dalhoff K, Ejlertsen B, et al. Ischemic cardiotoxicity of aromatase inhibitors in postmenopausal patients with early breast cancer in Denmark: a cohort study of real-world data. Lancet Oncol. 2024;25(11):1496–506. DOI: https://doi.org/10.1016/S1470-2045(24)00491-1
Harborg S, Cronin-Fenton D, Jensen MR, Ahern TP, Ewertz M,
Borgquist S. Obesity and risk of recurrence in patients with breast cancer treated with aromatase inhibitors. JAMA Netw Open. 2023;6(10):e2337780. DOI: https://doi.org/10.1001/jamanetworkopen.2023.37780
Aleixo GFP, Williams GR, Nyrop KA, Muss HB, Shachar SS. Muscle composition and outcomes in patients with breast cancer: meta-analysis and systematic review. Breast Cancer Res Treat. 2019;177(3):569–79. DOI: https://doi.org/10.1007/s10549-019-05352-3
Gal R, van Velzen SGM, Hooning MJ, Emaus MJ, van der Leij F, Gregorowitsch ML, et al. Identification of risk of cardiovascular disease by automatic quantification of coronary artery calcifications on radiotherapy planning CT scans in patients with breast cancer. JAMA Oncol. 2021;7(7):1024–32. DOI: https://doi.org/10.1001/jamaoncol.2021.1144
Awiwi MO, Zhang X, Kandemirli VB, Duran C, Hanna MF, Aburadi M, et al. Evaluation for osteoporosis using low-dose CT imaging of the chest obtained for lung cancer screening: a retrospective study of 1,336 patients. Chest. 2026;169(5):1381–1390. DOI: https://doi.org/10.1016/j.chest.2025.12.031
Chaudhary MFA, Awan HA, Gerard SE, Bodduluri S, Comellas AP, Barjaktarevic IZ, et al. Deep learning estimation of small airway disease from inspiratory chest computed tomography: clinical validation, repeatability, and associations with adverse clinical outcomes in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2025;211(7):1185–95. DOI: https://doi.org/10.1164/rccm.202409-1847OC
Yi J, Marcinkiewicz AM, Shanbhag A, Miller RJH, Geers J, Zhang W,
et al. AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study. Lancet Digit Health. 2025;7(5):100862. DOI: https://doi.org/10.1016/j.landig.2025.02.002
Carstensen FV, Gernaat SAM, Banning F, Batista E, van den Bongard D, Harbeck N, et al. Attitudes toward AI-generated risk prediction in patients with early breast cancer: an international multicenter survey. Acta Oncol (Madr). 2025;64:1125–8. DOI: https://doi.org/10.2340/1651-226X.2025.44030
Lambert SI, Madi M, Sopka S, Lenes A, Stange H, Buszello CP, et al. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. NPJ Digit Med. 2023;6(1):111. DOI: https://doi.org/10.1038/s41746-023-00852-5
United Nations. Department of Economic and Social Affairs, Statistics. UN Geoscheme M79, Geographic Regions [Internet]. c2025 [cited 2025 Nov 10]. Available from: https://unstats.un.org/unsd/methodology/m49/
Eltorai AEM, Parris DJ, Tarrant MJ, Mayo-Smith WW, Andriole KP. AI implementation: radiologists’ perspectives on AI-enabled opportunistic CT screening. Clin Imaging. 2024;115:110282. DOI: https://doi.org/10.1016/j.clinimag.2024.110282
Batumalai V, Jameson M, King O, Walker R, Slater C, Dundas K, et al. Cautiously optimistic: a survey of radiation oncology professionals’ perceptions of automation in radiotherapy planning. Tech Innov Patient Support Radiot Oncol. 2020:16;58–64. DOI: https://doi.org/10.1016/j.tipsro.2020.10.003
Bourbonne V, Laville A, Wagneur N, Ghannam Y, Larnaudie A. Excitement and concerns of young radiation oncologists over automatic segmentation: a French perspective. Cancers. 2023:15(7);2040. DOI: https://doi.org/10.3390/cancers15072040
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Copyright (c) 2026 Frederik Voigt Carstensen, Belinda Bøgh Irankunda, Eva Batista, Desiree van den Bongard, Tanja Spanic, Sofie A.M. Gernaat, Helena Verkooijen, Maja Vestmø Maraldo

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