{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,8]],"date-time":"2026-11-08T00:54:39Z","timestamp":1794099279642,"version":"4.8.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Deutsches Krebsforschungszentrum (DKFZ)"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Strahlenther Onkol"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) systems may personalise radiotherapy by assessing complex and multifaceted patient data and predicting tumour and normal tissue responses to radiotherapy. Here we describe three distinct generations of AI systems, namely personalised radiotherapy based on pretreatment data, response-driven radiotherapy and dynamically optimised radiotherapy. Finally, we discuss the main challenges in clinical translation of AI systems for radiotherapy personalisation.<\/jats:p>","DOI":"10.1007\/s00066-024-02281-z","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T07:03:18Z","timestamp":1725001398000},"page":"266-273","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Artificial intelligence for response prediction and personalisation in radiation oncology"],"prefix":"10.1007","volume":"201","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0342-9545","authenticated-orcid":false,"given":"Alex","family":"Zwanenburg","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4353-3360","authenticated-orcid":false,"given":"Gareth","family":"Price","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7017-3738","authenticated-orcid":false,"given":"Steffen","family":"L\u00f6ck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"2281_CR1","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1038\/nrc1451","volume":"4","author":"J Bernier","year":"2004","unstructured":"Bernier\u00a0J, Hall\u00a0EJ, Giaccia\u00a0A (2004) Radiation oncology: a\u00a0century of achievements. Nat Rev Cancer 4:737\u2013747. https:\/\/doi.org\/10.1038\/nrc1451","journal-title":"Nat Rev Cancer"},{"key":"2281_CR2","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1200\/JCO.19.01162","volume":"38","author":"JD Bradley","year":"2020","unstructured":"Bradley\u00a0JD, Hu\u00a0C, Komaki\u00a0RR et\u00a0al (2020) Long-term results of NRG oncology RTOG 0617: standard- versus high-dose chemoradiotherapy with or without cetuximab for unresectable stage\u00a0III non-small-cell lung cancer. J\u00a0Clin Oncol 38:706\u2013714. https:\/\/doi.org\/10.1200\/JCO.19.01162","journal-title":"J Clin Oncol"},{"key":"2281_CR3","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1038\/nrc.2016.18","volume":"16","author":"M Baumann","year":"2016","unstructured":"Baumann\u00a0M, Krause\u00a0M, Overgaard\u00a0J et\u00a0al (2016) Radiation oncology in the era of precision medicine. Nat Rev Cancer 16:234\u2013249. https:\/\/doi.org\/10.1038\/nrc.2016.18","journal-title":"Nat Rev Cancer"},{"key":"2281_CR4","doi-asserted-by":"publisher","first-page":"1944","DOI":"10.1056\/NEJMoa065497","volume":"356","author":"G D\u2019Souza","year":"2007","unstructured":"D\u2019Souza\u00a0G, Kreimer\u00a0AR, Viscidi\u00a0R et\u00a0al (2007) Case-control study of human papillomavirus and oropharyngeal cancer. N\u00a0Engl J Med 356:1944\u20131956. https:\/\/doi.org\/10.1056\/NEJMoa065497","journal-title":"N Engl J Med"},{"key":"2281_CR5","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1186\/s13014-020-01503-9","volume":"15","author":"M Chen","year":"2020","unstructured":"Chen\u00a0M, Yang\u00a0J, Liao\u00a0Z et\u00a0al (2020) Anatomic change over the course of treatment for non-small cell lung cancer patients and its impact on intensity-modulated radiation therapy and passive-scattering proton therapy deliveries. Radiat Oncol 15:55. https:\/\/doi.org\/10.1186\/s13014-020-01503-9","journal-title":"Radiat Oncol"},{"key":"2281_CR6","doi-asserted-by":"publisher","first-page":"95","DOI":"10.14338\/20-00092","volume":"8","author":"S Stieb","year":"2021","unstructured":"Stieb\u00a0S, Lee\u00a0A, van Dijk\u00a0LV et\u00a0al (2021) NTCP modeling of late effects for head and neck cancer: a\u00a0systematic review. Int J Part Ther 8:95\u2013107. https:\/\/doi.org\/10.14338\/20-00092","journal-title":"Int J Part Ther"},{"key":"2281_CR7","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/978-3-031-08601-4_13","volume-title":"Image-guided high-precision radiotherapy","author":"A Dutz","year":"2022","unstructured":"Dutz\u00a0A, Zwanenburg\u00a0A, Langendijk\u00a0JA, L\u00f6ck\u00a0S (2022) Modelling for radiation treatment outcome. In: Troost\u00a0EGC (ed) Image-guided high-precision radiotherapy. Springer, Cham, pp\u00a0285\u2013314"},{"key":"2281_CR8","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.clon.2021.12.007","volume":"34","author":"LJ Boersma","year":"2022","unstructured":"Boersma\u00a0LJ, Sattler\u00a0MGA, Maduro\u00a0JH et\u00a0al (2022) Model-based selection for proton therapy in breast cancer: development of the national indication protocol for proton therapy and first clinical experiences. Clin Oncol 34:247\u2013257. https:\/\/doi.org\/10.1016\/j.clon.2021.12.007","journal-title":"Clin Oncol"},{"key":"2281_CR9","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1016\/j.ijrobp.2009.09.040","volume":"76","author":"SM Bentzen","year":"2010","unstructured":"Bentzen\u00a0SM, Constine\u00a0LS, Deasy\u00a0JO et\u00a0al (2010) Quantitative analyses of normal tissue effects in the clinic (QUANTEC): an introduction to the scientific issues. Int J Radiat Oncol Biol Phys 76:S3\u2013S9. https:\/\/doi.org\/10.1016\/j.ijrobp.2009.09.040","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"2281_CR10","doi-asserted-by":"publisher","first-page":"249","DOI":"10.2147\/PROM.S256567","volume":"13","author":"LM Minasian","year":"2022","unstructured":"Minasian\u00a0LM, O\u2019Mara\u00a0A, Mitchell\u00a0SA (2022) Clinician and patient reporting of symptomatic adverse events in cancer clinical trials: using CTCAE and PRO-CTCAE\u00ae to provide two distinct and complementary perspectives. Patient Relat Outcome Meas 13:249\u2013258. https:\/\/doi.org\/10.2147\/PROM.S256567","journal-title":"Patient Relat Outcome Meas"},{"key":"2281_CR11","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/j.radonc.2018.06.012","volume":"128","author":"LV van Dijk","year":"2018","unstructured":"van Dijk\u00a0LV, Thor\u00a0M, Steenbakkers\u00a0RJHM et\u00a0al (2018) Parotid gland fat related magnetic resonance image biomarkers improve prediction of late radiation-induced xerostomia. Radiother Oncol 128:459\u2013466. https:\/\/doi.org\/10.1016\/j.radonc.2018.06.012","journal-title":"Radiother Oncol"},{"key":"2281_CR12","volume-title":"Applied predictive modeling","author":"M Kuhn","year":"2018","unstructured":"Kuhn\u00a0M, Johnson\u00a0K (2018) Applied predictive modeling. Springer, New York"},{"key":"2281_CR13","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1118\/1.3521406","volume":"38","author":"M Zaider","year":"2011","unstructured":"Zaider\u00a0M, Hanin\u00a0L (2011) Tumor control probability in radiation treatment. Med Phys 38:574\u2013583. https:\/\/doi.org\/10.1118\/1.3521406","journal-title":"Med Phys"},{"key":"2281_CR14","volume-title":"ICRU report 62, prescribing, recording and reporting photon beam therapy (supplement to ICRU\u00a050)","author":"International Commission on Radiation Units and Measurements","year":"1999","unstructured":"International Commission on Radiation Units and Measurements (1999) ICRU report 62, prescribing, recording and reporting photon beam therapy (supplement to ICRU\u00a050). ICRU"},{"key":"2281_CR15","volume-title":"ICRU report 78, prescribing, recording, and reporting proton-beam therapy","author":"International Commission on Radiation Units and Measurements","year":"2007","unstructured":"International Commission on Radiation Units and Measurements (2007) ICRU report 78, prescribing, recording, and reporting proton-beam therapy. ICRU"},{"key":"2281_CR16","volume-title":"ICRU report 83 prescribing, recording, and reporting photon-beam intensity-modulated radiation therapy (IMRT)","author":"International Commission on Radiation Units and Measurements","year":"2010","unstructured":"International Commission on Radiation Units and Measurements (2010) ICRU report 83 prescribing, recording, and reporting photon-beam intensity-modulated radiation therapy (IMRT). ICRU"},{"key":"2281_CR17","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.semradonc.2019.02.011","volume":"29","author":"KK Brock","year":"2019","unstructured":"Brock\u00a0KK (2019) Adaptive radiotherapy: moving into the future. Semin Radiat Oncol 29:181\u2013184. https:\/\/doi.org\/10.1016\/j.semradonc.2019.02.011","journal-title":"Semin Radiat Oncol"},{"key":"2281_CR18","doi-asserted-by":"publisher","first-page":"2536","DOI":"10.1109\/TMI.2017.2708987","volume":"36","author":"JM Wolterink","year":"2017","unstructured":"Wolterink\u00a0JM, Leiner\u00a0T, Viergever\u00a0MA, Isgum\u00a0I (2017) Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging 36:2536\u20132545. https:\/\/doi.org\/10.1109\/TMI.2017.2708987","journal-title":"IEEE Trans Med Imaging"},{"key":"2281_CR19","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1002\/acm2.12937","volume":"21","author":"Y Xing","year":"2020","unstructured":"Xing\u00a0Y, Zhang\u00a0Y, Nguyen\u00a0D et\u00a0al (2020) Boosting radiotherapy dose calculation accuracy with deep learning. J\u00a0Appl Clin Med Phys 21:149\u2013159. https:\/\/doi.org\/10.1002\/acm2.12937","journal-title":"J Appl Clin Med Phys"},{"key":"2281_CR20","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1111\/1754-9485.13285","volume":"65","author":"A Mylonas","year":"2021","unstructured":"Mylonas\u00a0A, Booth\u00a0J, Nguyen\u00a0DT (2021) A review of artificial intelligence applications for motion tracking in radiotherapy. J\u00a0Med Imaging Radiat Oncol 65:596\u2013611. https:\/\/doi.org\/10.1111\/1754-9485.13285","journal-title":"J Med Imaging Radiat Oncol"},{"key":"2281_CR21","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.radonc.2018.07.020","volume":"130","author":"S Leger","year":"2019","unstructured":"Leger\u00a0S, Zwanenburg\u00a0A, Pilz\u00a0K et\u00a0al (2019) CT imaging during treatment improves radiomic models for patients with locally advanced head and neck cancer. Radiother Oncol 130:10\u201317. https:\/\/doi.org\/10.1016\/j.radonc.2018.07.020","journal-title":"Radiother Oncol"},{"key":"2281_CR22","doi-asserted-by":"publisher","first-page":"673","DOI":"10.3390\/cancers15030673","volume":"15","author":"S Starke","year":"2023","unstructured":"Starke\u00a0S, Zwanenburg\u00a0A, Leger\u00a0K et\u00a0al (2023) Longitudinal and multimodal radiomics models for head and neck cancer outcome prediction. Cancers 15:673. https:\/\/doi.org\/10.3390\/cancers15030673","journal-title":"Cancers"},{"key":"2281_CR23","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.radonc.2019.01.010","volume":"134","author":"A Ajdari","year":"2019","unstructured":"Ajdari\u00a0A, Niyazi\u00a0M, Nicolay\u00a0NH et\u00a0al (2019) Towards optimal stopping in radiation therapy. Radiother Oncol 134:96\u2013100. https:\/\/doi.org\/10.1016\/j.radonc.2019.01.010","journal-title":"Radiother Oncol"},{"key":"2281_CR24","doi-asserted-by":"publisher","first-page":"4675","DOI":"10.1158\/0008-5472.CAN-15-1337","volume":"75","author":"PM Enriquez-Navas","year":"2015","unstructured":"Enriquez-Navas\u00a0PM, Wojtkowiak\u00a0JW, Gatenby\u00a0RA (2015) Application of evolutionary principles to cancer therapy. Cancer Res 75:4675\u20134680. https:\/\/doi.org\/10.1158\/0008-5472.CAN-15-1337","journal-title":"Cancer Res"},{"key":"2281_CR25","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1038\/s41571-020-0411-1","volume":"17","author":"RA Gatenby","year":"2020","unstructured":"Gatenby\u00a0RA, Brown\u00a0JS (2020) Integrating evolutionary dynamics into cancer therapy. Nat Rev Clin Oncol 17:675\u2013686. https:\/\/doi.org\/10.1038\/s41571-020-0411-1","journal-title":"Nat Rev Clin Oncol"},{"key":"2281_CR26","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.ejca.2008.10.026","volume":"45","author":"EA Eisenhauer","year":"2009","unstructured":"Eisenhauer\u00a0EA, Therasse\u00a0P, Bogaerts\u00a0J et\u00a0al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version\u00a01.1). Eur J Cancer 45:228\u2013247. https:\/\/doi.org\/10.1016\/j.ejca.2008.10.026","journal-title":"Eur J Cancer"},{"key":"2281_CR27","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1136\/thoraxjnl-2021-217808","volume":"77","author":"AC Kidd","year":"2022","unstructured":"Kidd\u00a0AC, Anderson\u00a0O, Cowell\u00a0GW et\u00a0al (2022) Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: validation and comparison with modified RECIST response criteria. Thorax 77:1251\u20131259. https:\/\/doi.org\/10.1136\/thoraxjnl-2021-217808","journal-title":"Thorax"},{"key":"2281_CR28","doi-asserted-by":"publisher","first-page":"9320","DOI":"10.1007\/s00330-023-09926-0","volume":"33","author":"L Joskowicz","year":"2023","unstructured":"Joskowicz\u00a0L, Szeskin\u00a0A, Rochman\u00a0S et\u00a0al (2023) Follow-up of liver metastases: a\u00a0comparison of deep learning and RECIST\u00a01.1. Eur Radiol 33:9320\u20139327. https:\/\/doi.org\/10.1007\/s00330-023-09926-0","journal-title":"Eur Radiol"},{"key":"2281_CR29","doi-asserted-by":"publisher","first-page":"e13545","DOI":"10.1200\/JCO.2023.41.16_suppl.e13545","volume":"41","author":"A Gong","year":"2023","unstructured":"Gong\u00a0A, Daly\u00a0M, Melendez-Corres\u00a0G et\u00a0al (2023) Toward automating RECIST\u00a01.1: Improving AI new lesion detection with longitudinal image data. J\u00a0Clin Orthod 41:e13545\u2013e13545. https:\/\/doi.org\/10.1200\/JCO.2023.41.16_suppl.e13545","journal-title":"J Clin Orthod"},{"key":"2281_CR30","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1002\/jmri.26171","volume":"48","author":"SC Thust","year":"2018","unstructured":"Thust\u00a0SC, van den Bent\u00a0MJ, Smits\u00a0M (2018) Pseudoprogression of brain tumors. J\u00a0Magn Reson Imaging 48:571\u2013589. https:\/\/doi.org\/10.1002\/jmri.26171","journal-title":"J Magn Reson Imaging"},{"key":"2281_CR31","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1148\/radiol.2020200443","volume":"297","author":"HJ Park","year":"2020","unstructured":"Park\u00a0HJ, Kim\u00a0KW, Pyo\u00a0J et\u00a0al (2020) Incidence of pseudoprogression during immune checkpoint inhibitor therapy for solid tumors: a\u00a0systematic review and meta-analysis. Radiology 297:87\u201396. https:\/\/doi.org\/10.1148\/radiol.2020200443","journal-title":"Radiology"},{"key":"2281_CR32","doi-asserted-by":"publisher","first-page":"2625","DOI":"10.1002\/cncr.32790","volume":"126","author":"H Akbari","year":"2020","unstructured":"Akbari\u00a0H, Rathore\u00a0S, Bakas\u00a0S et\u00a0al (2020) Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma. Cancer 126:2625\u20132636. https:\/\/doi.org\/10.1002\/cncr.32790","journal-title":"Cancer"},{"key":"2281_CR33","doi-asserted-by":"publisher","DOI":"10.3390\/jpm11080745","author":"MN Kamel Boulos","year":"2021","unstructured":"Kamel Boulos\u00a0MN, Zhang\u00a0P (2021) Digital twins: from personalised medicine to precision public health. J\u00a0Pers Med. https:\/\/doi.org\/10.3390\/jpm11080745","journal-title":"J Pers Med"},{"key":"2281_CR34","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1038\/s41571-022-00709-y","volume":"20","author":"JM Price","year":"2023","unstructured":"Price\u00a0JM, Prabhakaran\u00a0A, West\u00a0CML (2023) Predicting tumour radiosensitivity to deliver precision radiotherapy. Nat Rev Clin Oncol 20:83\u201398. https:\/\/doi.org\/10.1038\/s41571-022-00709-y","journal-title":"Nat Rev Clin Oncol"},{"key":"2281_CR35","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3389\/fonc.2018.00035","volume":"8","author":"HS Gabry\u015b","year":"2018","unstructured":"Gabry\u015b\u00a0HS, Buettner\u00a0F, Sterzing\u00a0F et\u00a0al (2018) Design and selection of machine learning methods using radiomics and dosiomics for normal tissue complication probability modeling of xerostomia. Front Oncol 8:35. https:\/\/doi.org\/10.3389\/fonc.2018.00035","journal-title":"Front Oncol"},{"key":"2281_CR36","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1080\/09553002.2017.1304590","volume":"93","author":"K Jelonek","year":"2017","unstructured":"Jelonek\u00a0K, Pietrowska\u00a0M, Widlak\u00a0P (2017) Systemic effects of ionizing radiation at the proteome and metabolome levels in the blood of cancer patients treated with radiotherapy: the influence of inflammation and radiation toxicity. Int J Radiat Biol 93:683\u2013696. https:\/\/doi.org\/10.1080\/09553002.2017.1304590","journal-title":"Int J Radiat Biol"},{"key":"2281_CR37","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1007\/s13244-018-0657-7","volume":"9","author":"E Neri","year":"2018","unstructured":"Neri\u00a0E, Del Re\u00a0M, Paiar\u00a0F et\u00a0al (2018) Radiomics and liquid biopsy in oncology: the holons of systems medicine. Insights Imaging 9:915\u2013924. https:\/\/doi.org\/10.1007\/s13244-018-0657-7","journal-title":"Insights Imaging"},{"key":"2281_CR38","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1038\/s41416-023-02398-5","volume":"129","author":"DB McLaren","year":"2023","unstructured":"McLaren\u00a0DB, Aitman\u00a0TJ (2023) Redefining precision radiotherapy through liquid biopsy. Br J Cancer 129:900\u2013903. https:\/\/doi.org\/10.1038\/s41416-023-02398-5","journal-title":"Br J Cancer"},{"key":"2281_CR39","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","volume":"17","author":"CJ Kelly","year":"2019","unstructured":"Kelly\u00a0CJ, Karthikesalingam\u00a0A, Suleyman\u00a0M et\u00a0al (2019) Key challenges for delivering clinical impact with artificial intelligence. BMC Med 17:195. https:\/\/doi.org\/10.1186\/s12916-019-1426-2","journal-title":"BMC Med"},{"key":"2281_CR40","doi-asserted-by":"publisher","first-page":"20180642","DOI":"10.1259\/bjr.20180642","volume":"92","author":"JPB O\u2019Connor","year":"2019","unstructured":"O\u2019Connor\u00a0JPB, Robinson\u00a0SP, Waterton\u00a0JC (2019) Imaging tumour hypoxia with oxygen-enhanced MRI and BOLD MRI. Br J Radiol 92:20180642. https:\/\/doi.org\/10.1259\/bjr.20180642","journal-title":"Br J Radiol"},{"key":"2281_CR41","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1053\/j.semnuclmed.2020.05.003","volume":"50","author":"M Busk","year":"2020","unstructured":"Busk\u00a0M, Overgaard\u00a0J, Horsman\u00a0MR (2020) Imaging of tumor hypoxia for radiotherapy: current status and future directions. Semin Nucl Med 50:562\u2013583. https:\/\/doi.org\/10.1053\/j.semnuclmed.2020.05.003","journal-title":"Semin Nucl Med"},{"key":"2281_CR42","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.3389\/fimmu.2020.01067","volume":"11","author":"CE McCarthy","year":"2020","unstructured":"McCarthy\u00a0CE, White\u00a0JM, Viola\u00a0NT, Gibson\u00a0HM (2020) In vivo imaging technologies to monitor the immune system. Front Immunol 11:1067. https:\/\/doi.org\/10.3389\/fimmu.2020.01067","journal-title":"Front Immunol"},{"key":"2281_CR43","doi-asserted-by":"publisher","first-page":"S155","DOI":"10.1016\/j.ijrobp.2009.08.074","volume":"76","author":"A Jackson","year":"2010","unstructured":"Jackson\u00a0A, Marks\u00a0LB, Bentzen\u00a0SM et\u00a0al (2010) The lessons of QUANTEC: recommendations for reporting and gathering data on dose-volume dependencies of treatment outcome. Int J Radiat Oncol Biol Phys 76:S155\u201360. https:\/\/doi.org\/10.1016\/j.ijrobp.2009.08.074","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"2281_CR44","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1186\/s13059-021-02528-3","volume":"22","author":"TR Mercer","year":"2021","unstructured":"Mercer\u00a0TR, Xu\u00a0J, Mason\u00a0CE et\u00a0al (2021) The sequencing quality control 2\u00a0study: establishing community standards for sequencing in precision medicine. Genome Biol 22:306. https:\/\/doi.org\/10.1186\/s13059-021-02528-3","journal-title":"Genome Biol"},{"key":"2281_CR45","doi-asserted-by":"publisher","first-page":"8842","DOI":"10.1007\/s00330-023-09921-5","volume":"33","author":"A Zwanenburg","year":"2023","unstructured":"Zwanenburg\u00a0A (2023) Standardisation and harmonisation efforts in quantitative imaging. Eur Radiol 33:8842\u20138843. https:\/\/doi.org\/10.1007\/s00330-023-09921-5","journal-title":"Eur Radiol"},{"key":"2281_CR46","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","volume":"3","author":"N Rieke","year":"2020","unstructured":"Rieke\u00a0N, Hancox\u00a0J, Li\u00a0W et\u00a0al (2020) The future of digital health with federated learning. NPJ Digit Med 3:119. https:\/\/doi.org\/10.1038\/s41746-020-00323-1","journal-title":"NPJ Digit Med"},{"key":"2281_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.clon.2024.03.011","author":"G Price","year":"2024","unstructured":"Price\u00a0G, Peek\u00a0N, Eleftheriou\u00a0I et\u00a0al (2024) An overview of real-world data infrastructure for cancer research. Clin Oncol. https:\/\/doi.org\/10.1016\/j.clon.2024.03.011","journal-title":"Clin Oncol"},{"key":"2281_CR48","doi-asserted-by":"publisher","first-page":"842","DOI":"10.3390\/jpm11090842","volume":"11","author":"SA Mali","year":"2021","unstructured":"Mali\u00a0SA, Ibrahim\u00a0A, Woodruff\u00a0HC et\u00a0al (2021) Making radiomics more reproducible across scanner and imaging protocol variations: a\u00a0review of harmonization methods. J\u00a0Pers Med 11:842. https:\/\/doi.org\/10.3390\/jpm11090842","journal-title":"J Pers Med"},{"key":"2281_CR49","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1200\/JCO.21.01308","volume":"40","author":"DR Spigel","year":"2022","unstructured":"Spigel\u00a0DR, Faivre-Finn\u00a0C, Gray\u00a0JE et\u00a0al (2022) Five-year survival outcomes from the PACIFIC trial: durvalumab after chemoradiotherapy in stage\u00a0III non-small-cell lung cancer. J\u00a0Clin Oncol 40:1301\u20131311. https:\/\/doi.org\/10.1200\/JCO.21.01308","journal-title":"J Clin Oncol"},{"key":"2281_CR50","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.1002\/1878-0261.12751","volume":"14","author":"V Gr\u00e9goire","year":"2020","unstructured":"Gr\u00e9goire\u00a0V, Guckenberger\u00a0M, Haustermans\u00a0K et\u00a0al (2020) Image guidance in radiation therapy for better cure of cancer. Mol Oncol 14:1470\u20131491. https:\/\/doi.org\/10.1002\/1878-0261.12751","journal-title":"Mol Oncol"},{"key":"2281_CR51","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.ejmp.2020.10.031","volume":"80","author":"S Surti","year":"2020","unstructured":"Surti\u00a0S, Karp\u00a0JS (2020) Update on latest advances in time-of-flight PET. Phys Med 80:251\u2013258. https:\/\/doi.org\/10.1016\/j.ejmp.2020.10.031","journal-title":"Phys Med"},{"key":"2281_CR52","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1038\/s41592-022-01730-w","volume":"20","author":"V Marx","year":"2023","unstructured":"Marx\u00a0V (2023) Method of the year: long-read sequencing. Nat Methods 20:6\u201311. https:\/\/doi.org\/10.1038\/s41592-022-01730-w","journal-title":"Nat Methods"},{"key":"2281_CR53","doi-asserted-by":"publisher","first-page":"20220878","DOI":"10.1259\/bjr.20220878","volume":"96","author":"B Sahiner","year":"2023","unstructured":"Sahiner\u00a0B, Chen\u00a0W, Samala\u00a0RK, Petrick\u00a0N (2023) Data drift in medical machine learning: implications and potential remedies. Br J Radiol 96:20220878. https:\/\/doi.org\/10.1259\/bjr.20220878","journal-title":"Br J Radiol"},{"key":"2281_CR54","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1136\/medethics-2020-107166","volume":"48","author":"T Grote","year":"2022","unstructured":"Grote\u00a0T (2022) Randomised controlled trials in medical AI: ethical considerations. J\u00a0Med Ethics 48:899\u2013906. https:\/\/doi.org\/10.1136\/medethics-2020-107166","journal-title":"J Med Ethics"},{"key":"2281_CR55","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.67527","author":"PB Kane","year":"2021","unstructured":"Kane\u00a0PB, Kimmelman\u00a0J (2021) Is preclinical research in cancer biology reproducible enough? Elife. https:\/\/doi.org\/10.7554\/eLife.67527","journal-title":"Elife"},{"key":"2281_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-13967-8","volume":"12","author":"I Shahzadi","year":"2022","unstructured":"Shahzadi\u00a0I, Zwanenburg\u00a0A, Lattermann\u00a0A et\u00a0al (2022) Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models. Sci Rep 12:1\u201315. https:\/\/doi.org\/10.1038\/s41598-022-13967-8","journal-title":"Sci Rep"},{"key":"2281_CR57","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1145\/230538.230561","volume":"14","author":"B Friedman","year":"1996","unstructured":"Friedman\u00a0B, Nissenbaum\u00a0H (1996) Bias in computer systems. ACM Trans Inf Syst Secur 14:330\u2013347. https:\/\/doi.org\/10.1145\/230538.230561","journal-title":"ACM Trans Inf Syst Secur"},{"key":"2281_CR58","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/3287560.3287600","volume-title":"Proceedings of the conference on fairness, accountability, and transparency","author":"B Hutchinson","year":"2019","unstructured":"Hutchinson\u00a0B, Mitchell\u00a0M (2019) 50 years of test (un)fairness: lessons for machine learning. In: Proceedings of the conference on fairness, accountability, and transparency. Association for Computing Machinery, New York, NY, USA, pp\u00a049\u201358"},{"key":"2281_CR59","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1200\/GO.21.00029","volume":"7","author":"M Abdel-Wahab","year":"2021","unstructured":"Abdel-Wahab\u00a0M, Gondhowiardjo\u00a0SS, Rosa\u00a0AA et\u00a0al (2021) Global radiotherapy: current status and future directions-white paper. JCO Glob Oncol 7:827\u2013842. https:\/\/doi.org\/10.1200\/GO.21.00029","journal-title":"JCO Glob Oncol"},{"key":"2281_CR60","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s11060-017-2391-2","volume":"132","author":"S Deb","year":"2017","unstructured":"Deb\u00a0S, Pendharkar\u00a0AV, Schoen\u00a0MK et\u00a0al (2017) The effect of socioeconomic status on gross total resection, radiation therapy and overall survival in patients with gliomas. J\u00a0Neurooncol 132:447\u2013453. https:\/\/doi.org\/10.1007\/s11060-017-2391-2","journal-title":"J Neurooncol"},{"key":"2281_CR61","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1016\/j.ygyno.2017.10.013","volume":"147","author":"JJ Laan","year":"2017","unstructured":"Laan\u00a0JJ, van Lonkhuijzen\u00a0LRCW, van Os\u00a0RM et\u00a0al (2017) Socioeconomic status as an independent risk factor for severe late bowel toxicity after primary radiotherapy for cervical cancer. Gynecol Oncol 147:684\u2013689. https:\/\/doi.org\/10.1016\/j.ygyno.2017.10.013","journal-title":"Gynecol Oncol"},{"key":"2281_CR62","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10420-8","author":"BC Stahl","year":"2023","unstructured":"Stahl\u00a0BC, Antoniou\u00a0J, Bhalla\u00a0N et\u00a0al (2023) A systematic review of artificial intelligence impact assessments. Artif Intell Rev. https:\/\/doi.org\/10.1007\/s10462-023-10420-8","journal-title":"Artif Intell Rev"},{"key":"2281_CR63","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1038\/s41746-024-01064-1","volume":"7","author":"D van de Sande","year":"2024","unstructured":"van de Sande\u00a0D, Chung\u00a0EFF, Oosterhoff\u00a0J et\u00a0al (2024) To warrant clinical adoption AI models require a\u00a0multi-faceted implementation evaluation. NPJ Digit Med 7:58. https:\/\/doi.org\/10.1038\/s41746-024-01064-1","journal-title":"NPJ Digit Med"},{"key":"2281_CR64","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1016\/j.semradonc.2022.06.011","volume":"32","author":"M Claessens","year":"2022","unstructured":"Claessens\u00a0M, Oria\u00a0CS, Brouwer\u00a0CL et\u00a0al (2022) Quality assurance for AI-based applications in radiation therapy. Semin Radiat Oncol 32:421\u2013431. https:\/\/doi.org\/10.1016\/j.semradonc.2022.06.011","journal-title":"Semin Radiat Oncol"},{"key":"2281_CR65","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.radonc.2020.09.008","volume":"153","author":"L Vandewinckele","year":"2020","unstructured":"Vandewinckele\u00a0L, Claessens\u00a0M, Dinkla\u00a0A et\u00a0al (2020) Overview of artificial intelligence-based applications in radiotherapy: recommendations for implementation and quality assurance. Radiother Oncol 153:55\u201366. https:\/\/doi.org\/10.1016\/j.radonc.2020.09.008","journal-title":"Radiother Oncol"},{"key":"2281_CR66","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1038\/s41746-020-0221-y","volume":"3","author":"RT Sutton","year":"2020","unstructured":"Sutton\u00a0RT, Pincock\u00a0D, Baumgart\u00a0DC et\u00a0al (2020) An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med 3:17. https:\/\/doi.org\/10.1038\/s41746-020-0221-y","journal-title":"NPJ Digit Med"}],"container-title":["Strahlentherapie und Onkologie"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00066-024-02281-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00066-024-02281-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00066-024-02281-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T14:10:44Z","timestamp":1739974244000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00066-024-02281-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,30]]},"references-count":66,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["2281"],"URL":"https:\/\/doi.org\/10.1007\/s00066-024-02281-z","relation":{},"ISSN":["0179-7158","1439-099X"],"issn-type":[{"value":"0179-7158","type":"print"},{"value":"1439-099X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,30]]},"assertion":[{"value":"10 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A.\u00a0Zwanenburg, G.\u00a0Price and S.\u00a0L\u00f6ck declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}