{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T08:08:32Z","timestamp":1777536512760,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Prostate cancer is the most frequent cancer in men and a leading cause of cancer death. Determining a patient\u2019s optimal therapy is a challenge, where oncologists must select a therapy with the highest likelihood of success and the lowest likelihood of toxicity. International standards for prognostication rely on non-specific and semi-quantitative tools, commonly leading to over- and under-treatment. Tissue-based molecular biomarkers have attempted to address this, but most have limited validation in prospective randomized trials and expensive processing costs, posing substantial barriers to widespread adoption. There remains a significant need for accurate and scalable tools to support therapy personalization. Here we demonstrate prostate cancer therapy personalization by predicting long-term, clinically relevant outcomes using a multimodal deep learning architecture and train models using clinical data and digital histopathology from prostate biopsies. We train and validate models using five phase III randomized trials conducted across hundreds of clinical centers. Histopathological data was available for 5654 of 7764 randomized patients (71%) with a median follow-up of 11.4\u2009years. Compared to the most common risk-stratification tool\u2014risk groups developed by the National Cancer Center Network (NCCN)\u2014our models have superior discriminatory performance across all endpoints, ranging from 9.2% to 14.6% relative improvement in a held-out validation set. This artificial intelligence-based tool improves prognostication over standard tools and allows oncologists to computationally predict the likeliest outcomes of specific patients to determine optimal treatment. Outfitted with digital scanners and internet access, any clinic could offer such capabilities, enabling global access to therapy personalization.<\/jats:p>","DOI":"10.1038\/s41746-022-00613-w","type":"journal-article","created":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T06:03:24Z","timestamp":1654668204000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":177,"title":["Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials"],"prefix":"10.1038","volume":"5","author":[{"given":"Andre","family":"Esteva","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2041-3104","authenticated-orcid":false,"given":"Jean","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Douwe","family":"van der Wal","sequence":"additional","affiliation":[]},{"given":"Shih-Cheng","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Jeffry P.","family":"Simko","sequence":"additional","affiliation":[]},{"given":"Sandy","family":"DeVries","sequence":"additional","affiliation":[]},{"given":"Emmalyn","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Edward M.","family":"Schaeffer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1140-0603","authenticated-orcid":false,"given":"Todd M.","family":"Morgan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1341-382X","authenticated-orcid":false,"given":"Yilun","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Amirata","family":"Ghorbani","sequence":"additional","affiliation":[]},{"given":"Nikhil","family":"Naik","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0296-8408","authenticated-orcid":false,"given":"Dhruv","family":"Nathawani","sequence":"additional","affiliation":[]},{"given":"Richard","family":"Socher","sequence":"additional","affiliation":[]},{"given":"Jeff M.","family":"Michalski","sequence":"additional","affiliation":[]},{"suffix":"III","given":"Mack","family":"Roach","sequence":"additional","affiliation":[]},{"given":"Thomas M.","family":"Pisansky","sequence":"additional","affiliation":[]},{"given":"Jedidiah M.","family":"Monson","sequence":"additional","affiliation":[]},{"given":"Farah","family":"Naz","sequence":"additional","affiliation":[]},{"given":"James","family":"Wallace","sequence":"additional","affiliation":[]},{"given":"Michelle J.","family":"Ferguson","sequence":"additional","affiliation":[]},{"given":"Jean-Paul","family":"Bahary","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8880-4764","authenticated-orcid":false,"given":"James","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Matthew","family":"Lungren","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0529-0628","authenticated-orcid":false,"given":"Serena","family":"Yeung","sequence":"additional","affiliation":[]},{"given":"Ashley E.","family":"Ross","sequence":"additional","affiliation":[]},{"name":"NRG Prostate Cancer AI Consortium","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Kucharczyk","sequence":"additional","affiliation":[]},{"given":"Luis","family":"Souhami","sequence":"additional","affiliation":[]},{"given":"Leslie","family":"Ballas","sequence":"additional","affiliation":[]},{"given":"Christopher A.","family":"Peters","sequence":"additional","affiliation":[]},{"given":"Sandy","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Alexander G.","family":"Balogh","sequence":"additional","affiliation":[]},{"given":"Pamela D.","family":"Randolph-Jackson","sequence":"additional","affiliation":[]},{"given":"David L.","family":"Schwartz","sequence":"additional","affiliation":[]},{"given":"Michael R.","family":"Girvigian","sequence":"additional","affiliation":[]},{"given":"Naoyuki G.","family":"Saito","sequence":"additional","affiliation":[]},{"given":"Adam","family":"Raben","sequence":"additional","affiliation":[]},{"given":"Rachel A.","family":"Rabinovitch","sequence":"additional","affiliation":[]},{"given":"Khalil","family":"Katato","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8193-5769","authenticated-orcid":false,"given":"Howard M.","family":"Sandler","sequence":"additional","affiliation":[]},{"given":"Phuoc T.","family":"Tran","sequence":"additional","affiliation":[]},{"given":"Daniel E.","family":"Spratt","sequence":"additional","affiliation":[]},{"given":"Stephanie","family":"Pugh","sequence":"additional","affiliation":[]},{"given":"Felix Y.","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Osama","family":"Mohamad","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,8]]},"reference":[{"key":"613_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209\u2013249 (2021).","journal-title":"CA Cancer J. Clin."},{"key":"613_CR2","doi-asserted-by":"publisher","first-page":"620","DOI":"10.6004\/jnccn.2018.0036","volume":"16","author":"PH Carroll","year":"2018","unstructured":"Carroll, P. H. & Mohler, J. L. NCCN Guidelines updates: prostate cancer and prostate cancer early detection. J. Natl Compr. Canc. Netw. 16, 620\u2013623 (2018).","journal-title":"J. Natl Compr. Canc. Netw."},{"key":"613_CR3","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1093\/jnci\/djz106","volume":"111","author":"EM Ward","year":"2019","unstructured":"Ward, E. M. et al. Annual report to the nation on the status of cancer, featuring cancer in men and women age 20\u201349 Years. J. Natl Cancer Inst. 111, 1279\u20131297 (2019).","journal-title":"J. Natl Cancer Inst."},{"key":"613_CR4","doi-asserted-by":"publisher","first-page":"600856","DOI":"10.3389\/fendo.2020.600856","volume":"11","author":"MH Houshdar Tehrani","year":"2020","unstructured":"Houshdar Tehrani, M. H., Gholibeikian, M., Bamoniri, A. & Mirjalili, B. B. F. Cancer treatment by Caryophyllaceae-type cyclopeptides. Front. Endocrinol. 11, 600856 (2020).","journal-title":"Front. Endocrinol."},{"key":"613_CR5","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.juro.2016.08.096","volume":"197","author":"TJ Daskivich","year":"2017","unstructured":"Daskivich, T. J., Wood, L. N., Skarecky, D., Ahlering, T. & Freedland, S. Limitations of the national comprehensive cancer network (NCCN\u00ae) guidelines for prediction of limited life expectancy in men with prostate cancer. J. Urol. 197, 356\u2013362 (2017).","journal-title":"J. Urol."},{"key":"613_CR6","doi-asserted-by":"crossref","first-page":"58","DOI":"10.21147\/j.issn.1000-9604.2016.06.02","volume":"28","author":"N Chen","year":"2016","unstructured":"Chen, N. & Zhou, Q. The evolving Gleason grading system. Chin. J. Cancer Res 28, 58\u201364 (2016).","journal-title":"Chin. J. Cancer Res"},{"key":"613_CR7","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1053\/hupa.2001.21134","volume":"32","author":"WC Allsbrook Jr.","year":"2001","unstructured":"Allsbrook, W. C. Jr. et al. Interobserver reproducibility of Gleason grading of prostatic carcinoma: urologic pathologists. Hum. Pathol. 32, 74\u201380 (2001).","journal-title":"Hum. Pathol."},{"key":"613_CR8","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1053\/hupa.2001.21135","volume":"32","author":"WC Allsbrook Jr.","year":"2001","unstructured":"Allsbrook, W. C. Jr. et al. Interobserver reproducibility of Gleason grading of prostatic carcinoma: general pathologist. Hum. Pathol. 32, 81\u201388 (2001).","journal-title":"Hum. Pathol."},{"key":"613_CR9","doi-asserted-by":"publisher","first-page":"134","DOI":"10.6004\/jnccn.2021.0008","volume":"19","author":"E Schaeffer","year":"2021","unstructured":"Schaeffer, E. et al. NCCN guidelines insights: prostate cancer, version 1.2021: featured updates to the NCCN guidelines. J. Natl Compr. Canc. Netw. 19, 134\u2013143 (2021).","journal-title":"J. Natl Compr. Canc. Netw."},{"key":"613_CR10","doi-asserted-by":"publisher","first-page":"459","DOI":"10.21037\/tau.2018.06.02","volume":"7","author":"Z Kornberg","year":"2018","unstructured":"Kornberg, Z., Cooperberg, M. R., Spratt, D. E. & Feng, F. Y. Genomic biomarkers in prostate cancer. Transl. Androl. Urol. 7, 459\u2013471 (2018).","journal-title":"Transl. Androl. Urol."},{"key":"613_CR11","first-page":"15","volume":"8","author":"P-O Gaudreau","year":"2016","unstructured":"Gaudreau, P.-O., Stagg, J., Souli\u00e8res, D. & Saad, F. The present and future of biomarkers in prostate cancer: proteomics, genomics, and immunology advancements. Biomark. Cancer 8, 15\u201333 (2016).","journal-title":"Biomark. Cancer"},{"key":"613_CR12","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1200\/JOP.19.00752","volume":"16","author":"SE Eggener","year":"2020","unstructured":"Eggener, S. E., Bryan Rumble, R. & Beltran, H. Molecular biomarkers in localized prostate cancer: ASCO guideline summary. JCO Oncol. Pract. 16, 340\u2013343 (2020).","journal-title":"JCO Oncol. Pract."},{"key":"613_CR13","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017).","journal-title":"Nature"},{"key":"613_CR14","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-19334-3","volume":"11","author":"N Naik","year":"2020","unstructured":"Naik, N. et al. Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains. Nat. Commun. 11, 5727 (2020).","journal-title":"Nat. Commun."},{"key":"613_CR15","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1126\/science.aaz3023","volume":"367","author":"D Ho","year":"2020","unstructured":"Ho, D. Artificial intelligence in cancer therapy. Science 367, 982\u2013983 (2020).","journal-title":"Science"},{"key":"613_CR16","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1016\/j.ccell.2021.04.002","volume":"39","author":"BH Kann","year":"2021","unstructured":"Kann, B. H., Hosny, A. & Aerts, H. J. W. L. Artificial intelligence for clinical oncology. Cancer Cell 39, 916\u2013927 (2021).","journal-title":"Cancer Cell"},{"key":"613_CR17","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1038\/s41591-021-01343-4","volume":"27","author":"J van der Laak","year":"2021","unstructured":"van der Laak, J., Litjens, G. & Ciompi, F. Deep learning in histopathology: the path to the clinic. Nat. Med. 27, 775\u2013784 (2021).","journal-title":"Nat. Med."},{"key":"613_CR18","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/s41746-021-00427-2","volume":"4","author":"E Wulczyn","year":"2021","unstructured":"Wulczyn, E. et al. Interpretable survival prediction for colorectal cancer using deep learning. NPJ Digit Med 4, 71 (2021).","journal-title":"NPJ Digit Med"},{"key":"613_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s43856-021-00005-3","volume":"1","author":"E Wulczyn","year":"2021","unstructured":"Wulczyn, E. et al. Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading. Commun. Med. 1, 1\u20138 (2021).","journal-title":"Commun. Med."},{"key":"613_CR20","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1056\/NEJMoa1012348","volume":"365","author":"CU Jones","year":"2011","unstructured":"Jones, C. U. et al. Radiotherapy and short-term androgen deprivation for localized prostate cancer. N. Engl. J. Med. 365, 107\u2013118 (2011).","journal-title":"N. Engl. J. Med."},{"key":"613_CR21","doi-asserted-by":"publisher","first-page":"e180039","DOI":"10.1001\/jamaoncol.2018.0039","volume":"4","author":"JM Michalski","year":"2018","unstructured":"Michalski, J. M. et al. Effect of standard vs dose-escalated radiation therapy for patients with intermediate-risk prostate cancer: the NRG oncology RTOG 0126 randomized clinical trial. JAMA Oncol. 4, e180039 (2018).","journal-title":"JAMA Oncol."},{"key":"613_CR22","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1200\/JCO.2014.58.0662","volume":"33","author":"TM Pisansky","year":"2015","unstructured":"Pisansky, T. M. et al. Duration of androgen suppression before radiotherapy for localized prostate cancer: radiation therapy oncology group randomized clinical trial 9910. J. Clin. Oncol. 33, 332\u2013339 (2015).","journal-title":"J. Clin. Oncol."},{"key":"613_CR23","doi-asserted-by":"publisher","first-page":"2497","DOI":"10.1200\/JCO.2007.14.9021","volume":"26","author":"EM Horwitz","year":"2008","unstructured":"Horwitz, E. M. et al. Ten-year follow-up of radiation therapy oncology group protocol 92-02: a phase III trial of the duration of elective androgen deprivation in locally advanced prostate cancer. J. Clin. Oncol. 26, 2497\u20132504 (2008).","journal-title":"J. Clin. Oncol."},{"key":"613_CR24","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1200\/JCO.2003.05.004","volume":"21","author":"M Roach 3rd","year":"2003","unstructured":"Roach, M. 3rd et al. Phase III trial comparing whole-pelvic versus prostate-only radiotherapy and neoadjuvant versus adjuvant combined androgen suppression: Radiation Therapy Oncology Group 9413. J. Clin. Oncol. 21, 1904\u20131911 (2003).","journal-title":"J. Clin. Oncol."},{"key":"613_CR25","doi-asserted-by":"crossref","unstructured":"Jing, L. & Tian, Y. Self-supervised visual feature learning with deep neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. PP, (2020).","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"613_CR26","doi-asserted-by":"publisher","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. & Gulin, A. CatBoost: unbiased boosting with categorical features. arXiv https:\/\/doi.org\/10.48550\/arXiv.1706.09516 (2017).","DOI":"10.48550\/arXiv.1706.09516"},{"key":"613_CR27","doi-asserted-by":"publisher","unstructured":"McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. arXiv https:\/\/doi.org\/10.48550\/arXiv.1802.03426 (2018).","DOI":"10.48550\/arXiv.1802.03426"},{"key":"613_CR28","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/s10732-014-9275-9","volume":"22","author":"C Fawcett","year":"2016","unstructured":"Fawcett, C. & Hoos, H. H. Analysing differences between algorithm configurations through ablation. J. Heuristics 22, 431\u2013458 (2016).","journal-title":"J. Heuristics"},{"key":"613_CR29","doi-asserted-by":"publisher","first-page":"1372","DOI":"10.1001\/jamaoncol.2020.2485","volume":"6","author":"K Nagpal","year":"2020","unstructured":"Nagpal, K. et al. Development and validation of a deep learning algorithm for gleason grading of prostate cancer from biopsy specimens. JAMA Oncol. 6, 1372\u20131380 (2020).","journal-title":"JAMA Oncol."},{"key":"613_CR30","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/S1470-2045(19)30739-9","volume":"21","author":"W Bulten","year":"2020","unstructured":"Bulten, W. et al. Automated deep-learning system for gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 21, 233\u2013241 (2020).","journal-title":"Lancet Oncol."},{"key":"613_CR31","doi-asserted-by":"crossref","unstructured":"Beede, E. et al. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In Proc. 2020 CHI Conference on Human Factors in Computing Systems 1\u201312 (Association for Computing Machinery, 2020).","DOI":"10.1145\/3313831.3376718"},{"key":"613_CR32","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R. Momentum contrast for unsupervised visual representation learning. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition 9729\u20139738 (IEEE, 2020).","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"613_CR33","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.urology.2016.01.012","volume":"90","author":"EA Klein","year":"2016","unstructured":"Klein, E. A. et al. Decipher genomic classifier measured on prostate biopsy predicts metastasis risk. Urology 90, 148\u2013152 (2016).","journal-title":"Urology"},{"key":"613_CR34","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1200\/JCO.2017.74.2940","volume":"36","author":"DE Spratt","year":"2018","unstructured":"Spratt, D. E. et al. Development and validation of a novel integrated clinical-Genomic risk group classification for localized prostate cancer. J. Clin. Oncol. 36, 581\u2013590 (2018).","journal-title":"J. Clin. Oncol."},{"key":"613_CR35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. IEEE conference on computer vision and pattern recognition 770\u2013778 (IEEE, 2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"613_CR36","doi-asserted-by":"publisher","unstructured":"Chen, X., Fan, H., Girshick, R. & He, K. Improved baselines with momentum contrastive learning. arXiv https:\/\/doi.org\/10.48550\/arXiv.2003.04297 (2020).","DOI":"10.48550\/arXiv.2003.04297"},{"key":"613_CR37","doi-asserted-by":"publisher","first-page":"5381","DOI":"10.1002\/sim.5958","volume":"32","author":"P Blanche","year":"2013","unstructured":"Blanche, P., Dartigues, J.-F. & Jacqmin-Gadda, H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat. Med. 32, 5381\u20135397 (2013).","journal-title":"Stat. Med."}],"updated-by":[{"DOI":"10.1038\/s41746-023-00769-z","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T00:00:00Z","timestamp":1677024000000}}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00613-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00613-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00613-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T12:12:54Z","timestamp":1677067974000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00613-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,8]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["613"],"URL":"https:\/\/doi.org\/10.1038\/s41746-022-00613-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-1573559\/v1","asserted-by":"object"}]},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,8]]},"assertion":[{"value":"5 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2023","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1038\/s41746-023-00769-z","URL":"https:\/\/doi.org\/10.1038\/s41746-023-00769-z","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A.E., D.v.d.W., and E.C. are employees at Artera. A.E., D.v.d.W., D.N., R.S., and N.N are or were employees of Salesforce.com, Inc. F.Y.F. is an advisor to and holds equity in Artera and is a consultant for Janssen, Roivant, Myovant, Bayer, Novartis, Varian, Blue Earth Diagnostics and Exact Sciences. L.S. received travel support and honorarium from Varian Medical Systems and is on the advisory board for AbbVie. M.K. received funding from Limbus AI, is a consultant for Palette Life Sciences, and is on the advisory board for AbbVie, Ferring, Janssen, and TerSera. A.E.R. is a consultant for Astellas, Bayer, Blue Earth, Janssen, Myovant, Pfizer, Progenics, and Veracyte. H.M.S. is a member of the ASTRO Board and a member of the clinical trials steering committee for Janssen. P.T.T. is a consultant for Johnson & Johnson, RefleXion Medical, Myovant, and AstraZeneca. D.E.S. is a consultant for AstraZeneca, Blue Earth, Bayer, Boston Scientific, Gammatile, Janssen, Novartis, and Varian. The remaining authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"71"}}