{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T15:27:38Z","timestamp":1785252458420,"version":"3.55.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:00:00Z","timestamp":1663200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:00:00Z","timestamp":1663200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100017268","name":"Berlin Institute of Health","doi-asserted-by":"crossref","award":["Clinician Scientist Grant"],"award-info":[{"award-number":["Clinician Scientist Grant"]}],"id":[{"id":"10.13039\/501100017268","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002839","name":"Charit\u00e9 - Universit\u00e4tsmedizin Berlin","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002839","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol Exp"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>We evaluated the role of radiomics applied to contrast-enhanced computed tomography (CT) in the detection of lymph node (LN) metastases in patients with known lung cancer compared to <jats:sup>18<\/jats:sup>F-fluorodeoxyglucose positron emission tomography (PET)\/CT as a reference.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>This retrospective analysis included 381 patients with 1,799 lymph nodes (450 malignant, 1,349 negative). The data set was divided into a training and validation set. A radiomics analysis with 4 filters and 6 algorithms resulting in 24 different radiomics signatures and a bootstrap algorithm (Bagging) with 30 bootstrap iterations was performed. A decision curve analysis was applied to generate a net benefit to compare the radiomics signature to two expert radiologists as one-by-one and as a prescreening tool in combination with the respective radiologist and only the radiologists.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>All 24 modeling methods showed good and reliable discrimination for malignant\/benign LNs (area under the curve 0.75\u22120.87). The decision curve analysis showed a net benefit for the least absolute shrinkage and selection operator (LASSO) classifier for the entire probability range and outperformed the expert radiologists except for the high probability range. Using the radiomics signature as a prescreening tool for the radiologists did not improve net benefit.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Radiomics showed good discrimination power irrespective of the modeling technique in detecting LN metastases in patients with known lung cancer. The LASSO classifier was a suitable diagnostic tool and even outperformed the expert radiologists, except for high probabilities. Radiomics failed to improve clinical benefit as a prescreening tool.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s41747-022-00296-8","type":"journal-article","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:03:21Z","timestamp":1663200201000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Inferring FDG-PET-positivity of lymph node metastases in proven lung cancer from contrast-enhanced CT using radiomics and machine learning"],"prefix":"10.1186","volume":"6","author":[{"given":"Boris","family":"Gorodetski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp Hendrik","family":"Becker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander Daniel Jacques","family":"Baur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Hartenstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian Manuel Michael","family":"Rogasch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Furth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Holger","family":"Amthauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernd","family":"Hamm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcus","family":"Makowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9591-8575","authenticated-orcid":false,"given":"Tobias","family":"Penzkofer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"296_CR1","doi-asserted-by":"publisher","unstructured":"Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Abate D et al (2019) Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: a systematic analysis for the global burden of disease study. JAMA Oncol. https:\/\/doi.org\/10.1001\/jamaoncol.2019.2996","DOI":"10.1001\/jamaoncol.2019.2996"},{"key":"296_CR2","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1007\/s11307-019-01336-3","volume":"21","author":"F Bianconi","year":"2019","unstructured":"Bianconi F, Palumbo I, Fravolini ML et al (2019) Texture analysis on [(18)F]FDG PET\/CT in non-small-cell lung cancer: correlations between PET features, CT features, and histological types. Mol Imaging Biol 21:1200\u20131209. https:\/\/doi.org\/10.1007\/s11307-019-01336-3","journal-title":"Mol Imaging Biol"},{"key":"296_CR3","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1148\/radiol.2018180910","volume":"290","author":"N Beig","year":"2019","unstructured":"Beig N, Khorrami M, Alilou M et al (2019) Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology 290:783\u2013792. https:\/\/doi.org\/10.1148\/radiol.2018180910","journal-title":"Radiology"},{"key":"296_CR4","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.lungcan.2017.10.015","volume":"115","author":"R Thawani","year":"2018","unstructured":"Thawani R, McLane M, Beig N et al (2018) Radiomics and radiogenomics in lung cancer: A review for the clinician. Lung Cancer 115:34\u201341. https:\/\/doi.org\/10.1016\/j.lungcan.2017.10.015","journal-title":"Lung Cancer"},{"key":"296_CR5","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1038\/s41598-019-57171-7","volume":"10","author":"F Yang","year":"2020","unstructured":"Yang F, Simpson G, Young L, Ford J, Dogan N, Wang L (2020) Impact of contouring variability on oncological PET radiomics features in the lung. Sci Rep 10:369. https:\/\/doi.org\/10.1038\/s41598-019-57171-7","journal-title":"Sci Rep"},{"key":"296_CR6","doi-asserted-by":"publisher","first-page":"584","DOI":"10.4065\/83.5.584","volume":"83","author":"JR Molina","year":"2008","unstructured":"Molina JR, Yang P, Cassivi SD, Schild SE, Adjei AA (2008) Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship. Mayo Clin Proc 83:584\u2013594. https:\/\/doi.org\/10.4065\/83.5.584","journal-title":"Mayo Clin Proc"},{"key":"296_CR7","doi-asserted-by":"publisher","unstructured":"Yang X, Pan X, Liu H et al (2018) A new approach to predict lymph node metastasis in solid lung adenocarcinoma: a radiomics nomogram. J Thorac Dis 10:S807\u2013S819. https:\/\/doi.org\/10.21037\/jtd.2018.03.126","DOI":"10.21037\/jtd.2018.03.126"},{"key":"296_CR8","doi-asserted-by":"publisher","unstructured":"Silvestri GA, Gould MK, Margolis ML et al (2007) Noninvasive staging of non-small cell lung cancer: ACCP evidenced-based clinical practice guidelines (2nd edition). Chest 132:178S\u2013201S.\u00a0https:\/\/doi.org\/10.1378\/chest.07-1360","DOI":"10.1378\/chest.07-1360"},{"key":"296_CR9","doi-asserted-by":"publisher","unstructured":"Schmidt-Hansen M, Baldwin DR, Hasler E, Zamora J, Abraira V, Roque IFM (2014) PET-CT for assessing mediastinal lymph node involvement in patients with suspected resectable non-small cell lung cancer. Cochrane Database Syst Rev. https:\/\/doi.org\/10.1002\/14651858.CD009519.pub2:CD009519","DOI":"10.1002\/14651858.CD009519.pub2:CD009519"},{"key":"296_CR10","doi-asserted-by":"publisher","unstructured":"Yang M, She Y, Deng J et al (2019) CT-based radiomics signature for the stratification of N2 disease risk in clinical stage I lung adenocarcinoma. Transl Lung Cancer Res 8:876\u2013885. https:\/\/doi.org\/10.21037\/tlcr.2019.11.18","DOI":"10.21037\/tlcr.2019.11.18"},{"key":"296_CR11","doi-asserted-by":"publisher","unstructured":"Decaluwe H, Dooms C, D'Journo XB et al (2017) Mediastinal staging by videomediastinoscopy in clinical N1 non-small cell lung cancer: a prospective multicentre study. Eur Respir J 50(6). https:\/\/doi.org\/10.1183\/13993003.01493-2017.","DOI":"10.1183\/13993003.01493-2017"},{"key":"296_CR12","doi-asserted-by":"publisher","unstructured":"Dercle L, Fronheiser M, Lu L et al (2020) Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics. Clin Cancer Res. https:\/\/doi.org\/10.1158\/1078-0432.CCR-19-2942","DOI":"10.1158\/1078-0432.CCR-19-2942"},{"key":"296_CR13","doi-asserted-by":"publisher","unstructured":"Schreyogg J, Weller J, Stargardt T et al (2010) Cost-effectiveness of hybrid PET\/CT for staging of non-small cell lung cancer. J Nucl Med 51:1668\u20131675. https:\/\/doi.org\/10.2967\/jnumed.109.072090","DOI":"10.2967\/jnumed.109.072090"},{"key":"296_CR14","doi-asserted-by":"publisher","unstructured":"Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749\u2013762. https:\/\/doi.org\/10.1038\/nrclinonc.2017.141","DOI":"10.1038\/nrclinonc.2017.141"},{"key":"296_CR15","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1097\/RLI.0000000000000530","volume":"54","author":"B Baessler","year":"2019","unstructured":"Baessler B, Weiss K, Pinto Dos Santos D (2019) Robustness and reproducibility of radiomics in magnetic resonance imaging: a phantom study. Invest Radiol 54:221\u2013228. https:\/\/doi.org\/10.1097\/RLI.0000000000000530","journal-title":"Invest Radiol"},{"key":"296_CR16","doi-asserted-by":"publisher","first-page":"6904","DOI":"10.1158\/1078-0432.CCR-17-1510","volume":"23","author":"S Wu","year":"2017","unstructured":"Wu S, Zheng J, Li Y et al (2017) A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Clin Cancer Res 23:6904\u20136911. https:\/\/doi.org\/10.1158\/1078-0432.CCR-17-1510","journal-title":"Clin Cancer Res"},{"key":"296_CR17","doi-asserted-by":"publisher","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441\u2013446.\u00a0https:\/\/doi.org\/10.1016\/j.ejca.2011.11.036","DOI":"10.1016\/j.ejca.2011.11.036"},{"key":"296_CR18","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology 278:563\u2013577. https:\/\/doi.org\/10.1148\/radiol.2015151169","journal-title":"Radiology"},{"key":"296_CR19","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1038\/ncomms5006","volume":"5","author":"HJ Aerts","year":"2014","unstructured":"Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. https:\/\/doi.org\/10.1038\/ncomms5006","journal-title":"Nat Commun"},{"key":"296_CR20","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1158\/1078-0432.CCR-17-2804","volume":"24","author":"H Aerts","year":"2018","unstructured":"Aerts H (2018) Data science in radiology: a path forward. Clin Cancer Res 24:532\u2013534. https:\/\/doi.org\/10.1158\/1078-0432.CCR-17-2804","journal-title":"Clin Cancer Res"},{"key":"296_CR21","doi-asserted-by":"publisher","first-page":"13087","DOI":"10.1038\/srep13087","volume":"5","author":"C Parmar","year":"2015","unstructured":"Parmar C, Grossmann P, Bussink J, Lambin P, Aerts H (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087. https:\/\/doi.org\/10.1038\/srep13087","journal-title":"Sci Rep"},{"key":"296_CR22","doi-asserted-by":"publisher","unstructured":"Hosny A, Parmar C, Coroller TP et al (2018) Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS Med 15.\u00a0https:\/\/doi.org\/10.1371\/journal.pmed.1002711\u00a0","DOI":"10.1371\/journal.pmed.1002711"},{"key":"296_CR23","doi-asserted-by":"publisher","first-page":"2120","DOI":"10.1371\/10.1016\/j.jtho.2016.07.002","volume":"11","author":"S Hawkins","year":"2016","unstructured":"Hawkins S, Wang H, Liu Y et al (2016) Predicting Malignant Nodules from Screening CT Scans. J Thorac Oncol 11:2120\u20132128. https:\/\/doi.org\/10.1371\/10.1016\/j.jtho.2016.07.002","journal-title":"J Thorac Oncol"},{"key":"296_CR24","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3978\/j.issn.2223-4292.2016.02.01","volume":"6","author":"C Dennie","year":"2016","unstructured":"Dennie C, Thornhill R, Sethi-Virmani V et al (2016) Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules. Quant Imaging Med Surg 6:6\u201315. https:\/\/doi.org\/10.3978\/j.issn.2223-4292.2016.02.01","journal-title":"Quant Imaging Med Surg"},{"key":"296_CR25","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1007\/s00259-018-3987-2","volume":"45","author":"M Kirienko","year":"2018","unstructured":"Kirienko M, Cozzi L, Rossi A et al (2018) Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging 45:1649\u20131660. https:\/\/doi.org\/10.1007\/s00259-018-3987-2","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"296_CR26","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1007\/s00330-011-2319-8","volume":"22","author":"B Ganeshan","year":"2012","unstructured":"Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K (2012) Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22:796\u2013802. https:\/\/doi.org\/10.1007\/s00330-011-2319-8","journal-title":"Eur Radiol"},{"key":"296_CR27","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1515\/raon-2015-0043","volume":"49","author":"A Nappi","year":"2015","unstructured":"Nappi A, Gallicchio R, Simeon V et al (2015) [F-18] FDG-PET\/CT parameters as predictors of outcome in inoperable NSCLC patients. Radiol Oncol 49:320\u2013326. https:\/\/doi.org\/10.1515\/raon-2015-0043","journal-title":"Radiol Oncol"},{"key":"296_CR28","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/j.crad.2017.01.015","volume":"72","author":"B Sacconi","year":"2017","unstructured":"Sacconi B, Anzidei M, Leonardi A et al (2017) Analysis of CT features and quantitative texture analysis in patients with lung adenocarcinoma: a correlation with EGFR mutations and survival rates. Clin Radiol 72:443\u2013450. https:\/\/doi.org\/10.1016\/j.crad.2017.01.015","journal-title":"Clin Radiol"},{"key":"296_CR29","doi-asserted-by":"publisher","first-page":"3450","DOI":"10.1007\/s00330-013-2965-0","volume":"23","author":"M Ravanelli","year":"2013","unstructured":"Ravanelli M, Farina D, Morassi M et al (2013) Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy. Eur Radiol 23:3450\u20133455. https:\/\/doi.org\/10.1007\/s00330-013-2965-0","journal-title":"Eur Radiol"},{"key":"296_CR30","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1016\/S1470-2045(18)30413-3","volume":"19","author":"R Sun","year":"2018","unstructured":"Sun R, Limkin EJ, Vakalopoulou M et al (2018) A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 19:1180\u20131191. https:\/\/doi.org\/10.1016\/S1470-2045(18)30413-3","journal-title":"Lancet Oncol"},{"key":"296_CR31","doi-asserted-by":"publisher","unstructured":"Li J, Dong D, Fang M et al (2020) Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-019-06621-x","DOI":"10.1007\/s00330-019-06621-x"},{"key":"296_CR32","doi-asserted-by":"publisher","first-page":"15198","DOI":"10.1038\/s41598-019-51599-7","volume":"9","author":"M Bogowicz","year":"2019","unstructured":"Bogowicz M, Tanadini-Lang S, Guckenberger M, Riesterer O (2019) Combined CT radiomics of primary tumor and metastatic lymph nodes improves prediction of loco-regional control in head and neck cancer. Sci Rep 9:15198. https:\/\/doi.org\/10.1038\/s41598-019-51599-7","journal-title":"Sci Rep"},{"key":"296_CR33","doi-asserted-by":"publisher","first-page":"559","DOI":"10.2967\/jnumed.113.128504","volume":"55","author":"P Flechsig","year":"2014","unstructured":"Flechsig P, Kratochwil C, Schwartz LH et al (2014) Quantitative volumetric CT-histogram analysis in N-staging of 18F-FDG-equivocal patients with lung cancer. J Nucl Med 55:559\u2013564. https:\/\/doi.org\/10.2967\/jnumed.113.128504","journal-title":"J Nucl Med"},{"key":"296_CR34","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.ejca.2008.10.026","volume":"45","author":"EA Eisenhauer","year":"2009","unstructured":"Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228\u2013247. https:\/\/doi.org\/10.1016\/j.ejca.2008.10.026","journal-title":"Eur J Cancer"},{"key":"296_CR35","doi-asserted-by":"publisher","first-page":"6116","DOI":"10.1007\/s00330-020-07572-4","volume":"31","author":"FHJ Elsholtz","year":"2021","unstructured":"Elsholtz FHJ, Asbach P, Haas M et al (2021) Introducing the Node Reporting and Data System 1.0 (Node-RADS): a concept for standardized assessment of lymph nodes in cancer. Eur Radiol 31:6116\u20136124. https:\/\/doi.org\/10.1007\/s00330-020-07572-4","journal-title":"Eur Radiol"},{"key":"296_CR36","unstructured":"Zwanenburg A, Leger S, Valli\u00e8res M, L\u00f6ck S (2016) Image biomarker standardisation initiative. arXiv e-prints. Retrieved from\u00a0https:\/\/ui.adsabs.harvard.edu\/abs\/2016arXiv161207003Z."},{"key":"296_CR37","doi-asserted-by":"publisher","unstructured":"Zwanenburg A, Vallieres M, Abdalah MA et al (2020) The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. https:\/\/doi.org\/10.1148\/radiol.2020191145:191145","DOI":"10.1148\/radiol.2020191145:191145"},{"key":"296_CR38","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","unstructured":"van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104\u2013e107. https:\/\/doi.org\/10.1158\/0008-5472.CAN-17-0339","journal-title":"Cancer Res"},{"key":"296_CR39","doi-asserted-by":"publisher","unstructured":"Dissaux G, Visvikis D, Da-Ano R et al (2019) Pre-treatment (18)F-FDG PET\/CT Radiomics predict local recurrence in patients treated with stereotactic radiotherapy for early-stage non-small cell lung cancer: a multicentric study. J Nucl Med. https:\/\/doi.org\/10.2967\/jnumed.119.228106","DOI":"10.2967\/jnumed.119.228106"},{"key":"296_CR40","doi-asserted-by":"publisher","unstructured":"Zhang R, Zhu L, Cai Z et al (2019) Potential feature exploration and model development based on 18F-FDG PET\/CT images for differentiating benign and malignant lung lesions. Eur J Radiol 121.\u00a0https:\/\/doi.org\/10.1016\/j.ejrad.2019.108735","DOI":"10.1016\/j.ejrad.2019.108735"},{"key":"296_CR41","doi-asserted-by":"publisher","unstructured":"Vickers AJ, Van Calster B, Steyerberg EW (2016) Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests.\u00a0BMJ 352:i6.https:\/\/doi.org\/10.1136\/bmj.i6\u00a0","DOI":"10.1136\/bmj.i6"},{"key":"296_CR42","doi-asserted-by":"publisher","unstructured":"Van Calster B, Vickers AJ (2015) Calibration of risk prediction models: impact on decision-analytic performance. Med Decis Mak 35:162\u2013169.\u00a0https:\/\/doi.org\/10.1177\/0272989X14547233","DOI":"10.1177\/0272989X14547233"},{"key":"296_CR43","doi-asserted-by":"publisher","unstructured":"Vickers AJ, van Calster B, Steyerberg EW (2019) A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res 3:18.\u00a0https:\/\/doi.org\/10.1186\/s41512-019-0064-7","DOI":"10.1186\/s41512-019-0064-7"}],"container-title":["European Radiology Experimental"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s41747-022-00296-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s41747-022-00296-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s41747-022-00296-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:05:00Z","timestamp":1663200300000},"score":1,"resource":{"primary":{"URL":"https:\/\/eurradiolexp.springeropen.com\/articles\/10.1186\/s41747-022-00296-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,15]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["296"],"URL":"https:\/\/doi.org\/10.1186\/s41747-022-00296-8","relation":{},"ISSN":["2509-9280"],"issn-type":[{"value":"2509-9280","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,15]]},"assertion":[{"value":"16 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Institutional Review Board approval was not required because of the retrospective nature of the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"BG, PHB, AH, JMMR, CF, and MM declare that they have no competing interests. ADJB declares no conflicts of interest directly related to this work. He has received payments as a speaker for b\u00b7e\u00b7imaging GmbH as well as Takeda Pharmaceutical Company Limited. HA received lecture fees from Pfizer, Sirtex, Novartis, GE, Norgine, and travel expenses from Terumo. BH received grant money from the following companies or non-profit organisations to the Dept of Radiology: Abbott, AbbVie, Ablative Solutions, Accovion, Achaogen Inc., Actelion Pharmaceuticals, ADIR, Aesculap, Agios Pharmaceuticals, INC, AGO, AIF Arbeitsgemeinschaft industrieller Forschungsvereinigungen, AIO: Arbeitsgemeinschaft Internistische Onkologie, Alexion Pharmaceuticals, Amgen, AO Foundation, Arena Pharmaceuticals, ARMO Biosciences, Inc., art photonics GmbG Berlin, Ascelia Pharma AB, ASR Advanced sleep research, Astellas, AstraZeneca, BARD, Bayer Healthcare, Bayer Schering Pharma, Bayer Vital, BBraun (Sponsoring a workshop), Berlin-Brandenburger Centrum f\u00fcr Regenerative Therapien (BCRT), Berliner Krebsgesellschaft, Biotronik, Bioven, BMBF, Boehring Ingelheimer, Boston Biomedical Inc., Boston Scientific Medizintechnik GmbH, BRACCO Group, Brainsgate, Bristol-Myers Squibb, Calithera Bioschiences UK, Cantargia AB, Medcion Village, Cascadian 7 Therapeutics, Inc., Celgene, CELLACT Pharma, Celldex, Therapeutics, CeloNova BioSciences, Charit\u00e9 research organisatin GmbH, Chiltern, CLOVIS ONCOLOGY, INC., Covance, CRO Charite, CUBIST, CureVac AG, T\u00fcbingen, Curis, Daiichi Sankyo, Dartmouth College, Hanover, NH, USA, DC Devices, Inc. USA, Delcath Systems, Dermira Inc. Deutsche Krebshilfe, Deutsche Rheuma Liga, Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e.V., DFG, Dr. Falk Pharma GmbH, DSM Nutritional Products AG, Dt. Stiftung f\u00fcr Herzforschung, Dynavax, Eisai Ltd., European Knowledge Centre, Mosquito Way, Hatfield, Eli Lilly and Company Ltd. EORTC, Epizyme, INC., Essex Pharma, EU Programmes, Euroscreen S.A., Ferring Pharmaceuticals A\/S, Fibrex Medical Inc., Focused Ultrasound Surgery Foundation, Fraunhofer Gesellschaft, Galena Biopharma, Galmed Research and Development Ltd., Ganymed, GE, Genentech Inc., Genmab A\/S, Genzysme Europe B.V., GETNE (Grupo Espanol de Tumores Neuroendocrinos), Gilead Sciences, Inc, Glaxo Smith Kline, Glycotope GmbH, Berlin, Goethe Uni Frankfurt, Guerbet, Guidant Europe NV, Halozyme, Hewlett Packard GmbH, Holaira Inc. ICON (CRO), Idera Pharmaceuticals, Inc., Ignyta, Inc. Immunomedics Inc., Immunocore, Incyte, INC Research, Innate Pharma, InSightec Ltd., Inspiremd, inVentiv Health Clinical UK Ltd., Inventivhealth, IOMEDICO, IONIS, IPSEN Pharma, IQVIA, ISA Therapeutics, Isis Pharmaceuticals Inc., ITM Solucin GmbH, Jansen-Cilag GmbH, Kantar Health Gmbh (CRO), Kartos Therapeutics, Inc., Karyopharm Therapeutics, Inc., Kendle\/ MorphoSys Ag, Kite Pharma, Kli Fo Berlin Mitte, La Roche, Land Berlin, Lilly GmbH, Lion Biotechnology, Lombard Medical, Loxo Oncology, Inc., LSK BioPartners; USA, Lundbeck GmbH, Lux Biosciences, LYSARC, MacroGenics, MagForce, MedImmune Inc., MedImmune Limited, Medpace, Medpace Germany GmbH (CRO), MedPass (CRO), Medronic, Medtrave\u00f6 GmbH, Merck, Merrimack Pharmaceuticals Inc., MeVis Medical Solutions AG, Millennium Pharmaceuticals Inc., miRagen Boukider, Mologen, Monika Kutzner Stiftung, MSD Sharp, Nektar Therapeutics, NeoVacs SA, Newlink Genentics Corporation, Nexus Oncology, NIH, Novartis, novocure, Nuvisan, Ockham oncology, Odonate Therapeutics San Diego, OHIRC Kanada, Orion Corporation Orion Pharma, OSE Immunotherapeutics, Parexel CRO Service, Perceptive, Pfizer GmbH, PharmaCept GmbH, Pharma Mar, Pharmaceutical Research Associates GmbH (PRA), Pharmacyclics Inc., Philipps, PIQUR Therapeutics Ltd., Pluristem, PneumRX. Inc, Portola Pharmaceuticals, PPD (CRO), PRAint, Premier-research, Provectus Biopharmaceuticals, Inc., psi-cro, Pulmonx International S\u00e0rl, Quintiles GmbH, Regeneron Pharmaceuticals, Inc., Respicardia, Roche, Samsung, Sanofi, sanofis-aventis S.A., Schumacher GmbH (Sponsoring a workshop), Seattle Genetics, Servier (CRO), SGS Life Science Services (CRO), Shire Human Genetic Therapies, Siemens, Silena Therapeutics, SIRTEX Medical Europe GmbH, Spectranetics GmbH, Spectrum Pharmaceuticals, Stiftung Charite\/BIH, St. Jude Medical, Stiftung Wolfgang Schulze, Symphogen, Taiho Oncology, Inc., Taiho Pharmaceutical Co., Target Pharma Solutions Inc., TauRx Therapeutics Ltd., Terumo Medical Corporation, Tesaro, tetec-ag, TEVA, Theorem, Theradex, Theravance, Threshold Pharmaceuticals Inc., TNS Healthcare GmbH, Toshiba, UCB Pharma, Uni M\u00fcnchen, Uni T\u00fcbingen, Vaccibody A.S., 8 VDI\/VDE, Vertex Pharmaceuticals Incorporated, Virtualscopis LLC, winicker-norimed, Wyeth Pharma, Xcovery Holding Company, Zukunftsfond Berlin (TSB). TP was supported by Berlin Institute of Health (Clinician Scientist Grant, Platform Grant) and reports research agreements (no personal payments, outside of submitted work) with AGO, Aprea AB, ARCAGY-GINECO, Astellas Pharma Global Inc. (APGD), Astra Zeneca, Clovis Oncology, Inc., Dohme Corp, Holaira, Incyte Corporation, Karyopharm, Lion Biotechnologies, Inc., MedImmune, Merck Sharp, Millennium Pharmaceuticals, Inc., Morphotec Inc., NovoCure Ltd., PharmaMar S.A. and PharmaMar USA, Inc., Roche, Siemens Healthineers, and TESARO Inc.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"44"}}