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In total, 192 primary tumors (&gt;\u20093\u00a0cm) and 462 lymph nodes (LN\u2009&gt;\u20090.5\u00a0cm) were analyzed. The pretreatment clinical features of these patients were recorded, and the radiomic features of their primary tumor and lymph node were extracted from PET\/CT imaging. The Spearman\u2019s relevance combined with the least absolute shrinkage and selection operator was used for radiomic feature selection. Five independent machine learning models (multi-layer perceptron, extreme Gradient Boosting, light gradient boosting machine, gradient boosting decision tree, and support vector machine) were tested as classifiers for model development. We developed the following three models to predict LNM: tumor PET\/CT-clinical (TPC), lymph PET\/CT-clinical (LPC), and tumor and lymph PET\/CT-clinical (TLPC). The performance of the models and the clinical node (cN) staging was evaluated using the ROC curve and confusion matrix analysis.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The ROC analysis showed that among the three models, the TLPC model had better predictive clinical utility and efficiency in predicting LNM of NSCLC (AUC\u2009=\u20090.93, accuracy\u2009=\u200985%; sensitivity\u2009=\u20090.93; specificity\u2009=\u20090.75) than both the TPC model (AUC\u2009=\u20090.54, accuracy\u2009=\u200950%; specificity\u2009=\u20090.38; sensitivity\u2009=\u20090.59) and the LPC model (AUC\u2009=\u20090.82, accuracy\u2009=\u200970%; specificity\u2009=\u20090.41; sensitivity\u2009=\u20090.92). The TLPC model also exhibited great potential in predicting the N2 stage in NSCLC (AUC\u2009=\u20090.94, accuracy\u2009=\u200979%; specificity\u2009=\u20090.64; sensitivity\u2009=\u20090.91).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The combination of CT and PET radiomic features of the primary tumor and lymph node showed great potential for predicting LNM of resectable T2-4 NSCLC. The TLPC model can non-invasively predict lymph node metastasis in NSCLC, which may be helpful for clinicians to develop more rational therapeutic strategies.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00432-022-04545-6","type":"journal-article","created":{"date-parts":[[2022,12,24]],"date-time":"2022-12-24T13:02:27Z","timestamp":1671886947000},"page":"247-261","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Developing a primary tumor and lymph node 18F-FDG PET\/CT-clinical (TLPC) model to predict lymph node metastasis of resectable T2-4 NSCLC"],"prefix":"10.1007","volume":"149","author":[{"given":"Meng","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingming","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,24]]},"reference":[{"issue":"4","key":"4545_CR1","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1200\/JCO.2005.05.303","volume":"23","author":"S Burdett","year":"2005","unstructured":"Burdett S, Stewart L, Auperin A et al (2005) Chemotherapy in non\u2013small-cell lung cancer: an update of an individual patient data meta-analysis. 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