{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T21:23:38Z","timestamp":1794345818852,"version":"4.9.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T00:00:00Z","timestamp":1749254400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T00:00:00Z","timestamp":1749254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100015652","name":"Barts Charity","doi-asserted-by":"publisher","award":["G-001522, MGU0461"],"award-info":[{"award-number":["G-001522, MGU0461"]}],"id":[{"id":"10.13039\/100015652","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015652","name":"Barts Charity","doi-asserted-by":"publisher","award":["G-001522"],"award-info":[{"award-number":["G-001522"]}],"id":[{"id":"10.13039\/100015652","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015652","name":"Barts Charity","doi-asserted-by":"publisher","award":["G-001522, MGU0461"],"award-info":[{"award-number":["G-001522, MGU0461"]}],"id":[{"id":"10.13039\/100015652","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Precis. Onc."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Clinical adoption of digital pathology-based artificial intelligence models for diagnosing lung cancer has been limited, partly due to lack of robust external validation. This review provides an overview of such tools, their performance and external validation. We systematically searched for external validation studies in medical, engineering and grey literature databases from 1st January 2010 to 31st October 2024. 22 studies were included. Models performed various tasks, including classification of malignant versus non-malignant tissue, tumour growth pattern classification and subtyping of adeno- versus squamous cell carcinomas. Subtyping models were most common and performed highly, with average AUC values ranging from 0.746 to 0.999. Although most studies used restricted datasets, methodological issues relevant to the applicability of models in real-world settings included small and\/or non-representative datasets, retrospective studies and case-control studies without further real-world validation. Ultimately, more rigorous external validation of models is warranted for increased clinical adoption.<\/jats:p>","DOI":"10.1038\/s41698-025-00940-7","type":"journal-article","created":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T07:17:58Z","timestamp":1749280678000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Systematic scoping review of external validation studies of AI pathology models for lung cancer diagnosis"],"prefix":"10.1038","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8850-725X","authenticated-orcid":false,"given":"Soumya","family":"Arun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariia","family":"Grosheva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Kosenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan Lukas","family":"Robertus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oleg","family":"Blyuss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rhian","family":"Gabe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Munblit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judith","family":"Offman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,7]]},"reference":[{"key":"940_CR1","doi-asserted-by":"publisher","first-page":"11","DOI":"10.12965\/jer.2142018.009","volume":"17","author":"YJ Kim","year":"2021","unstructured":"Kim, Y. J., Roh, E. H. & Park, S. A literature review of quality, costs, process-associated with digital pathology. J. Exerc. Rehabil. 17, 11\u201314 (2021).","journal-title":"J. Exerc. Rehabil."},{"key":"940_CR2","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1016\/S1470-2045(20)30691-4","volume":"21","author":"E Gourd","year":"2020","unstructured":"Gourd, E. Lung cancer control in the UK hit badly by COVID-19 pandemic. Lancet Oncol. 21, 1559 (2020).","journal-title":"Lancet Oncol."},{"key":"940_CR3","unstructured":"Cancer Research UK. Survival for Lung Cancer. https:\/\/www.cancerresearchuk.org\/about-cancer\/lung-cancer\/survival (2022)."},{"key":"940_CR4","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.jtho.2023.07.019","volume":"19","author":"S Lam","year":"2024","unstructured":"Lam, S. et al. Current and future perspectives on computed tomography screening for lung cancer: a roadmap from 2023 to 2027 from the International Association for the study of lung cancer. J. Thorac. Oncol. 19, 36\u201351 (2024).","journal-title":"J. Thorac. Oncol."},{"key":"940_CR5","unstructured":"The Royal College of Pathologists. Meeting Pathology Demand: Histopathology Workforce Census. https:\/\/www.rcpath.org\/static\/952a934d-2ec3-48c9-a8e6e00fcdca700f\/Meeting-Pathology-Demand-Histopathology-Workforce-Census-2018.pdf (2018)."},{"key":"940_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.modpat.2024.100657","volume":"38","author":"S Vos","year":"2025","unstructured":"Vos, S. et al. Making pathologists ready for the new artificial intelligence era: changes in required competencies. Mod. Pathol. 38, 100657 (2025).","journal-title":"Mod. Pathol."},{"key":"940_CR7","doi-asserted-by":"publisher","first-page":"185","DOI":"10.3389\/fmed.2019.00185","volume":"6","author":"A Serag","year":"2019","unstructured":"Serag, A. et al. Translational AI and deep learning in diagnostic pathology. Front. Med. 6, 185 (2019).","journal-title":"Front. Med."},{"key":"940_CR8","doi-asserted-by":"publisher","first-page":"6214","DOI":"10.18632\/aging.202504","volume":"13","author":"J Ning","year":"2021","unstructured":"Ning, J. et al. Early diagnosis of lung cancer: which is the optimal choice? Aging.13, 6214\u20136227 (2021).","journal-title":"Aging"},{"key":"940_CR9","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1111\/imj.16393","volume":"54","author":"IA Scott","year":"2024","unstructured":"Scott, I. A. & Zuccon, G. The new paradigm in machine learning\u2014foundation models, large language models and beyond: a primer for physicians. Intern. Med. J. 54, 705\u2013715 (2024).","journal-title":"Intern. Med. J."},{"key":"940_CR10","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s00428-023-03518-5","volume":"482","author":"C Eloy","year":"2023","unstructured":"Eloy, C. et al. Artificial intelligence-assisted cancer diagnosis improves the efficiency of pathologists in prostatic biopsies. Virchows Arch. 482, 595\u2013604 (2023).","journal-title":"Virchows Arch."},{"key":"940_CR11","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1426-2","volume":"17","author":"CJ Kelly","year":"2019","unstructured":"Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G. & King, D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 17, 195 (2019).","journal-title":"BMC Med."},{"key":"940_CR12","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-021-01634-3","volume":"21","author":"R Tsopra","year":"2021","unstructured":"Tsopra, R. et al. A framework for validating AI in precision medicine: considerations from the European ITFoC consortium. BMC Med. Inform. Decis. Mak. 21, 274 (2021).","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"940_CR13","doi-asserted-by":"crossref","unstructured":"Davri, A. et al. Deep Learning for lung cancer diagnosis, prognosis and prediction using histological and cytological images: a systematic review. Cancers. 15, 3981 (2023).","DOI":"10.3390\/cancers15153981"},{"key":"940_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105209","volume":"142","author":"S Prabhu","year":"2022","unstructured":"Prabhu, S., Prasad, K., Robels-Kelly, A. & Lu, X. AI-based carcinoma detection and classification using histopathological images: a systematic review. Comput Biol. Med. 142, 105209 (2022).","journal-title":"Comput Biol. Med."},{"key":"940_CR15","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01106-8","volume":"7","author":"C McGenity","year":"2024","unstructured":"McGenity, C. et al. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. npj Digit. Med. 7, 114 (2024).","journal-title":"npj Digit. Med."},{"key":"940_CR16","doi-asserted-by":"publisher","first-page":"2924","DOI":"10.1038\/s41591-024-03141-0","volume":"30","author":"E Vorontsov","year":"2024","unstructured":"Vorontsov, E. et al. A foundation model for clinical-grade computational pathology and rare cancers detection. Nat. Med. 30, 2924\u20132935 (2024).","journal-title":"Nat. Med."},{"key":"940_CR17","doi-asserted-by":"publisher","unstructured":"Bilaloglu, S. et al. Efficient pan-cancer whole-slide image classification and outlier detection using convolutional neural networks. bioRxiv, 633123. https:\/\/doi.org\/10.1101\/633123 (2019).","DOI":"10.1101\/633123"},{"key":"940_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102837","volume":"88","author":"L Cao","year":"2023","unstructured":"Cao, L. et al. E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image. Med. Image Anal. 88, 102837 (2023).","journal-title":"Med. Image Anal."},{"key":"940_CR19","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.lungcan.2022.01.005","volume":"165","author":"Y Chen","year":"2022","unstructured":"Chen, Y. et al. A whole-slide image (WSI)-based immunohistochemical feature prediction system improves the subtyping of lung cancer. Lung Cancer. 165, 18\u201327 (2022).","journal-title":"Lung Cancer"},{"key":"940_CR20","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","volume":"24","author":"N Coudray","year":"2018","unstructured":"Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559\u20131567 (2018).","journal-title":"Nat. Med."},{"key":"940_CR21","doi-asserted-by":"publisher","unstructured":"Hari, S. N. et al. Examining batch effect in histopathology as a distributionally robust optimization problem. bioRxiv. https:\/\/doi.org\/10.1101\/2021.09.14.460365 (2021).","DOI":"10.1101\/2021.09.14.460365"},{"key":"940_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-87644-7","volume":"11","author":"F Kanavati","year":"2021","unstructured":"Kanavati, F. et al. A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images. Sci. Rep. 11, 8110 (2021).","journal-title":"Sci. Rep."},{"key":"940_CR23","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-03206-x","volume":"11","author":"AL Le Page","year":"2021","unstructured":"Le Page, A. L. et al. Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images. Sci. Rep. 11, 23912 (2021).","journal-title":"Sci. Rep."},{"key":"940_CR24","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu, M. Y. et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5, 555\u2013570 (2021).","journal-title":"Nat. Biomed. Eng."},{"key":"940_CR25","doi-asserted-by":"publisher","first-page":"104908","DOI":"10.1016\/j.ebiom.2023.104908","volume":"99","author":"P Mukashyaka","year":"2024","unstructured":"Mukashyaka, P., Sheridan, T. B., Foroughi, P. A. & Chuang, J. H. SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images. eBioMedicine 99, 104908 (2024).","journal-title":"eBioMedicine"},{"key":"940_CR26","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-20030-5","volume":"11","author":"J Noorbakhsh","year":"2020","unstructured":"Noorbakhsh, J. et al. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nat. Commun. 11, 6367 (2020).","journal-title":"Nat. Commun."},{"key":"940_CR27","doi-asserted-by":"publisher","first-page":"4596","DOI":"10.1038\/s41467-024-48666-7","volume":"15","author":"A Quiros","year":"2024","unstructured":"Quiros, A. et al. Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides. Nat. Commun. 15, 4596 (2024).","journal-title":"Nat. Commun."},{"key":"940_CR28","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-43172-8","volume":"14","author":"S Wang","year":"2023","unstructured":"Wang, S. et al. Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images. Nat. Commun. 14, 7872 (2023).","journal-title":"Nat. Commun."},{"key":"940_CR29","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-021-01953-2","volume":"19","author":"H Yang","year":"2021","unstructured":"Yang, H. et al. Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study. BMC Med. 19, 80 (2021).","journal-title":"BMC Med."},{"key":"940_CR30","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1093\/jamia\/ocz230","volume":"27","author":"KH Yu","year":"2020","unstructured":"Yu, K. H. et al. Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks. J. Am. Med Inf. Assoc. 27, 757\u2013769 (2020).","journal-title":"J. Am. Med Inf. Assoc."},{"key":"940_CR31","doi-asserted-by":"publisher","unstructured":"Sharma, R., Kumar, S., Shrivastava, A. & Bhatt, T. Optimizing knowledge transfer in sequential models: leveraging residual connections in flow transfer learning for lung cancer classification. 14th Indian Conference on Computer Vision, Graphics and Image Processing. https:\/\/doi.org\/10.1145\/3627631.3627663 (2024).","DOI":"10.1145\/3627631.3627663"},{"key":"940_CR32","doi-asserted-by":"publisher","unstructured":"Borras Ferris, L. et al. A full pipeline to analyze lung histopathology images. SPIE\u2014Progress in Biomedical Optics and Imaging. 12933. https:\/\/doi.org\/10.1117\/12.3006708 (2024).","DOI":"10.1117\/12.3006708"},{"key":"940_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-37638-9","volume":"9","author":"A Gertych","year":"2019","unstructured":"Gertych, A. et al. Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Sci. Rep. 9, 1483 (2019).","journal-title":"Sci. Rep."},{"key":"940_CR34","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-66333-x","volume":"10","author":"F Kanavati","year":"2020","unstructured":"Kanavati, F. et al. Weakly-supervised learning for lung carcinoma classification using deep learning. Sci. Rep. 10, 9297 (2020).","journal-title":"Sci. Rep."},{"key":"940_CR35","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1111\/his.14779","volume":"81","author":"T Sakamoto","year":"2022","unstructured":"Sakamoto, T. et al. A collaborative workflow between pathologists and deep learning for the evaluation of tumour cellularity in lung adenocarcinoma. Histopathology. 81, 758\u2013769 (2022).","journal-title":"Histopathology"},{"key":"940_CR36","doi-asserted-by":"publisher","unstructured":"Swiderska-Chadaj, Z. et al. A deep learning approach to assess the predominant tumor growth pattern in whole-slide images of lung adenocarcinoma. Medical Imaging: Digital Pathology. https:\/\/doi.org\/10.1117\/12.2549742 (2020).","DOI":"10.1117\/12.2549742"},{"key":"940_CR37","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.ebiom.2019.10.033","volume":"50","author":"S Wang","year":"2019","unstructured":"Wang, S. et al. ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine. 50, 103\u2013110 (2019).","journal-title":"EBioMedicine"},{"key":"940_CR38","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1038\/s41591-021-01517-0","volume":"27","author":"V Sounderajah","year":"2021","unstructured":"Sounderajah, V. et al. A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat. Med. 27, 1663\u20131665 (2021).","journal-title":"Nat. Med."},{"key":"940_CR39","doi-asserted-by":"publisher","first-page":"i3139","DOI":"10.1136\/bmj.i3139","volume":"353","author":"JA Usher-Smith","year":"2016","unstructured":"Usher-Smith, J. A., Sharp, S. J. & Griffin, S. J. The spectrum effect in tests for risk prediction, screening, and diagnosis. BMJ. 353, i3139 (2016).","journal-title":"BMJ."},{"key":"940_CR40","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.21037\/tlcr-21-794","volume":"11","author":"H Zeng","year":"2022","unstructured":"Zeng, H. et al. Racial disparities in histological subtype, stage, tumor grade and cancer-specific survival in lung cancer. Transl. Lung Cancer Res. 11, 1348\u20131358 (2022).","journal-title":"Transl. Lung Cancer Res."},{"key":"940_CR41","doi-asserted-by":"publisher","first-page":"76","DOI":"10.21037\/tlcr-23-407","volume":"13","author":"FC Duncan","year":"2024","unstructured":"Duncan, F. C. et al. Racial disparities in staging, treatment, and mortality in non-small cell lung cancer. Transl. Lung Cancer Res. 13, 76\u201394 (2024).","journal-title":"Transl. Lung Cancer Res."},{"key":"940_CR42","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-023-05156-9","volume":"24","author":"D Rajput","year":"2023","unstructured":"Rajput, D., Wang, W.-J. & Chen, C.-C. Evaluation of a decided sample size in machine learning applications. BMC Bioinform. 24, 48 (2023).","journal-title":"BMC Bioinform."},{"key":"940_CR43","doi-asserted-by":"publisher","unstructured":"Wang, S. et al. Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images. Nat. Commun. 14. https:\/\/doi.org\/10.1038\/s41467-023-43172-8 (2023).","DOI":"10.1038\/s41467-023-43172-8"},{"key":"940_CR44","doi-asserted-by":"publisher","first-page":"467","DOI":"10.7326\/M18-0850","volume":"169","author":"AC Tricco","year":"2018","unstructured":"Tricco, A. C. et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann. Intern. Med. 169, 467\u2013473 (2018).","journal-title":"Ann. Intern. Med."},{"key":"940_CR45","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1338634","volume":"14","author":"X Wang","year":"2024","unstructured":"Wang, X., Zheng, K. & Hao, Z. In-depth analysis of immune cell landscapes reveals differences between lung adenocarcinoma and lung squamous cell carcinoma. Front. Oncol. 14, 1338634 (2024).","journal-title":"Front. Oncol."},{"key":"940_CR46","volume":"2","author":"WE Lewis","year":"2021","unstructured":"Lewis, W. E. et al. Efficacy of targeted inhibitors in metastatic lung squamous cell carcinoma with EGFR or ALK alterations. JTO Clin. Res. Rep. 2, 100237 (2021).","journal-title":"JTO Clin. Res. Rep."},{"key":"940_CR47","unstructured":"Cancer Research UK. Treatment Options for Non Small Cell Lung Cancer (NSCLC). https:\/\/www.cancerresearchuk.org\/about-cancer\/lung-cancer\/treatment\/non-small-cell-lung-cancer (2023)."},{"key":"940_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.socscimed.2023.116357","volume":"338","author":"V Vo","year":"2023","unstructured":"Vo, V. et al. Multi-stakeholder preferences for the use of artificial intelligence in healthcare: a systematic review and thematic analysis. Soc. Sci. Med. 338, 116357 (2023).","journal-title":"Soc. Sci. Med."},{"key":"940_CR49","doi-asserted-by":"publisher","first-page":"255","DOI":"10.3389\/fmed.2019.00255","volume":"6","author":"R Mar\u00e9e","year":"2019","unstructured":"Mar\u00e9e, R. Open practices and resources for collaborative digital pathology. Front. Med. 6, 255 (2019).","journal-title":"Front. Med."},{"key":"940_CR50","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","volume":"155","author":"PF Whiting","year":"2011","unstructured":"Whiting, P. F. et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann. Intern. Med. 155, 529\u2013536 (2011).","journal-title":"Ann. Intern. Med."},{"key":"940_CR51","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1016\/j.jclinepi.2004.12.009","volume":"58","author":"A Flahault","year":"2005","unstructured":"Flahault, A., Cadilhac, M. & Thomas, G. Sample size calculation should be performed for design accuracy in diagnostic test studies. J. Clin. Epidemiol. 58, 859\u2013862 (2005).","journal-title":"J. Clin. Epidemiol."},{"key":"940_CR52","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.jclinepi.2021.02.011","volume":"135","author":"KIE Snell","year":"2021","unstructured":"Snell, K. I. E. et al. External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb. J. Clin. Epidemiol. 135, 79\u201389 (2021).","journal-title":"J. Clin. Epidemiol."},{"key":"940_CR53","unstructured":"Cancer Research UK. Lung Cancer Incidence Statistics. https:\/\/www.cancerresearchuk.org\/health-professional\/cancer-statistics\/statistics-by-cancer-type\/lung-cancer\/incidence#heading-Zero (2024)."}],"container-title":["npj Precision Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41698-025-00940-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41698-025-00940-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41698-025-00940-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T14:03:42Z","timestamp":1749305022000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41698-025-00940-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,7]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["940"],"URL":"https:\/\/doi.org\/10.1038\/s41698-025-00940-7","relation":{},"ISSN":["2397-768X"],"issn-type":[{"value":"2397-768X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,7]]},"assertion":[{"value":"24 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Author J.O. has previously acted as a paid consultant for Hardian Health but declares no non-financial competing interests. All other authors declare no financial or non-financial competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"166"}}