{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T06:16:08Z","timestamp":1789020968542,"version":"build-2803163510"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"publisher","award":["20212BAB206050"],"award-info":[{"award-number":["20212BAB206050"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Pulm Med"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The prognosis of non-small cell lung cancer (NSCLC) is substantially affected by lymph node metastasis (LNM), but there are no noninvasive, inexpensive methods of relatively high accuracy available to predict LNM in NSCLC patients.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Clinical data on NSCLC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors for LNM were recognized LASSO and multivariate logistic regression. Six predictive models were constructed with machine learning based on risk factors. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Subgroup analysis with different T-stages was performed on an optimal model. A webpage LNM risk calculator for optimal model was built using the Shinyapps.io platform.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We enrolled 64,012 NSCLC patients, of whom 26,611 (41.57%) had LNM. Using multivariate logistic regression, we finally identified 10 independent risk factors for LNM: age, sex, race, histology, primary site, grade, T stage, M stage, tumor size, and bone metastases. GLM is the optimal model among all six machine learning models in both the training and validation cohorts. Subgroup analyses revealed that GLM has good predictability for populations with different T staging. A webpage LNM risk calculator based on GLM was posted on the shinyapps.io platform (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/wubopredict.shinyapps.io\/dynnomapp\/\">https:\/\/wubopredict.shinyapps.io\/dynnomapp\/<\/jats:ext-link>).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The predictive model based on GLM can be used to precisely predict the probability of LNM in NSCLC patients, which was proven effective in all subgroup analyses according to T staging.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12890-024-03345-7","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T17:02:59Z","timestamp":1729616579000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Machine learning predictive models and risk factors for lymph node metastasis in non-small\u2002cell\u2002lung\u2002cancer"],"prefix":"10.1186","volume":"24","author":[{"given":"Bo","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihui","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuozheng","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weijun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maoyan","family":"Si","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiying","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhicheng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxiong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"issue":"1","key":"3345_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3322\/caac.21763","volume":"73","author":"RL Siegel","year":"2023","unstructured":"Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17\u201348.","journal-title":"CA Cancer J Clin"},{"key":"3345_CR2","doi-asserted-by":"publisher","first-page":"15009","DOI":"10.1038\/nrdp.2015.9","volume":"1","author":"C Gridelli","year":"2015","unstructured":"Gridelli C, Rossi A, Carbone DP, Guarize J, Karachaliou N, Mok T, et al. Non-small-cell lung cancer. Nat Rev Dis Primers. 2015;1:15009.","journal-title":"Nat Rev Dis Primers"},{"issue":"12","key":"3345_CR3","doi-asserted-by":"publisher","first-page":"1824","DOI":"10.1001\/jamaoncol.2021.4932","volume":"7","author":"AK Ganti","year":"2021","unstructured":"Ganti AK, Klein AB, Cotarla I, Seal B, Chou E. Update of incidence, prevalence, survival, and initial treatment in patients with Non-small Cell Lung Cancer in the US. JAMA Oncol. 2021;7(12):1824\u201332.","journal-title":"JAMA Oncol"},{"issue":"1","key":"3345_CR4","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.chest.2016.10.010","volume":"151","author":"FC Detterbeck","year":"2017","unstructured":"Detterbeck FC, Boffa DJ, Kim AW, Tanoue LT. The Eighth Edition Lung Cancer Stage classification. Chest. 2017;151(1):193\u2013203.","journal-title":"Chest"},{"issue":"3","key":"3345_CR5","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1016\/j.chest.2021.04.048","volume":"160","author":"G Martinez-Zayas","year":"2021","unstructured":"Martinez-Zayas G, Almeida FA, Yarmus L, Steinfort D, Lazarus DR, Simoff MJ, et al. Predicting Lymph Node Metastasis in Non-small Cell Lung Cancer: prospective external and temporal validation of the HAL and HOMER models. Chest. 2021;160(3):1108\u201320.","journal-title":"Chest"},{"issue":"22","key":"3345_CR6","doi-asserted-by":"publisher","first-page":"3785","DOI":"10.1200\/JCO.23.00867","volume":"41","author":"EG Dunne","year":"2023","unstructured":"Dunne EG, Fick CN, Jones DR. Mediastinal Staging in Non-small-cell Lung Cancer: saying Goodbye to Mediastinoscopy. J Clin Oncol. 2023;41(22):3785\u201390.","journal-title":"J Clin Oncol"},{"issue":"4","key":"3345_CR7","doi-asserted-by":"publisher","first-page":"340","DOI":"10.6004\/jnccn.2023.0020","volume":"21","author":"DS Ettinger","year":"2023","unstructured":"Ettinger DS, Wood DE, Aisner DL, Akerley W, Bauman JR, Bharat A, et al. NCCN Guidelines\u00ae Insights: Non-small Cell Lung Cancer, Version 2.2023. J Natl Compr Canc Netw. 2023;21(4):340\u201350.","journal-title":"J Natl Compr Canc Netw"},{"issue":"8","key":"3345_CR8","doi-asserted-by":"publisher","first-page":"1246","DOI":"10.1097\/JTO.0b013e31825871de","volume":"7","author":"T Koike","year":"2012","unstructured":"Koike T, Koike T, Yamato Y, Yoshiya K, Toyabe S. Predictive risk factors for mediastinal lymph node metastasis in clinical stage IA non-small-cell lung cancer patients. J Thorac Oncol. 2012;7(8):1246\u201351.","journal-title":"J Thorac Oncol"},{"issue":"20","key":"3345_CR9","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","volume":"132","author":"RC Deo","year":"2015","unstructured":"Deo RC. Machine learning in Medicine. Circulation. 2015;132(20):1920\u201330.","journal-title":"Circulation"},{"key":"3345_CR10","doi-asserted-by":"publisher","first-page":"e073800","DOI":"10.1136\/bmj-2022-073800","volume":"381","author":"AK Clift","year":"2023","unstructured":"Clift AK, Dodwell D, Lord S, Petrou S, Brady M, Collins GS, et al. Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study. BMJ. 2023;381:e073800.","journal-title":"BMJ"},{"key":"3345_CR11","unstructured":"Sabbagh A, Washington SL 3rd, Tilki D, Hong JC, Feng J et al. Development and External Validation of a machine learning model for prediction of Lymph Node Metastasis in patients with prostate Cancer. Eur Urol Oncol. 2023;S2588-9311(23)00038-X."},{"key":"3345_CR12","doi-asserted-by":"publisher","first-page":"106706","DOI":"10.1016\/j.phrs.2023.106706","volume":"189","author":"A Bakrania","year":"2023","unstructured":"Bakrania A, Joshi N, Zhao X, Zheng G, Bhat M. Artificial intelligence in liver cancers: decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases. Pharmacol Res. 2023;189:106706.","journal-title":"Pharmacol Res"},{"key":"3345_CR13","unstructured":"\u0160tef\u00e1nia GALLOV\u00c1, Member IAENG, Augustin M, Altahr SSA. Machine Learning with Missing Attributes Values Methods Implementation.Machine Learning with Missing Attributes Values Methods Implementation. World Congress on Engineering and Computer Science (WCECS 2015); San Francisco, USA; 2015."},{"key":"3345_CR14","doi-asserted-by":"publisher","first-page":"831848","DOI":"10.3389\/fimmu.2022.831848","volume":"13","author":"L Chen","year":"2022","unstructured":"Chen L, Kong X, Huang S, Su Z, Zhu M, Fang Y, et al. Preoperative breast Immune Prognostic Index as prognostic factor predicts the clinical outcomes of breast Cancer patients receiving Neoadjuvant Chemotherapy. Front Immunol. 2022;13:831848.","journal-title":"Front Immunol"},{"key":"3345_CR15","doi-asserted-by":"publisher","first-page":"1083569","DOI":"10.3389\/fendo.2022.1083569","volume":"13","author":"Z Wang","year":"2022","unstructured":"Wang Z, Xu C, Liu W, Zhang M, Zou J, Shao M, et al. A clinical prediction model for predicting the risk of liver metastasis from renal cell carcinoma based on machine learning. Front Endocrinol (Lausanne). 2022;13:1083569.","journal-title":"Front Endocrinol (Lausanne)"},{"key":"3345_CR16","doi-asserted-by":"publisher","first-page":"1054358","DOI":"10.3389\/fendo.2022.1054358","volume":"13","author":"X Feng","year":"2022","unstructured":"Feng X, Hong T, Liu W, Xu C, Li W, Yang B, et al. Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma. Front Endocrinol (Lausanne). 2022;13:1054358.","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"5","key":"3345_CR17","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1097\/RLU.0000000000004110","volume":"47","author":"SW Lee","year":"2022","unstructured":"Lee SW, Kim SJ. Is delayed image of 18F-FDG PET\/CT necessary for Mediastinal Lymph Node staging in Non-small Cell Lung Cancer patients? Clin Nucl Med. 2022;47(5):414\u201321.","journal-title":"Clin Nucl Med"},{"key":"3345_CR18","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","volume":"13","author":"K Kourou","year":"2015","unstructured":"Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8\u201317.","journal-title":"Comput Struct Biotechnol J"},{"issue":"2","key":"3345_CR19","doi-asserted-by":"publisher","first-page":"176","DOI":"10.21037\/hbsn-20-466","volume":"11","author":"J Zeng","year":"2022","unstructured":"Zeng J, Zeng J, Lin K, Lin H, Wu Q, Guo P, et al. Development of a machine learning model to predict early recurrence for hepatocellular carcinoma after curative resection. Hepatobiliary Surg Nutr. 2022;11(2):176\u201387.","journal-title":"Hepatobiliary Surg Nutr"},{"key":"3345_CR20","doi-asserted-by":"publisher","first-page":"851552","DOI":"10.3389\/fonc.2022.851552","volume":"12","author":"W Li","year":"2022","unstructured":"Li W, Wang B, Dong S, Xu C, Song Y, Qiao X, et al. A novel nomogram for prediction and evaluation of lymphatic metastasis in patients with renal cell carcinoma. Front Oncol. 2022;12:851552.","journal-title":"Front Oncol"},{"issue":"3","key":"3345_CR21","doi-asserted-by":"publisher","first-page":"2548","DOI":"10.21037\/apm-20-1756","volume":"10","author":"Y Tian","year":"2021","unstructured":"Tian Y, He Y, Li X, Liu X. Novel nomograms to predict lymph node metastasis and distant metastasis in resected patients with early-stage non-small cell lung cancer. Ann Palliat Med. 2021;10(3):2548\u201366.","journal-title":"Ann Palliat Med"},{"issue":"3","key":"3345_CR22","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/s00592-019-01385-8","volume":"57","author":"M Finessi","year":"2020","unstructured":"Finessi M, Bisi G, Deandreis D. Hyperglycemia and 18F-FDG PET\/CT, issues and problem solving: a literature review. Acta Diabetol. 2020;57(3):253\u201362.","journal-title":"Acta Diabetol"},{"issue":"1","key":"3345_CR23","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.athoracsur.2018.02.026","volume":"106","author":"Y Gu","year":"2018","unstructured":"Gu Y, She Y, Xie D, Dai C, Ren Y, Fan Z, et al. A texture analysis-based prediction model for Lymph Node Metastasis in Stage IA Lung Adenocarcinoma. Ann Thorac Surg. 2018;106(1):214\u201320.","journal-title":"Ann Thorac Surg"},{"issue":"9","key":"3345_CR24","doi-asserted-by":"publisher","first-page":"5772","DOI":"10.1245\/s10434-022-11911-8","volume":"29","author":"X Huang","year":"2022","unstructured":"Huang X, Luo Z, Liang W, Xie G, Lang X, Gou J, et al. Survival nomogram for young breast Cancer patients based on the SEER database and an external validation cohort. Ann Surg Oncol. 2022;29(9):5772\u201381.","journal-title":"Ann Surg Oncol"},{"key":"3345_CR25","doi-asserted-by":"publisher","first-page":"105644","DOI":"10.1016\/j.phrs.2021.105644","volume":"169","author":"C Jiang","year":"2021","unstructured":"Jiang C, Liu Y, Wen S, Xu C, Gu L. In silico development and clinical validation of novel 8 gene signature based on lipid metabolism related genes in colon adenocarcinoma. Pharmacol Res. 2021;169:105644.","journal-title":"Pharmacol Res"},{"key":"3345_CR26","doi-asserted-by":"publisher","first-page":"683282","DOI":"10.3389\/fonc.2021.683282","volume":"11","author":"Y Qi","year":"2021","unstructured":"Qi Y, Wu S, Tao L, Shi Y, Yang W, Zhou L, et al. Development of Nomograms for Predicting Lymph Node Metastasis and distant metastasis in newly diagnosed T1-2 Non-small Cell Lung Cancer: a Population-based analysis. Front Oncol. 2021;11:683282.","journal-title":"Front Oncol"},{"issue":"16","key":"3345_CR27","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1111\/1759-7714.14892","volume":"14","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Lian X, Yue H, Xiang D, Niu Z. Identification and validation of the model consisting of DDX49, EGFR, and T-stage as a possible risk factor for lymph node metastasis in patients with lung cancer. Thorac Cancer. 2023;14(16):1492\u20139.","journal-title":"Thorac Cancer"},{"issue":"5","key":"3345_CR28","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/s10585-019-09985-y","volume":"36","author":"J Yang","year":"2019","unstructured":"Yang J, Peng A, Wang B, Gusdon AM, Sun X, Jiang G, et al. The prognostic impact of lymph node metastasis in patients with non-small cell lung cancer and distant organ metastasis. Clin Exp Metastasis. 2019;36(5):457\u201366.","journal-title":"Clin Exp Metastasis"},{"issue":"3","key":"3345_CR29","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.lungcan.2010.06.008","volume":"71","author":"R Kanzaki","year":"2011","unstructured":"Kanzaki R, Higashiyama M, Fujiwara A, Tokunaga T, Maeda J, Okami J, et al. Occult mediastinal lymph node metastasis in NSCLC patients diagnosed as clinical N0-1 by preoperative integrated FDG-PET\/CT and CT: risk factors, pattern, and histopathological study. Lung Cancer. 2011;71(3):333\u20137.","journal-title":"Lung Cancer"},{"issue":"3","key":"3345_CR30","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1111\/1759-7714.12970","volume":"10","author":"B Chen","year":"2019","unstructured":"Chen B, Wang X, Yu X, Xia WJ, Zhao H, Li XF, et al. Lymph node metastasis in Chinese patients with clinical T1 non-small cell lung cancer: a multicenter real-world observational study. Thorac Cancer. 2019;10(3):533\u201342.","journal-title":"Thorac Cancer"},{"issue":"8","key":"3345_CR31","doi-asserted-by":"publisher","first-page":"2610","DOI":"10.1007\/s00268-021-06136-x","volume":"45","author":"HY Deng","year":"2021","unstructured":"Deng HY, Liu C, Qiu XM, Zhu DX, Tang XJ, Zhou Q. Assessing differences in Lymph Node Metastasis Based upon Sex in early non-small cell Lung Cancer. World J Surg. 2021;45(8):2610\u20138.","journal-title":"World J Surg"},{"issue":"3","key":"3345_CR32","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1007\/s00268-018-4848-7","volume":"43","author":"HY Deng","year":"2019","unstructured":"Deng HY, Zeng M, Li G, Alai G, Luo J, Liu LX, et al. Lung adenocarcinoma has a higher risk of Lymph Node Metastasis than squamous cell carcinoma: a propensity score-matched analysis. World J Surg. 2019;43(3):955\u201362.","journal-title":"World J Surg"},{"issue":"4","key":"3345_CR33","doi-asserted-by":"publisher","first-page":"299","DOI":"10.3322\/caac.21671","volume":"71","author":"E Sosa","year":"2021","unstructured":"Sosa E, D\u2019Souza G, Akhtar A, Sur M, Love K, Duffels J, et al. Racial and socioeconomic disparities in lung cancer screening in the United States: a systematic review. CA Cancer J Clin. 2021;71(4):299\u2013314.","journal-title":"CA Cancer J Clin"},{"issue":"1","key":"3345_CR34","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1023\/A:1022207917249","volume":"22","author":"SM Gadgeel","year":"2003","unstructured":"Gadgeel SM, Kalemkerian GP. Racial differences in lung cancer. Cancer Metastasis Rev. 2003;22(1):39\u201346.","journal-title":"Cancer Metastasis Rev"}],"container-title":["BMC Pulmonary Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12890-024-03345-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12890-024-03345-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12890-024-03345-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T17:03:23Z","timestamp":1729616603000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcpulmmed.biomedcentral.com\/articles\/10.1186\/s12890-024-03345-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,22]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["3345"],"URL":"https:\/\/doi.org\/10.1186\/s12890-024-03345-7","relation":{},"ISSN":["1471-2466"],"issn-type":[{"value":"1471-2466","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,22]]},"assertion":[{"value":"21 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent"}},{"value":"For this type of study formal consent is not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"526"}}