{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T18:20:03Z","timestamp":1765995603373,"version":"build-2065373602"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"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>This study evaluates the CT-based volumetric sarcopenia index (SI) as a baseline prognostic factor for overall survival (OS) in 10,340 solid tumor patients (40% female). Automated body composition analysis was applied to internal baseline abdomen CTs and to thorax CTs. SI\u2019s prognostic value was assessed using multivariable Cox proportional hazards regression, accelerated failure time models, and gradient-boosted machine learning. External validation included 439 patients (40% female). Higher SI was associated with prolonged OS in the internal abdomen (HR 0.56, 95% CI 0.52\u20130.59; P\u2009&lt;\u20090.001) and thorax cohorts (HR 0.40, 95% CI 0.37\u20130.43; P\u2009&lt;\u20090.001), as well as in the external validation cohort (HR 0.56, 95% CI 0.41\u20130.79; <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001). Machine learning models identified SI as the most important factor in survival prediction. Our results demonstrate SI\u2019s potential as a fully automated body composition feature for standard oncologic workflows.<\/jats:p>","DOI":"10.1038\/s41746-025-02016-z","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T15:29:37Z","timestamp":1760455777000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Sarcopenia index by automated CT body composition analysis for pan cancer prognostic stratification"],"prefix":"10.1038","volume":"8","author":[{"given":"Katarzyna","family":"Borys","sequence":"first","affiliation":[]},{"given":"Johannes","family":"Haubold","sequence":"additional","affiliation":[]},{"given":"Julius","family":"Keyl","sequence":"additional","affiliation":[]},{"given":"Maria A.","family":"Bali","sequence":"additional","affiliation":[]},{"given":"Riccardo","family":"De Angelis","sequence":"additional","affiliation":[]},{"given":"K\u00e9vin Brou","family":"Boni","sequence":"additional","affiliation":[]},{"given":"Nicolas","family":"Coquelet","sequence":"additional","affiliation":[]},{"given":"Judith","family":"Kohnke","sequence":"additional","affiliation":[]},{"given":"Giulia","family":"Baldini","sequence":"additional","affiliation":[]},{"given":"Lennard","family":"Kroll","sequence":"additional","affiliation":[]},{"given":"Sara","family":"Schramm","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Stang","sequence":"additional","affiliation":[]},{"given":"Eugen","family":"Malamutmann","sequence":"additional","affiliation":[]},{"given":"Jens","family":"Kleesiek","sequence":"additional","affiliation":[]},{"given":"Moon","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Stefan","family":"Kasper","sequence":"additional","affiliation":[]},{"given":"Jens T.","family":"Siveke","sequence":"additional","affiliation":[]},{"given":"Marcel","family":"Wiesweg","sequence":"additional","affiliation":[]},{"given":"Anja","family":"Merkel-Jens","sequence":"additional","affiliation":[]},{"given":"Benedikt M.","family":"Schaarschmidt","sequence":"additional","affiliation":[]},{"given":"Viktor","family":"Gruenwald","sequence":"additional","affiliation":[]},{"given":"Sebastian","family":"Bauer","sequence":"additional","affiliation":[]},{"given":"Arzu","family":"Oezcelik","sequence":"additional","affiliation":[]},{"given":"Servet","family":"B\u00f6l\u00fckbas","sequence":"additional","affiliation":[]},{"given":"Ken","family":"Herrmann","sequence":"additional","affiliation":[]},{"given":"Rainer","family":"Kimmig","sequence":"additional","affiliation":[]},{"given":"Stephan","family":"Lang","sequence":"additional","affiliation":[]},{"given":"J\u00fcrgen","family":"Treckmann","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Stuschke","sequence":"additional","affiliation":[]},{"given":"Boris","family":"Hadaschik","sequence":"additional","affiliation":[]},{"given":"Lale","family":"Umutlu","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Forsting","sequence":"additional","affiliation":[]},{"given":"Dirk","family":"Schadendorf","sequence":"additional","affiliation":[]},{"given":"Christoph M.","family":"Friedrich","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Schuler","sequence":"additional","affiliation":[]},{"given":"Ren\u00e9","family":"Hosch","sequence":"additional","affiliation":[]},{"given":"Felix","family":"Nensa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"2016_CR1","doi-asserted-by":"publisher","first-page":"109943","DOI":"10.1016\/j.ejrad.2021.109943","volume":"145","author":"A Tolonen","year":"2021","unstructured":"Tolonen, A. et al. Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review. Eur. J. Radiol. 145, 109943 (2021).","journal-title":"Eur. J. Radiol."},{"key":"2016_CR2","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/S2213-8587(21)00089-9","volume":"9","author":"M Gao","year":"2021","unstructured":"Gao, M. et al. Associations between body-mass index and COVID-19 severity in 6\u00b79 million people in England: a prospective, community-based, cohort study. Lancet Diab Endocrinol. 9, 350\u2013359 (2021).","journal-title":"Lancet Diab Endocrinol."},{"key":"2016_CR3","doi-asserted-by":"publisher","first-page":"718815","DOI":"10.3389\/fonc.2021.718815","volume":"11","author":"M Del Grande","year":"2021","unstructured":"Del Grande, M. et al. Computed tomography\u2013based body composition in patients with ovarian cancer: Association with chemotoxicity and prognosis. Front. Oncol. 11, 718815 (2021).","journal-title":"Front. Oncol."},{"key":"2016_CR4","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1080\/0284186X.2020.1744716","volume":"59","author":"M Shirdel","year":"2020","unstructured":"Shirdel, M. et al. Body composition measured by computed tomography is associated with colorectal cancer survival, also in early-stage disease. Acta Oncol. 59, 799\u2013808 (2020).","journal-title":"Acta Oncol."},{"key":"2016_CR5","doi-asserted-by":"publisher","first-page":"2241","DOI":"10.1245\/s10434-017-5829-z","volume":"24","author":"D Black","year":"2017","unstructured":"Black, D. et al. Prognostic value of computed tomography: Measured parameters of body composition in primary operable gastrointestinal cancers. Ann. Surg. Oncol. 24, 2241\u20132251 (2017).","journal-title":"Ann. Surg. Oncol."},{"key":"2016_CR6","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-019-5319-8","volume":"19","author":"N Charette","year":"2019","unstructured":"Charette, N. et al. Prognostic value of adipose tissue and muscle mass in advanced colorectal cancer: a post hoc analysis of two non-randomized phase II trials. BMC Cancer 19, 134 (2019).","journal-title":"BMC Cancer"},{"key":"2016_CR7","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1093\/jncimonographs\/lgad001","volume":"2023","author":"UA Shah","year":"2023","unstructured":"Shah, U. A. et al. Imaging modalities for measuring body composition in patients with cancer: opportunities and challenges. J. Natl. Cancer Inst. Monogr. 2023, 56\u201367 (2023).","journal-title":"J. Natl. Cancer Inst. Monogr."},{"key":"2016_CR8","doi-asserted-by":"publisher","first-page":"2921","DOI":"10.3390\/cancers13122921","volume":"13","author":"U Fehrenbach","year":"2021","unstructured":"Fehrenbach, U. et al. CT body composition of sarcopenia and sarcopenic obesity: predictors of postoperative complications and survival in patients with locally advanced esophageal adenocarcinoma. Cancers 13, 2921 (2021).","journal-title":"Cancers"},{"key":"2016_CR9","doi-asserted-by":"publisher","first-page":"e2115274","DOI":"10.1001\/jamanetworkopen.2021.15274","volume":"4","author":"CA Fleming","year":"2021","unstructured":"Fleming, C. A. et al. Body composition, inflammation, and 5-year outcomes in colon cancer. JAMA Netw. Open 4, e2115274 (2021).","journal-title":"JAMA Netw. Open"},{"key":"2016_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-65460-9","volume":"10","author":"B Cha","year":"2020","unstructured":"Cha, B., Yu, J. H., Jin, Y.-J., Suh, Y. J. & Lee, J.-W. Survival outcomes according to body mass index in hepatocellular carcinoma patient: Analysis of nationwide cancer registry database. Sci. Rep. 10, 8347 (2020).","journal-title":"Sci. Rep."},{"key":"2016_CR11","doi-asserted-by":"publisher","first-page":"S28","DOI":"10.1016\/j.afos.2021.03.002","volume":"7","author":"PC-M Au","year":"2021","unstructured":"Au, P. C.-M. et al. Sarcopenia and mortality in cancer: A meta-analysis. Osteoporos. Sarcopenia 7, S28\u2013S33 (2021).","journal-title":"Osteoporos. Sarcopenia"},{"key":"2016_CR12","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.tipsro.2020.10.001","volume":"16","author":"M Anjanappa","year":"2020","unstructured":"Anjanappa, M. et al. Sarcopenia in cancer: Risking more than muscle loss. Tech. Innov. Patient Support Radiat. Oncol. 16, 50\u201357 (2020).","journal-title":"Tech. Innov. Patient Support Radiat. Oncol."},{"key":"2016_CR13","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s10549-020-05617-2","volume":"181","author":"MA Franzoi","year":"2020","unstructured":"Franzoi, M. A. et al. Computed tomography-based analyses of baseline body composition parameters and changes in breast cancer patients under treatment with CDK 4\/6 inhibitors. Breast Cancer Res. Treat. 181, 199\u2013209 (2020).","journal-title":"Breast Cancer Res. Treat."},{"key":"2016_CR14","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.2147\/CMAR.S195869","volume":"11","author":"D Portal","year":"2019","unstructured":"Portal, D. et al. L3 skeletal muscle index (L3SMI) is a surrogate marker of sarcopenia and frailty in non-small cell lung cancer patients. Cancer Manag. Res. 11, 2579\u20132588 (2019).","journal-title":"Cancer Manag. Res."},{"key":"2016_CR15","doi-asserted-by":"publisher","first-page":"58","DOI":"10.3945\/ajcn.115.111203","volume":"102","author":"L Schweitzer","year":"2015","unstructured":"Schweitzer, L. et al. What is the best reference site for a single MRI slice to assess whole-body skeletal muscle and adipose tissue volumes in healthy adults?. Am. J. Clin. Nutr. 102, 58\u201365 (2015).","journal-title":"Am. J. Clin. Nutr."},{"key":"2016_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-17611-3","volume":"12","author":"L Kroll","year":"2022","unstructured":"Kroll, L. et al. CT-derived body composition analysis could possibly replace DXA and BIA to monitor NET-patients. Sci. Rep. 12, 13419 (2022).","journal-title":"Sci. Rep."},{"key":"2016_CR17","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.3390\/jcm10051079","volume":"10","author":"S Catanese","year":"2021","unstructured":"Catanese, S. et al. Role of Baseline Computed-Tomography-Evaluated Body Composition in Predicting Outcome and Toxicity from First-Line Therapy in Advanced Gastric Cancer Patients. J. Clin. Med. 10, 1079 (2021).","journal-title":"J. Clin. Med."},{"key":"2016_CR18","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1038\/bjc.2017.149","volume":"117","author":"M Ebadi","year":"2017","unstructured":"Ebadi, M. et al. Subcutaneous adiposity is an independent predictor of mortality in cancer patients. Br. J. Cancer 117, 148\u2013155 (2017).","journal-title":"Br. J. Cancer"},{"key":"2016_CR19","doi-asserted-by":"crossref","unstructured":"Keyl, J. et al. Deep learning-based assessment of body composition and liver tumour burden for survival modelling in advanced colorectal cancer. J. Cachexia Sarcopenia Muscle 14, 545\u2013552 (2023).","DOI":"10.1002\/jcsm.13158"},{"key":"2016_CR20","doi-asserted-by":"publisher","first-page":"109340","DOI":"10.1016\/j.ejrad.2020.109340","volume":"133","author":"A Cromb\u00e9","year":"2020","unstructured":"Cromb\u00e9, A., Kind, M., Toulmonde, M., Italiano, A. & Cousin, S. Impact of CT-based body composition parameters at baseline, their early changes and response in metastatic cancer patients treated with immune checkpoint inhibitors. Eur. J. Radiol. 133, 109340 (2020).","journal-title":"Eur. J. Radiol."},{"key":"2016_CR21","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.1007\/s00330-020-07147-3","volume":"31","author":"S Koitka","year":"2021","unstructured":"Koitka, S., Kroll, L., Malamutmann, E., Oezcelik, A. & Nensa, F. Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks. Eur. Radiol. 31, 1795\u20131804 (2021).","journal-title":"Eur. Radiol."},{"key":"2016_CR22","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1148\/radiol.2018181432","volume":"290","author":"AD Weston","year":"2019","unstructured":"Weston, A. D. et al. Automated abdominal segmentation of CT scans for body composition analysis using deep learning. Radiology 290, 669\u2013679 (2019).","journal-title":"Radiology"},{"key":"2016_CR23","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-00161-5","volume":"11","author":"J Ha","year":"2021","unstructured":"Ha, J. et al. Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography. Sci. Rep. 11, 21656 (2021).","journal-title":"Sci. Rep."},{"key":"2016_CR24","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1002\/jcsm.13404","volume":"15","author":"A Bimurzayeva","year":"2024","unstructured":"Bimurzayeva, A. et al. Three-dimensional body composition parameters using automatic volumetric segmentation allow accurate prediction of colorectal cancer outcomes. J. Cachexia Sarcopenia Muscle 15, 281\u2013291 (2024).","journal-title":"J. Cachexia Sarcopenia Muscle"},{"key":"2016_CR25","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-63806-1","volume":"14","author":"K Zheng","year":"2024","unstructured":"Zheng, K., Liu, X., Li, Y., Cui, J. & Li, W. CT-based muscle and adipose measurements predict prognosis in patients with digestive system malignancy. Sci. Rep. 14, 13036 (2024).","journal-title":"Sci. Rep."},{"key":"2016_CR26","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-76280-6","volume":"14","author":"O Mironchuk","year":"2024","unstructured":"Mironchuk, O. et al. Volumetric body composition analysis of the Cancer Genome Atlas reveals novel body composition traits and molecular markers Associated with Renal Carcinoma outcomes. Sci. Rep. 14, 27022 (2024).","journal-title":"Sci. Rep."},{"key":"2016_CR27","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-024-12524-y","volume":"24","author":"M Takahashi","year":"2024","unstructured":"Takahashi, M. et al. Use of 3D-CT-derived psoas major muscle volume in defining sarcopenia in colorectal cancer. BMC Cancer 24, 741 (2024).","journal-title":"BMC Cancer"},{"key":"2016_CR28","unstructured":"BfArM - ICD-10-GM. https:\/\/www.bfarm.de\/EN\/Code-systems\/Classifications\/ICD\/ICD-10-GM\/_node.html."},{"key":"2016_CR29","doi-asserted-by":"publisher","first-page":"e340","DOI":"10.1016\/j.clml.2021.11.006","volume":"22","author":"D Albano","year":"2022","unstructured":"Albano, D. et al. Prognostic role of \u2018Radiological\u2019 sarcopenia in lymphoma: A systematic review. Clin. Lymphoma Myeloma Leuk. 22, e340\u2013e349 (2022).","journal-title":"Clin. Lymphoma Myeloma Leuk."},{"key":"2016_CR30","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s11912-024-01494-w","volume":"26","author":"D Clement","year":"2024","unstructured":"Clement, D. et al. Sarcopenia and neuroendocrine neoplasms. Curr. Oncol. Rep. 26, 121\u2013128 (2024).","journal-title":"Curr. Oncol. Rep."},{"key":"2016_CR31","doi-asserted-by":"publisher","first-page":"1298","DOI":"10.3390\/nu14061298","volume":"14","author":"L Agate","year":"2022","unstructured":"Agate, L. et al. Nutrition in advanced thyroid cancer patients. Nutrients 14, 1298 (2022).","journal-title":"Nutrients"},{"key":"2016_CR32","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1093\/nop\/npv017","volume":"2","author":"C Amidei","year":"2015","unstructured":"Amidei, C. & Kushner, D. S. Clinical implications of motor deficits related to brain tumors\u2020. Neuro-Oncol. Pract. 2, 179\u2013184 (2015).","journal-title":"Neuro-Oncol. Pract."},{"key":"2016_CR33","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1097\/RLI.0000000000001040","volume":"59","author":"J Haubold","year":"2024","unstructured":"Haubold, J. et al. BOA: A CT-based body and organ analysis for radiologists at the point of care. Invest. Radiol. 59, 433\u2013441 (2024).","journal-title":"Invest. Radiol."},{"key":"2016_CR34","doi-asserted-by":"publisher","first-page":"597675","DOI":"10.3389\/fphys.2020.597675","volume":"11","author":"JM Webster","year":"2020","unstructured":"Webster, J. M., Kempen, L. J. A. P., Hardy, R. S. & Langen, R. C. J. Inflammation and skeletal muscle wasting during cachexia. Front. Physiol. 11, 597675 (2020).","journal-title":"Front. Physiol."},{"key":"2016_CR35","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1198\/000313006X152207","volume":"60","author":"DA Freedman","year":"2006","unstructured":"Freedman, D. A. On the so-called \u201cHuber Sandwich Estimator\u201d and \u201cRobust Standard Errors\u201d. Am. Stat. 60, 299\u2013302 (2006).","journal-title":"Am. Stat."},{"key":"2016_CR36","doi-asserted-by":"publisher","first-page":"13041","DOI":"10.3390\/app132413041","volume":"13","author":"E Liu","year":"2023","unstructured":"Liu, E., Liu, R. Y. & Lim, K. Using the weibull accelerated failure time regression model to predict time to health events. Appl. Sci. 13, 13041 (2023).","journal-title":"Appl. Sci."},{"key":"2016_CR37","first-page":"1","volume":"21","author":"S P\u00f6lsterl","year":"2020","unstructured":"P\u00f6lsterl, S. scikit-survival: A library for time-to-event analysis built on top of scikit-learn. J. Mach. Learn. Res. 21, 1\u20136 (2020).","journal-title":"J. Mach. Learn. Res."},{"key":"2016_CR38","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/j.eswa.2016.07.018","volume":"63","author":"M Schmid","year":"2016","unstructured":"Schmid, M., Wright, M. N. & Ziegler, A. On the use of Harrell\u2019s C for clinical risk prediction via random survival forests. Expert Syst. Appl. 63, 450\u2013459 (2016).","journal-title":"Expert Syst. Appl."},{"key":"2016_CR39","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1002\/cjs.10046","volume":"38","author":"H Hung","year":"2010","unstructured":"Hung, H. & Chiang, C.-T. Estimation methods for time-dependent AUC models with survival data. Can. J. Stat. 38, 8\u201326 (2010).","journal-title":"Can. J. Stat."},{"key":"2016_CR40","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.21105\/joss.01317","volume":"4","author":"C Davidson-Pilon","year":"2019","unstructured":"Davidson-Pilon, C. lifelines: survival analysis in Python. J. Open Source Softw. 4, 1317 (2019).","journal-title":"J. Open Source Softw."},{"key":"2016_CR41","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F. et al. Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02016-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02016-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02016-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T04:04:47Z","timestamp":1760501087000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02016-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2016"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-02016-z","relation":{},"ISSN":["2398-6352"],"issn-type":[{"type":"electronic","value":"2398-6352"}],"subject":[],"published":{"date-parts":[[2025,10,14]]},"assertion":[{"value":"9 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"K.H. reports personal fees from Bayer, personal fees and other from Sofie Biosciences, personal fees from SIRTEX, non-financial support from ABX, personal fees from Adacap, personal fees from Curium, personal fees from Endocyte, grants and personal fees from BTG, personal fees from IPSEN, personal fees from Siemens Healthineers, personal fees from GE Healthcare, personal fees from Amgen, personal fees from Novartis, personal fees from ymabs, personal fees from Aktis Oncology, personal fees from Theragnostics, personal fees from Pharma15, personal fees from Debiopharm, personal fees from AstraZeneca, personal fees from Janssen, outside the submitted work. MSchuler reports consultant fees from Amgen, AstraZeneca, BIOCAD, Blueprint Medicines, Boehringer Ingelheim, Bristol Myers Squibb, GlaxoSmithKline, Janssen, Merck Serono, Novartis, Roche, Sanofi, Takeda; Honoraries for CME presentations from Amgen, Boehringer Ingelheim, Bristol-Myers Squibb, Janssen, Novartis, Roche, Sanofi; Research funding to institution from AstraZeneca, Bristol Myers Squibb. DS reports grants from Novartis, Amgen, MSD, Roche, BMS and consulting fees from Nektar, Philogen, lnFlarX, Neracare, Merck Sharp & Dohme, Novartis, Bristol Myers Squibb, Pfizer, Pierre Fabre, Replimune, Amgen, SunPharma, Daiichi Sanyo, AstraZeneca, IQVIA, LabCorp, BioAlta, and Sanofi; Honoraria from Merck Sharp & Dohme, Novartis, Bristol Myers Squibb, Merck-Serono, Pierre Fabre, Replimune, SunPharma, LabCorp and Sanofi; Support for attending meetings from Pierre Fabre, BMS, MSD; Leadership or fiduciary role in other board (all unpaid) from EORTC, WTZ, Deutsche Krebshilfe, University Alliance Ruhr. MStuschke reports grants from AstraZeneca, Bristol-Myers-Squibb, Sanofi-Aventis, Janssen-Cilag; Participation on a data safety monitoring or advisory board from Sanofi-Aventis, Bristol-Myers-Squibb, Janssen-Cilag, AstraZeneca, Medupdate GmbH, Bristol-Myers-Squibb. BMS received grants from PharmaCept, Else Kr\u00f6ner-Fresenius-Foundation. JH received support from the German Research Foundation as a member of the clinical scientist program. JK received support from the German Cancer Consortium Joint Funding (DKTK JF RAMTAS). KBB received grants from Elekta. JS received grants from Bristol-Myers-Squibb, Roche\/Genentech, Eisbach Bio, Abalos Therapeutics; Consulting fees from Celgene, AstraZeneca, Immunocore, Bayer, Roche, Novartis, SERVIER, MSD Sharpe Dome; Honoraria for lectures from Celgene, Astrazeneca, Immunocore, Bayer, Roche, Novartis, SERVIER, MSD Sharpe Dome; Support for attending meetings from SERVIER; Support for attending meetings from Novartis, Immunocore, Bristol-Myers-Squibb, AstraZeneca; Stock options from Pharma15. MW received grants from Takeda; Consulting fees from GlaxoSmithKline and Novartis; Honoraria from Amgen, AstraZeneca, Roche, and Takeda; Support for attending Meetings from Janssen and GlaxoSmithKline; Participation on a data safety monitoring or advisory board from Amgen, AstraZeneca, Daiichi Sankyo, GlaxoSmithKline, Janssen, Novartis, Pfizer, Roche, Takeda. SK received grants from BMS, Roche, Lilly; Consulting fees from BMS, Lilly, Amgen, Merck Serono, MSD, Novartis, Onkowissen.de, Incyte; Honoraria from BMS, Amgen, MSD, Merck Serono, Lilly, Servier; Support for attending Meetings from Amgen, Pierre Fabre, BMS, Roche, Lilly; Participation on a data safety monitoring or advisory board from Novartis; Leadership or fiduciary role in other board society from DGHO, DKG-AIO; VG received support from Wilhelm-Sander-Foundation; Grants from Pfizer, AstraZeneca, BMS, Ipsen; Consulting fees from AstraZeneca, Apogepha, Astellas, BMS, Novartis, Apogepha, EISAI, MSD, MerckSerono, Roche, EUSAPharm, Nanobiotix, Debiopharm, Oncorena, PCI Biotech; Honoraria from AstraZeneca, Astellas, BMS, Novartis, Ipsen, EISAI, MSD, MerckSerono, Roche, EUSAPharm, Janssen, ONO Pharmaceutical; Support for attending meetings from Merck, Pfizer, Janssen; Leadership or fiduciary role in other board society from German medical oncology working group for cancer treatment, German Cancer Society, German Society of Hematology and Oncology, ESMO, and ASCO; BH reports grants from German Research Foundation; Royalties from Uromed; Consulting fees from AAA\/Novartis, ABX, BMS, Bayer, Janssen, Lightpoint, MSD, Pfizer; Honoraria from Astellas, AstraZeneca, Bayer, Janssen, Pfizer; Support for attending meetings from Astellas, Bayer, Janssen; Participation on a data safety monitoring or advisory board from Janssen; Leadership or fiduciary role in other board society from German Cancer Society. All remaining authors declare no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"611"}}