{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T13:04:59Z","timestamp":1779800699620,"version":"3.53.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"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":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Radiogenomic studies have mostly linked single-site radiomic features (RFs) to genomic alterations in locally-advanced lung cancer, limiting their applicability to patients with metastatic lung adenocarcinoma (MLUAD). Our aim was to evaluate associations between unsupervised CT-based radiomic clustering of single-site and multi-site features and oncogenic alterations (OAs) and response to treatment in MLUAD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>Patients managed at our center (October 2016\u2013January 2024) with pre-treatment CT scans and next-generation sequencing were retrospectively included. Reproducible RFs were extracted from all solid tumor lesions\u2009&gt;\u20091\u2009cm\u00b3 using an automated pipeline. Patient-level integration used the centroid of each patient\u2019s lesions in radiomic space, providing multi-site radiomics data. RFs from the largest and biopsied lesions were also isolated. Patients were clustered by unsupervised hierarchical consensus clustering using centroid-based (Cluster-C), largest lesion (Cluster-M), and biopsied lesion (Cluster-B) features. Uni- and multivariable associations with OAs (any OA, smoker-related [sOA], non-smoker-related [nsOA], or wild-type), overall response rate (ORR), and overall survival (OS) were investigated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 361 patients (median age 63.2 years; 41.3% women; 1721 segmented tumor lesions), 48.2% had sOA and 13% had nsOA. Cluster-M2\u2009+\u2009M5 was enriched in KRAS (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.048), MET (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.046), and PI3KCA (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) alterations. Cluster-M (especially Cluster-M2\u2009+\u2009M5) independently predicted sOA (OR\u2009=\u20092.28,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.006), and nsOA (OR\u2009=\u20095.49,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.004). Cluster-M was linked to higher ORR (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.026) and longer OS (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.016).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Baseline CT-based single- and multi-site radiomics capture patterns associated with key OAs in MLUAD, suggesting their potential role as a non-invasive adjunct to guide molecular testing and optimize treatment selection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>In MLUAD, can single- and multi-site RFs from all measurable lesions enhance the detection of key OAs and outcome prediction beyond standard clinical\u2013radiological assessment?<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>In 361 MLUAD patients, robust clustering using RFs from multiple tumor lesions per patient identified subgroups associated with key OAs, response to treatment, and survival<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Whatever the initial disease staging, radiomic clustering may serve as a non-invasive AI biomarker that complements molecular testing, helping identify actionable tumor profiles and stratify patients for treatment selection and prognostication in MLUAD<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-025-12292-8","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:09:54Z","timestamp":1768522194000},"page":"5109-5124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Single and multi-site CT-based radiogenomics analysis of metastatic lung adenocarcinoma and correlations with outcome"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0098-6482","authenticated-orcid":false,"given":"Amandine","family":"Cromb\u00e9","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lou Andrea","family":"Sitruk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C\u00e9cile","family":"Masson-Grehaigne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathilde","family":"Lafon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean","family":"Palussiere","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Bonhomme","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Cousin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathalie","family":"Lassau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antoine","family":"Italiano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"12292_CR1","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3322\/caac.21834","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray F, Laversanne M, Sung H et al (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 74:229\u2013263. https:\/\/doi.org\/10.3322\/caac.21834","journal-title":"CA Cancer J Clin"},{"key":"12292_CR2","doi-asserted-by":"publisher","first-page":"3357","DOI":"10.1111\/1759-7714.13681","volume":"11","author":"B Campos-Balea","year":"2020","unstructured":"Campos-Balea B, de Castro Carpe\u00f1o J, Massut\u00ed B et al (2020) Prognostic factors for survival in patients with metastatic lung adenocarcinoma: an analysis of the SEER database. Thorac Cancer 11:3357\u20133364. https:\/\/doi.org\/10.1111\/1759-7714.13681","journal-title":"Thorac Cancer"},{"key":"12292_CR3","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1148\/radiol.14140848","volume":"275","author":"C-M Choi","year":"2015","unstructured":"Choi C-M, Kim MY, Hwang HJ, Lee JB, Kim WS (2015) Advanced adenocarcinoma of the lung: comparison of CT characteristics of patients with anaplastic lymphoma kinase gene rearrangement and those with epidermal growth factor receptor mutation. Radiology 275:272\u2013279. https:\/\/doi.org\/10.1148\/radiol.14140848","journal-title":"Radiology"},{"key":"12292_CR4","doi-asserted-by":"publisher","first-page":"5021","DOI":"10.1007\/s00330-020-06784-y","volume":"30","author":"A Dormieux","year":"2020","unstructured":"Dormieux A, Mezquita L, Cournede PH et al (2020) Association of metastatic pattern and molecular status in stage IV non-small cell lung cancer adenocarcinoma. Eur Radiol 30:5021\u20135028. https:\/\/doi.org\/10.1007\/s00330-020-06784-y","journal-title":"Eur Radiol"},{"key":"12292_CR5","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1148\/radiol.2017161845","volume":"286","author":"M Zhou","year":"2018","unstructured":"Zhou M, Leung A, Echegaray S et al (2018) Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology 286:307\u2013315. https:\/\/doi.org\/10.1148\/radiol.2017161845","journal-title":"Radiology"},{"key":"12292_CR6","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1093\/annonc\/mdx034","volume":"28","author":"EJ Limkin","year":"2017","unstructured":"Limkin EJ, Sun R, Dercle L et al (2017) Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. 28:1191\u20131206. https:\/\/doi.org\/10.1093\/annonc\/mdx034","journal-title":"Ann Oncol"},{"key":"12292_CR7","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1158\/0008-5472.CAN-20-0999","volume":"81","author":"G Rossi","year":"2021","unstructured":"Rossi G, Barabino E, Fedeli A et al (2021) Radiomic detection of EGFR mutations in NSCLC. Cancer Res 81:724\u2013731. https:\/\/doi.org\/10.1158\/0008-5472.CAN-20-0999","journal-title":"Cancer Res"},{"key":"12292_CR8","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1111\/1759-7714.13163","volume":"10","author":"X Wang","year":"2019","unstructured":"Wang X, Kong C, Xu W et al (2019) Decoding tumor mutation burden and driver mutations in early stage lung adenocarcinoma using CT-based radiomics signature. Thorac Cancer 10:1904\u20131912. https:\/\/doi.org\/10.1111\/1759-7714.13163","journal-title":"Thorac Cancer"},{"key":"12292_CR9","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1148\/radiol.2020192431","volume":"297","author":"G Wu","year":"2020","unstructured":"Wu G, Woodruff HC, Shen J et al (2020) Diagnosis of invasive lung adenocarcinoma based on chest CT radiomic features of part-solid pulmonary nodules: a multicenter study. Radiology 297:451\u2013458. https:\/\/doi.org\/10.1148\/radiol.2020192431","journal-title":"Radiology"},{"key":"12292_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01221-8","volume":"24","author":"J Wang","year":"2024","unstructured":"Wang J, Wang J, Huang X et al (2024) CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer. BMC Med Imaging 24:45. https:\/\/doi.org\/10.1186\/s12880-024-01221-8","journal-title":"BMC Med Imaging"},{"key":"12292_CR11","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1148\/radiol.211582","volume":"303","author":"R Perez-Johnston","year":"2022","unstructured":"Perez-Johnston R, Araujo-Filho JA, Connolly JG et al (2022) CT-based radiogenomic analysis of clinical stage I lung adenocarcinoma with histopathologic features and oncologic outcomes. Radiology 303:664\u2013672. https:\/\/doi.org\/10.1148\/radiol.211582","journal-title":"Radiology"},{"key":"12292_CR12","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.diii.2024.07.005","volume":"105","author":"C Masson-Grehaigne","year":"2024","unstructured":"Masson-Grehaigne C, Lafon M, Palussi\u00e8re J et al (2024) Single- and multi-site radiomics may improve overall survival prediction for patients with metastatic lung adenocarcinoma. Diagn Interv Imaging 105:439\u2013452. https:\/\/doi.org\/10.1016\/j.diii.2024.07.005","journal-title":"Diagn Interv Imaging"},{"key":"12292_CR13","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 1990 45:228\u2013247. https:\/\/doi.org\/10.1016\/j.ejca.2008.10.026","journal-title":"Eur J Cancer 1990"},{"key":"12292_CR14","unstructured":"Sholl L, Cooper W, Kerr K et al (2023) IASLC atlas of molecular testing for targeted therapy in lung cancer. International Association for the Study of Lung Cancer"},{"key":"12292_CR15","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-47606-9","volume":"15","author":"S Hobor","year":"2024","unstructured":"Hobor S, Al Bakir M, Hiley CT et al (2024) Mixed responses to targeted therapy driven by chromosomal instability through p53 dysfunction and genome doubling. Nat Commun 15:4871. https:\/\/doi.org\/10.1038\/s41467-024-47606-9","journal-title":"Nat Commun"},{"key":"12292_CR16","doi-asserted-by":"publisher","first-page":"4786","DOI":"10.1158\/0008-5472.CAN-18-0125","volume":"78","author":"C Nioche","year":"2018","unstructured":"Nioche C, Orlhac F, Boughdad S et al (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786\u20134789. https:\/\/doi.org\/10.1158\/0008-5472.CAN-18-0125","journal-title":"Cancer Res"},{"key":"12292_CR17","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg A, Valli\u00e8res M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328\u2013338. https:\/\/doi.org\/10.1148\/radiol.2020191145","journal-title":"Radiology"},{"key":"12292_CR18","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1093\/biomet\/87.4.954","volume":"87","author":"I-K Yeo","year":"2000","unstructured":"Yeo I-K, Johnson RA (2000) A new family of power transformations to improve normality or symmetry. Biometrika 87:954\u2013959","journal-title":"Biometrika"},{"key":"12292_CR19","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1093\/bioinformatics\/btq170","volume":"26","author":"MD Wilkerson","year":"2010","unstructured":"Wilkerson MD, Hayes DN (2010) ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26:1572\u20131573. https:\/\/doi.org\/10.1093\/bioinformatics\/btq170","journal-title":"Bioinformatics"},{"key":"12292_CR20","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/s00357-022-09413-z","volume":"39","author":"MJ Warrens","year":"2022","unstructured":"Warrens MJ, van der Hoef H (2022) Understanding the adjusted rand index and other partition comparison indices based on counting object pairs. J Classif 39:487\u2013509. https:\/\/doi.org\/10.1007\/s00357-022-09413-z","journal-title":"J Classif"},{"key":"12292_CR21","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1016\/j.crad.2010.04.005","volume":"65","author":"RJ Gillies","year":"2010","unstructured":"Gillies RJ, Anderson AR, Gatenby RA, Morse DL (2010) The biology underlying molecular imaging in oncology: from genome to anatome and back again. Clin Radiol 65:517\u2013521. https:\/\/doi.org\/10.1016\/j.crad.2010.04.005","journal-title":"Clin Radiol"},{"key":"12292_CR22","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.lungcan.2020.05.021","volume":"146","author":"MF Sharpnack","year":"2020","unstructured":"Sharpnack MF, Cho JH, Johnson TS et al (2020) Clinical and molecular correlates of tumor mutation burden in non-small cell lung cancer. Lung Cancer 146:36\u201341. https:\/\/doi.org\/10.1016\/j.lungcan.2020.05.021","journal-title":"Lung Cancer"},{"key":"12292_CR23","doi-asserted-by":"publisher","first-page":"e002891","DOI":"10.1136\/jitc-2021-002891","volume":"9","author":"MV Negrao","year":"2021","unstructured":"Negrao MV, Skoulidis F, Montesion M et al (2021) Oncogene-specific differences in tumor mutational burden, PD-L1 expression, and outcomes from immunotherapy in non-small cell lung cancer. J Immunother Cancer 9:e002891. https:\/\/doi.org\/10.1136\/jitc-2021-002891","journal-title":"J Immunother Cancer"},{"key":"12292_CR24","doi-asserted-by":"publisher","first-page":"2566","DOI":"10.1158\/0008-5472.CAN-20-3991","volume":"81","author":"X Wang","year":"2021","unstructured":"Wang X, Ricciuti B, Nguyen T et al (2021) Association between smoking history and tumor mutation burden in advanced non-small cell lung cancer. Cancer Res 81:2566\u20132573. https:\/\/doi.org\/10.1158\/0008-5472.CAN-20-3991","journal-title":"Cancer Res"},{"key":"12292_CR25","doi-asserted-by":"publisher","first-page":"5840","DOI":"10.1002\/cncr.27637","volume":"118","author":"PK Paik","year":"2012","unstructured":"Paik PK, Johnson ML, D\u2019Angelo SP et al (2012) Driver mutations determine survival in smokers and never-smokers with stage IIIB\/IV lung adenocarcinomas. Cancer 118:5840\u20135847. https:\/\/doi.org\/10.1002\/cncr.27637","journal-title":"Cancer"},{"key":"12292_CR26","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":"12292_CR27","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1093\/annonc\/mdy512","volume":"30","author":"J Zugazagoitia","year":"2019","unstructured":"Zugazagoitia J, Ramos I, Trigo JM et al (2019) Clinical utility of plasma-based digital next-generation sequencing in patients with advance-stage lung adenocarcinomas with insufficient tumor samples for tissue genotyping. Ann Oncol 30:290\u2013296. https:\/\/doi.org\/10.1093\/annonc\/mdy512","journal-title":"Ann Oncol"},{"key":"12292_CR28","doi-asserted-by":"publisher","DOI":"10.1038\/s41698-024-00616-8","volume":"8","author":"A Cromb\u00e9","year":"2024","unstructured":"Cromb\u00e9 A, Lucchesi C, Bertolo F et al (2024) Integration of pre-treatment computational radiomics, deep radiomics, and transcriptomics enhances soft-tissue sarcoma patient prognosis. NPJ Precis Oncol 8:129. https:\/\/doi.org\/10.1038\/s41698-024-00616-8","journal-title":"NPJ Precis Oncol"},{"key":"12292_CR29","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1007\/s10278-024-01163-1","volume":"38","author":"M Lafon","year":"2025","unstructured":"Lafon M, Cousin S, Alam\u00e9 M, Nougaret S, Italiano A, Cromb\u00e9 A (2025) Metastatic lung adenocarcinomas: development and evaluation of radiomic-based methods to measure baseline intra-patient inter-tumor lesion heterogeneity. J Imaging Inform Med 38:148\u2013164. https:\/\/doi.org\/10.1007\/s10278-024-01163-1","journal-title":"J Imaging Inform Med"},{"key":"12292_CR30","doi-asserted-by":"publisher","unstructured":"Cromb\u00e9 A, Lafon M, Nougaret S, Kind M, Cousin S (2022) Ranking the most influential predictors of CT-based radiomics feature values in metastatic lung adenocarcinoma. Eur J Radiol. 155:110472. https:\/\/doi.org\/10.1016\/j.ejrad.2022.110472","DOI":"10.1016\/j.ejrad.2022.110472"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-12292-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-025-12292-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-12292-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T12:25:32Z","timestamp":1779798332000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-025-12292-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,16]]},"references-count":30,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["12292"],"URL":"https:\/\/doi.org\/10.1007\/s00330-025-12292-8","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,16]]},"assertion":[{"value":"11 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The scientific guarantor of this publication is Pr. Amandine Cromb\u00e9.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare relationships with the following companies: Nathalie Lassau.: Advisory board for Jazz Pharmaceuticals Sophie Cousin: Advisory board: Amgen, Lilly, Roche, Astrazeneca, AbbVie, BMS, Antoine Italiano: Research grant: AMGEN, AstraZeneca, Bayer, BMS, Merck, MSD, Novartis, CHUGAI, Par-thenon, Roche, Pharmamar, BMS Daiichi Sankyo. Advisory board: Bayer, BMS, Merck, MSD, Novartis, CHUGAI, Parthenon, Roche, AstraZeneca, Daiichi Sankyo. Jean Palussi\u00e8re: fees for workshop by Boston Scientific. The other authors declare no financial or non-financial conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"One of the authors has significant statistical expertise: Pr. Amandine Cromb\u00e9 has a PhD and a postdoc in applied mathematics.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board of Bergoni\u00e9 Institute (Bordeaux, France).","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained (Bergoni\u00e9 Institute, Bordeaux, France).","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"In a prior study, our group reported 237 common patients (\n                      \n                      ), but the objectives were different. Indeed, we developed supervised machine learning models to predict OS and compared them to clinical-pathological models, and we did not investigate unsupervised approaches and associations with mutational status (which was incomplete for some of them).","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}