{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T21:53:08Z","timestamp":1787003588688,"version":"build-2736575974"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Markusovszky Teaching Hospital of County Vas"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>One possible adverse effect of breast irradiation is the development of pulmonary fibrosis. The aim of this study was to determine whether planning CT scans can predict which patients are more likely to develop lung lesions after treatment. A retrospective analysis of 242 patient records was performed using different machine learning models. These models showed a remarkable correlation between the occurrence of fibrosis and the hounsfield units of lungs in CT data. Three different classification methods (Tree, Kernel-based, k-Nearest Neighbors) showed predictive values above 60%. The human predictive factor (HPF), a mathematical predictive model, further strengthened the association between lung hounsfield unit (HU) metrics and radiation-induced lung injury (RILI). These approaches optimize radiation treatment plans to preserve lung health. Machine learning models and HPF can also provide effective diagnostic and therapeutic support for other diseases.<\/jats:p>","DOI":"10.1038\/s41598-025-02617-4","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:52:54Z","timestamp":1748307174000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy"],"prefix":"10.1038","volume":"15","author":[{"given":"Tam\u00e1s","family":"Ungv\u00e1ri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D\u00f6me","family":"Szab\u00f3","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e1s","family":"Gy\u0151rfi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zs\u00f3fia","family":"Dankovics","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bal\u00e1zs","family":"Kiss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judit","family":"Olajos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K\u00e1roly","family":"T\u0151k\u00e9si","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"2617_CR1","unstructured":"National Cancer Institute. (n.d.). Breast cancer statistics. Surveillance, epidemiology, and end results (SEER) program. https:\/\/seer.cancer.gov\/statfacts\/html\/breast.html"},{"key":"2617_CR2","doi-asserted-by":"publisher","first-page":"e2200269","DOI":"10.1200\/GO.22.00269","volume":"9","author":"RF Mohamed","year":"2023","unstructured":"Mohamed, R. F., Abdelhameed, D. H. & Mohamed, M. A. Combination of anatomical and biological factors to predict disease-free survival in breast cancer. JCO Global Oncol. 9, e2200269. https:\/\/doi.org\/10.1200\/GO.22.00269 (2023).","journal-title":"JCO Global Oncol."},{"issue":"542","key":"2617_CR3","first-page":"148","volume":"46","author":"J Ambrose","year":"1973","unstructured":"Ambrose, J. & Hounsfield, G. Computerized transverse axial tomography. Br. J. Radiol. 46 (542), 148\u2013149 (1973).","journal-title":"Br. J. Radiol."},{"key":"2617_CR4","volume-title":"StatPearls","author":"TD DenOtter","year":"2023","unstructured":"DenOtter, T. D. & Schubert, J. Hounsfield unit. In StatPearls.  (StatPearls Publishing, 2023)."},{"issue":"2","key":"2617_CR5","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1038\/mi.2008.85","volume":"2","author":"MS Wilson","year":"2009","unstructured":"Wilson, M. S. & Wynn, T. A. Pulmonary fibrosis: pathogenesis, etiology, and regulation. Mucosal Immunol. 2 (2), 103\u2013121. https:\/\/doi.org\/10.1038\/mi.2008.85 (2009).","journal-title":"Mucosal Immunol."},{"issue":"2","key":"2617_CR6","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1164\/ajrccm\/140.2.513","volume":"140","author":"A Burkhardt","year":"1989","unstructured":"Burkhardt, A. Alveolitis and collapse in the pathogenesis of pulmonary fibrosis. Am. Rev. Respir. Dis. 140 (2), 513\u2013524. https:\/\/doi.org\/10.1164\/ajrccm\/140.2.513 (1989).","journal-title":"Am. Rev. Respir. Dis."},{"issue":"25","key":"2617_CR7","doi-asserted-by":"publisher","first-page":"3991","DOI":"10.1200\/JCO.2007.10.9777","volume":"25","author":"JR Carver","year":"2007","unstructured":"Carver, J. R. et al. American society of clinical oncology clinical evidence review on the ongoing care of adult cancer survivors: cardiac and pulmonary late effects. J. Clin. Oncol. 25 (25), 3991\u20134008. https:\/\/doi.org\/10.1200\/JCO.2007.10.9777 (2007).","journal-title":"J. Clin. Oncol."},{"issue":"2","key":"2617_CR8","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1080\/02841860701630267","volume":"47","author":"R V\u00e5gane","year":"2008","unstructured":"V\u00e5gane, R. et al. Radiological and functional assessment of radiation-induced pulmonary damage following breast irradiation. Acta Oncol. 47 (2), 248\u2013254. https:\/\/doi.org\/10.1080\/02841860701630267 (2008).","journal-title":"Acta Oncol."},{"issue":"1","key":"2617_CR9","first-page":"37","volume":"22","author":"P Ghafoori","year":"2008","unstructured":"Ghafoori, P., Marks, L. B., Vujaskovic, Z. & Kelsey, C. R. Radiation-induced lung injury: assessment, management, and prevention. Oncol. (Williston Park). 22 (1), 37\u201353 (2008).","journal-title":"Oncol. (Williston Park)"},{"issue":"3","key":"2617_CR10","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.radonc.2007.09.013","volume":"86","author":"OK R\u00f8dningen","year":"2008","unstructured":"R\u00f8dningen, O. K. et al. Radiation-induced gene expression in human subcutaneous fibroblasts is predictive of radiation-induced fibrosis. Radiother. Oncol. 86 (3), 314\u2013320. https:\/\/doi.org\/10.1016\/j.radonc.2007.09.013 (2008).","journal-title":"Radiother. Oncol."},{"issue":"2","key":"2617_CR11","first-page":"43","volume":"4","author":"T Beinert","year":"1999","unstructured":"Beinert, T. et al. Oxidant-induced lung injury in anticancer therapy. Eur. J. Med. Res. 4 (2), 43\u201353 (1999).","journal-title":"Eur. J. Med. Res."},{"issue":"3","key":"2617_CR12","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1667\/RR1319.1","volume":"170","author":"AM Lemay","year":"2008","unstructured":"Lemay, A. M. & Haston, C. K. Radiation-induced lung response of AcB\/BcA Recombinant congenic mice. Radiat. Res. 170 (3), 299\u2013306. https:\/\/doi.org\/10.1667\/RR1319.1 (2008).","journal-title":"Radiat. Res."},{"issue":"5","key":"2617_CR13","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1080\/01902140490438915","volume":"30","author":"CJ Johnston","year":"2004","unstructured":"Johnston, C. J. et al. Inflammatory cell recruitment following thoracic irradiation. Exp. Lung Res. 30 (5), 369\u2013382. https:\/\/doi.org\/10.1080\/01902140490438915 (2004).","journal-title":"Exp. Lung Res."},{"issue":"5","key":"2617_CR14","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1080\/095530099140276","volume":"75","author":"W Westermann","year":"1999","unstructured":"Westermann, W. et al. Th2 cells as effectors in postirradiation pulmonary damage preceding fibrosis in the rat. Int. J. Radiat. Biol. 75 (5), 629\u2013638. https:\/\/doi.org\/10.1080\/095530099140276 (1999).","journal-title":"Int. J. Radiat. Biol."},{"issue":"5","key":"2617_CR15","doi-asserted-by":"publisher","first-page":"e13759","DOI":"10.1111\/crj.13759","volume":"18","author":"F Zarei","year":"2024","unstructured":"Zarei, F. et al. Quantitative analysis of lung lesions using unenhanced chest computed tomography images. Clin. Respiratory J. 18 (5), e13759. https:\/\/doi.org\/10.1111\/crj.13759 (2024).","journal-title":"Clin. Respiratory J."},{"key":"2617_CR16","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1038\/s41746-020-00341-z","volume":"3","author":"SC Huang","year":"2020","unstructured":"Huang, S. C., Pareek, A., Seyyedi, S., Banerjee, I. & Lungren, M. P. Fusion of medical imaging and electronic health records using deep learning: A systematic review and implementation guidelines. NPJ Digit. Med. 3, 136. https:\/\/doi.org\/10.1038\/s41746-020-00341-z (2020).","journal-title":"NPJ Digit. Med."},{"issue":"1","key":"2617_CR17","doi-asserted-by":"publisher","first-page":"4788","DOI":"10.1038\/s41598-023-30870-y","volume":"13","author":"J Albers","year":"2023","unstructured":"Albers, J. et al. High-resolution propagation-based lung imaging at clinically relevant X-ray dose levels. Sci. Rep. 13 (1), 4788. https:\/\/doi.org\/10.1038\/s41598-023-30870-y (2023).","journal-title":"Sci. Rep."},{"issue":"5","key":"2617_CR18","doi-asserted-by":"publisher","first-page":"3064","DOI":"10.1007\/s00330-023-09500-8","volume":"33","author":"G Adelsmayr","year":"2023","unstructured":"Adelsmayr, G. et al. CT texture analysis reliability in pulmonary lesions: the influence of 3D vs. 2D lesion segmentation and volume definition by a Hounsfield-unit threshold. Eur. Radiol. 33 (5), 3064\u20133071. https:\/\/doi.org\/10.1007\/s00330-023-09500-8 (2023).","journal-title":"Eur. Radiol."},{"issue":"3","key":"2617_CR19","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1007\/s10278-023-00799-9","volume":"36","author":"G Costa","year":"2023","unstructured":"Costa, G. et al. Mapping tumor heterogeneity via local entropy assessment: making biomarkers visible. J. Digit. Imaging. 36 (3), 1038\u20131048. https:\/\/doi.org\/10.1007\/s10278-023-00799-9 (2023).","journal-title":"J. Digit. Imaging"},{"issue":"1","key":"2617_CR20","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1038\/s41698-023-00473-x","volume":"7","author":"LC Roisman","year":"2023","unstructured":"Roisman, L. C. et al. Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer. NPJ Precision Oncol. 7 (1), 125. https:\/\/doi.org\/10.1038\/s41698-023-00473-x (2023).","journal-title":"NPJ Precision Oncol."},{"issue":"2","key":"2617_CR21","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1111\/1759-7714.13273","volume":"11","author":"ML Chen","year":"2020","unstructured":"Chen, M. L. et al. Is there any correlation between spectral CT imaging parameters and PD-L1 expression of lung adenocarcinoma? Thorac. Cancer. 11 (2), 362\u2013368. https:\/\/doi.org\/10.1111\/1759-7714.13273 (2020).","journal-title":"Thorac. Cancer"},{"key":"2617_CR22","doi-asserted-by":"publisher","first-page":"107692","DOI":"10.1016\/j.cmpb.2023.107692","volume":"240","author":"A Alksas","year":"2023","unstructured":"Alksas, A. et al. A novel higher order appearance texture analysis to diagnose lung cancer based on a modified local ternary pattern. Comput. Methods Programs Biomed. 240, 107692. https:\/\/doi.org\/10.1016\/j.cmpb.2023.107692 (2023).","journal-title":"Comput. Methods Programs Biomed."},{"key":"2617_CR23","doi-asserted-by":"publisher","first-page":"110106","DOI":"10.1016\/j.radonc.2024.110106","volume":"192","author":"KH Brown","year":"2024","unstructured":"Brown, K. H. et al. Characterisation of quantitative imaging biomarkers for inflammatory and fibrotic radiation-induced lung injuries using preclinical radiomics. Radiotherapy Oncology: J. Eur. Soc. Therapeutic Radiol. Oncol. 192, 110106. https:\/\/doi.org\/10.1016\/j.radonc.2024.110106 (2024).","journal-title":"Radiotherapy Oncology: J. Eur. Soc. Therapeutic Radiol. Oncol."},{"issue":"1","key":"2617_CR24","doi-asserted-by":"publisher","first-page":"5853","DOI":"10.1038\/s41598-023-32979-6","volume":"13","author":"H Tan","year":"2023","unstructured":"Tan, H. et al. A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by radiomics machine learning. Sci. Rep. 13 (1), 5853. https:\/\/doi.org\/10.1038\/s41598-023-32979-6 (2023).","journal-title":"Sci. Rep."},{"issue":"10","key":"2617_CR25","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1007\/s00066-020-01625-9","volume":"196","author":"M Avanzo","year":"2020","unstructured":"Avanzo, M., Stancanello, J., Pirrone, G. & Sartor, G. Radiomics and deep learning in lung cancer. Strahlentherapie Und Onkologie: Organ. Der Deutschen Rontgengesellschaft \u2026 Et Al]. 196 (10), 879\u2013887. https:\/\/doi.org\/10.1007\/s00066-020-01625-9 (2020).","journal-title":"Strahlentherapie Und Onkologie: Organ. Der Deutschen Rontgengesellschaft \u2026 Et Al]"},{"issue":"5","key":"2617_CR26","doi-asserted-by":"publisher","first-page":"439","DOI":"10.4103\/lungindia.lungindia_489_17","volume":"35","author":"NP Ghonge","year":"2018","unstructured":"Ghonge, N. P. & Chowdhury, V. Minimum-intensity projection images in high-resolution computed tomography lung: technology update. Lung India. 35 (5), 439\u2013440. https:\/\/doi.org\/10.4103\/lungindia.lungindia_489_17 (2018).","journal-title":"Lung India"},{"issue":"1","key":"2617_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/21681163.2015.1124249","volume":"6","author":"M Gao","year":"2018","unstructured":"Gao, M. et al. Holistic classification of CT Attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput. Methods Biomech. Biomedical Engineering: Imaging Visualization. 6 (1), 1\u20136. https:\/\/doi.org\/10.1080\/21681163.2015.1124249 (2018).","journal-title":"Comput. Methods Biomech. Biomedical Engineering: Imaging Visualization"},{"issue":"1","key":"2617_CR28","doi-asserted-by":"publisher","first-page":"19373","DOI":"10.1038\/s41598-023-46147-3","volume":"13","author":"MH Al-Sheikh","year":"2023","unstructured":"Al-Sheikh, M. H., Dandan, A., Al-Shamayleh, O., Jalab, A. S., Ibrahim, R. W. & H. A., & Multi-class deep learning architecture for classifying lung diseases from chest X-ray and CT images. Sci. Rep. 13 (1), 19373. https:\/\/doi.org\/10.1038\/s41598-023-46147-3 (2023).","journal-title":"Sci. Rep."},{"issue":"9","key":"2617_CR29","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.3390\/biomedicines9091181","volume":"9","author":"X Liu","year":"2021","unstructured":"Liu, X., Shao, C. & Fu, J. Promising biomarkers of radiation-induced lung injury: A review. Biomedicines 9 (9), 1181. https:\/\/doi.org\/10.3390\/biomedicines9091181 (2021).","journal-title":"Biomedicines"},{"issue":"3","key":"2617_CR30","doi-asserted-by":"publisher","first-page":"513","DOI":"10.3892\/or.6.3.513","volume":"6","author":"A Nishioka","year":"1999","unstructured":"Nishioka, A. et al. Analysis of radiation pneumonitis and radiation-induced lung fibrosis in breast cancer patients after breast conservation treatment. Oncol. Rep. 6 (3), 513\u2013517. https:\/\/doi.org\/10.3892\/or.6.3.513 (1999).","journal-title":"Oncol. Rep."},{"issue":"12","key":"2617_CR31","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.1080\/0284186X.2021.1976828","volume":"60","author":"J Karlsen","year":"2021","unstructured":"Karlsen, J. et al. Pneumonitis and fibrosis after breast cancer radiotherapy: occurrence and treatment-related predictors. Acta Oncol. 60 (12), 1651\u20131658. https:\/\/doi.org\/10.1080\/0284186X.2021.1976828 (2021).","journal-title":"Acta Oncol."},{"issue":"6","key":"2617_CR32","doi-asserted-by":"publisher","first-page":"1365","DOI":"10.1007\/s10278-020-00386-2","volume":"33","author":"Y Li","year":"2020","unstructured":"Li, Y. et al. CT image-based texture analysis to predict microvascular invasion in primary hepatocellular carcinoma. J. Digit. Imaging. 33 (6), 1365\u20131375. https:\/\/doi.org\/10.1007\/s10278-020-00386-2 (2020).","journal-title":"J. Digit. Imaging"},{"issue":"18","key":"2617_CR33","doi-asserted-by":"publisher","first-page":"2988","DOI":"10.3390\/diagnostics13182988","volume":"13","author":"T Iwasawa","year":"2023","unstructured":"Iwasawa, T., Matsushita, S., Hirayama, M., Baba, T. & Ogura, T. Quantitative analysis for lung disease on thin-section CT. Diagnostics (Basel). 13 (18), 2988. https:\/\/doi.org\/10.3390\/diagnostics13182988 (2023).","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"2617_CR34","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1148\/radiol.210551","volume":"303","author":"B Jiang","year":"2022","unstructured":"Jiang, B. et al. Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT. Radiology 303 (1), 202\u2013212. https:\/\/doi.org\/10.1148\/radiol.210551 (2022).","journal-title":"Radiology"},{"issue":"12","key":"2617_CR35","doi-asserted-by":"publisher","first-page":"8716","DOI":"10.1007\/s00330-022-08869-2","volume":"32","author":"D Park","year":"2022","unstructured":"Park, D. et al. Importance of CT image normalization in radiomics analysis: prediction of 3-year recurrence-free survival in non-small cell lung cancer. Eur. Radiol. 32 (12), 8716\u20138725. https:\/\/doi.org\/10.1007\/s00330-022-08869-2 (2022).","journal-title":"Eur. Radiol."},{"key":"2617_CR36","doi-asserted-by":"publisher","first-page":"920457","DOI":"10.3389\/fsurg.2022.920457","volume":"9","author":"S Krass","year":"2022","unstructured":"Krass, S., Lassen-Schmidt, B. & Schenk, A. Computer-assisted image-based risk analysis and planning in lung surgery\u2014A review. Front. Surg. 9, 920457. https:\/\/doi.org\/10.3389\/fsurg.2022.920457 (2022).","journal-title":"Front. Surg."},{"issue":"1","key":"2617_CR37","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.rdc.2024.09.004","volume":"51","author":"S Simpson","year":"2025","unstructured":"Simpson, S., Hershman, M., Nachiappan, A. C., Raptis, C. & Hammer, M. M. The short and long of COVID-19: A review of acute and chronic radiologic pulmonary manifestations of SARS-CoV-2 and their clinical significance. Rheumatic Disease Clin. North. Am. 51 (1), 157\u2013187. https:\/\/doi.org\/10.1016\/j.rdc.2024.09.004 (2025).","journal-title":"Rheumatic Disease Clin. North. Am."},{"key":"2617_CR38","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.2147\/CMAR.S473653","volume":"16","author":"S Gegin","year":"2024","unstructured":"Gegin, S., Pazarl\u0131, A. C., \u00d6zdemir, B., \u00d6zdemir, L. & Aksu, E. A. The effect of Hounsfield unit value on the differentiation of malignant\/benign mediastinal lymphadenopathy and masses diagnosed by endobronchial ultrasonography. Cancer Manage. Res. 16, 1013\u20131020. https:\/\/doi.org\/10.2147\/CMAR.S473653 (2024).","journal-title":"Cancer Manage. Res."},{"issue":"11","key":"2617_CR39","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1111\/resp.14582","volume":"28","author":"M O\u2019Callaghan","year":"2023","unstructured":"O\u2019Callaghan, M. et al. Analysis of tissue lipidomics and computed tomography pulmonary fat Attenuation volume (CTPFAV) in idiopathic pulmonary fibrosis. Respirology 28 (11), 1043\u20131052. https:\/\/doi.org\/10.1111\/resp.14582 (2023).","journal-title":"Respirology"},{"issue":"1","key":"2617_CR40","doi-asserted-by":"publisher","first-page":"13156","DOI":"10.1038\/s41598-021-92609-x","volume":"11","author":"AE Wuschner","year":"2021","unstructured":"Wuschner, A. E. et al. Radiation-induced Hounsfield unit change correlates with dynamic CT perfusion better than 4DCT-based ventilation measures in a novel-swine model. Sci. Rep. 11 (1), 13156. https:\/\/doi.org\/10.1038\/s41598-021-92609-x (2021).","journal-title":"Sci. Rep."},{"issue":"1","key":"2617_CR41","doi-asserted-by":"publisher","first-page":"15731","DOI":"10.1038\/s41598-024-64778-y","volume":"14","author":"T Kadeethum","year":"2024","unstructured":"Kadeethum, T., O\u2019Malley, D., Choi, Y., Viswanathan, H. S. & Yoon, H. Progressive transfer learning for advancing machine learning-based reduced-order modeling. Sci. Rep. 14 (1), 15731. https:\/\/doi.org\/10.1038\/s41598-024-64778-y (2024).","journal-title":"Sci. Rep."},{"issue":"1","key":"2617_CR42","doi-asserted-by":"publisher","first-page":"24256","DOI":"10.1038\/s41598-024-75993-y","volume":"14","author":"OGG Drayson","year":"2024","unstructured":"Drayson, O. G. G., Montay-Gruel, P. & Limoli, C. L. Radiomics approach for identifying radiation-induced normal tissue toxicity in the lung. Sci. Rep. 14 (1), 24256. https:\/\/doi.org\/10.1038\/s41598-024-75993-y (2024).","journal-title":"Sci. Rep."},{"issue":"36","key":"2617_CR43","doi-asserted-by":"publisher","first-page":"4431","DOI":"10.1200\/JCO.2016.69.1188","volume":"34","author":"A Recht","year":"2016","unstructured":"Recht, A. et al. Postmastectomy radiotherapy: an American society of clinical oncology, American society for radiation oncology, and society of surgical oncology focused guideline update. J. Clin. Oncol. 34 (36), 4431\u20134442. https:\/\/doi.org\/10.1200\/JCO.2016.69.1188 (2016).","journal-title":"J. Clin. Oncol."},{"issue":"2","key":"2617_CR44","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1515\/ijb-2021-0014","volume":"18","author":"Y Cho","year":"2022","unstructured":"Cho, Y., Molinaro, A. M., Hu, C. & Strawderman, R. L. Regression trees and ensembles for cumulative incidence functions. Int. J. Biostatistics. 18 (2), 397\u2013419. https:\/\/doi.org\/10.1515\/ijb-2021-0014 (2022).","journal-title":"Int. J. Biostatistics"},{"issue":"1","key":"2617_CR45","doi-asserted-by":"publisher","first-page":"5570","DOI":"10.1038\/s41467-023-41215-8","volume":"14","author":"A Radhakrishnan","year":"2023","unstructured":"Radhakrishnan, A., Ruiz Luyten, M., Prasad, N. & Uhler, C. Transfer learning with kernel methods. Nat. Commun. 14 (1), 5570. https:\/\/doi.org\/10.1038\/s41467-023-41215-8 (2023).","journal-title":"Nat. Commun."},{"issue":"11","key":"2617_CR46","doi-asserted-by":"publisher","first-page":"16720","DOI":"10.1109\/TNNLS.2023.3297261","volume":"35","author":"J Wang","year":"2024","unstructured":"Wang, J. & Geng, X. Large margin weighted k-nearest neighbors label distribution learning for classification. IEEE Trans. Neural Networks Learn. Syst. 35 (11), 16720\u201316732. https:\/\/doi.org\/10.1109\/TNNLS.2023.3297261 (2024).","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"2617_CR47","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.radonc.2021.07.015","volume":"162","author":"O Kaidar-Person","year":"2021","unstructured":"Kaidar-Person, O. et al. Tricks and tips for target volume definition and delineation in breast cancer: lessons learned from ESTRO breast courses. Radiother. Oncol. 162, 185\u2013194. https:\/\/doi.org\/10.1016\/j.radonc.2021.07.015 (2021).","journal-title":"Radiother. Oncol."},{"issue":"31","key":"2617_CR48","doi-asserted-by":"publisher","first-page":"3615","DOI":"10.1200\/JCO.20.01363","volume":"38","author":"BV Offersen","year":"2020","unstructured":"Offersen, B. V. et al. Hypofractionated versus standard fractionated radiotherapy in patients with early breast cancer or ductal carcinoma in situ in a randomized phase III trial: the DBCG HYPO trial. J. Clin. Oncol. 38 (31), 3615\u20133625. https:\/\/doi.org\/10.1200\/JCO.20.01363 (2020).","journal-title":"J. Clin. Oncol."},{"issue":"6","key":"2617_CR49","doi-asserted-by":"publisher","first-page":"e613","DOI":"10.1200\/JOP.2015.008508","volume":"12","author":"RA Freedman","year":"2016","unstructured":"Freedman, R. A. et al. Association of breast cancer knowledge with receipt of guideline-recommended breast cancer treatment. J. Oncol. Pract. 12 (6), e613\u2013e625. https:\/\/doi.org\/10.1200\/JOP.2015.008508 (2016).","journal-title":"J. Oncol. Pract."},{"key":"2617_CR50","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.breast.2019.08.001","volume":"48","author":"TRM Andrade","year":"2019","unstructured":"Andrade, T. R. M. et al. Meta-analysis of long-term efficacy and safety of hypofractionated radiotherapy in the treatment of early breast cancer. Breast 48, 24\u201331. https:\/\/doi.org\/10.1016\/j.breast.2019.08.001 (2019).","journal-title":"Breast"},{"key":"2617_CR51","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.breast.2018.07.002","volume":"41","author":"K Rock","year":"2018","unstructured":"Rock, K. et al. Local control in young women with early-stage breast cancer treated with hypofractionated whole breast irradiation. Breast 41, 89\u201392. https:\/\/doi.org\/10.1016\/j.breast.2018.07.002 (2018).","journal-title":"Breast"},{"issue":"6","key":"2617_CR52","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1056\/NEJMoa0906260","volume":"362","author":"TJ Whelan","year":"2010","unstructured":"Whelan, T. J. et al. Long-term results of hypofractionated radiation therapy for breast cancer. N. Engl. J. Med. 362 (6), 513\u2013520. https:\/\/doi.org\/10.1056\/NEJMoa0906260 (2010).","journal-title":"N. Engl. J. Med."},{"issue":"3","key":"2617_CR53","doi-asserted-by":"publisher","first-page":"458","DOI":"10.5603\/RPOR.a2022.0043","volume":"27","author":"B Bukovszky","year":"2022","unstructured":"Bukovszky, B. et al. Radiotherapy instead of axillary lymph node dissection: evaluation of axillary lymph node dose coverage with whole breast radiotherapy. Rep. Practical Oncol. Radiotherapy. 27 (3), 458\u2013466. https:\/\/doi.org\/10.5603\/RPOR.a2022.0043 (2022).","journal-title":"Rep. Practical Oncol. Radiotherapy"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-02617-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-02617-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-02617-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T13:03:21Z","timestamp":1748351001000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-02617-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,27]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2617"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-02617-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,27]]},"assertion":[{"value":"30 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study was approved by the Regional and Institutional Research Ethics Committee of the Markusovszky University Teaching Hospital of Szombathely on 19 September 2022, under the protocol No. 26\/2022. Reporting of all experimental procedures complied with recommendations in committee based on the ICH-GCP guidelines.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}],"article-number":"18473"}}