{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:50:39Z","timestamp":1785919839126,"version":"3.56.0"},"reference-count":32,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Objectives: Precise lymph node staging remains a cornerstone in the management of early-stage and locally advanced non-small cell lung cancer (NSCLC), directly influencing surgical planning and multimodal therapy. Despite the widespread use of 2-[18F]FDG PET\/CT, occult nodal metastases frequently lead to unexpected upstaging after surgery, potentially affecting prognosis and therapeutic strategies. This study aimed to investigate whether radiomic features derived from preoperative PET\/CT scans can predict nodal involvement in patients with early-stage lung cancer. Methods: A retrospective analysis was conducted on 124 patients with cT1N0 NSCLC who underwent 2-[18F]FDG PET\/CT scans as part of the preoperative workup, followed by anatomical lung resection and systematic mediastinal lymph node dissection. Radiomic features were extracted from PET predictive of pathological nodal upstaging. Results: During the study period, 67 patients who underwent anatomical lung resection for early-stage lung cancer demonstrated unexpected nodal metastasis; a continuous series of 57 patients with the same clinical TMN was enrolled as a control group. Several radiomic parameters were significantly associated with nodal upstaging. According to variable importance (VIMP) analysis, metabolic tumor volume (MTV), total lesion glycolysis (TLG), run-length non-uniformity (RLNU), and gray-level non-uniformity (GLNU) emerged as the strongest predictors of lymph node involvement. Conclusions: Although the clinical utility of these findings remains to be validated, radiomic analysis of 2-[18F]FDG PET\/CT imaging offers non-invasive biomarkers that may enhance the preoperative prediction of nodal involvement in early-stage NSCLC. Integrating radiomics into clinical workflows could improve surgical decision-making, refine patient selection, and reduce the incidence of unforeseen nodal upstaging.<\/jats:p>","DOI":"10.3390\/cancers18152470","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:02:01Z","timestamp":1785510121000},"page":"2470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Driven Radiomics for an Early-Stage Predictive Model of Nodal Upstaging in Thoracic Oncology"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4234-4456","authenticated-orcid":false,"given":"Ivan","family":"Lomangino","sequence":"first","affiliation":[{"name":"Cardiothoracic Department, Thoracic Surgery Unit, Spedali Civili, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1855-5456","authenticated-orcid":false,"given":"Giacomo","family":"Grisorio","sequence":"additional","affiliation":[{"name":"Cardiothoracic Department, Thoracic Surgery Unit, Spedali Civili, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0810-6494","authenticated-orcid":false,"given":"Domenico","family":"Albano","sequence":"additional","affiliation":[{"name":"Nuclear Medicine Department, Universit\u00e0 degli Studi di Brescia, 25121 Brescia, Italy"},{"name":"Nuclear Medicine Department, ASST Spedali Civili of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Vecchiarelli","sequence":"additional","affiliation":[{"name":"Cardiothoracic Department, Thoracic Surgery Unit, Spedali Civili, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3928-5966","authenticated-orcid":false,"given":"Matteo","family":"Rota","sequence":"additional","affiliation":[{"name":"Units of Biostatistics and Biomathematics and Bioinformatics, Department of Molecular and Translational Medicine, University of Brescia, 25121 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8703-9498","authenticated-orcid":false,"given":"Matteo","family":"Baldi","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Universit\u00e0 degli Studi di Padova, 35122 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Letizia","family":"Perri","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Universit\u00e0 degli Studi di Modena e Reggio Emilia, 41121 Modena, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauro Roberto","family":"Benvenuti","sequence":"additional","affiliation":[{"name":"Cardiothoracic Department, Thoracic Surgery Unit, Spedali Civili, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6922-1934","authenticated-orcid":false,"given":"Salvatore","family":"Grisanti","sequence":"additional","affiliation":[{"name":"Medical Oncology, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health Medical, ASST-Spedali Civili, University of Brescia, 25121 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Bertagna","sequence":"additional","affiliation":[{"name":"Nuclear Medicine Department, Universit\u00e0 degli Studi di Brescia, 25121 Brescia, Italy"},{"name":"Nuclear Medicine Department, ASST Spedali Civili of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"ref_1","unstructured":"WHO (2026, June 18). Cancer Factsheets. Available online: https:\/\/gco.iarc.who.int\/today\/en\/fact-sheets-cancers."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1097\/CM9.0000000000002729","article-title":"Persistent increase and improved survival of stage I lung cancer based on a large-scale real-world sample of 26,226 cases","volume":"136","author":"Wang","year":"2023","journal-title":"Chin. Med. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1016\/j.jtho.2024.02.011","article-title":"The International Association for the Study of Lung Cancer Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groups in the Forthcoming (Ninth) Edition of the TNM Classification for Lung Cancer","volume":"19","author":"Nishimura","year":"2024","journal-title":"J. Thorac. Oncol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1002\/mp.16177","article-title":"Predicting N2 lymph node metastasis in presurgical stage I-II non-small cell lung cancer using multiview radiomics and deep learning method","volume":"50","author":"Zhang","year":"2023","journal-title":"Med. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.lungcan.2022.12.013","article-title":"Preoperative PET-SUVmax and volume based PET parameters of the primary tumor fail to predict nodal upstaging in early-stage lung cancer","volume":"176","author":"Okumus","year":"2022","journal-title":"Lung Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1016\/j.athoracsur.2023.04.013","article-title":"Impact of FDG PET Standardized Uptake Value in Resected Clinical Stage IA Non-Small Cell Lung Cancer","volume":"117","author":"Tapias","year":"2023","journal-title":"Ann. Thorac. Surg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"592","DOI":"10.21037\/jtd-20-2267","article-title":"Quality assessment of robot assisted thoracic surgical resection of non-small cell lung cancer: Nodal upstaging and mediastinal recurrence","volume":"13","author":"Shahin","year":"2021","journal-title":"J. Thorac. Dis."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zheng, K., Wang, X., Jiang, C., Tang, Y., Fang, Z., Hou, J., Zhu, Z., and Hu, S. (2021). Pre-Operative Prediction of Mediastinal Node Metastasis Using Radiomics Model Based on 18F-FDG PET\/CT of the Primary Tumor in Non-Small Cell Lung Cancer Patients. Front. Med., 8.","DOI":"10.3389\/fmed.2021.673876"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.athoracsur.2004.06.041","article-title":"Meta-Analysis of Positron Emission Tomographic and Computed Tomographic Imaging in Detecting Mediastinal Lymph Node Metastases in Nonsmall Cell Lung Cancer","volume":"79","author":"Birim","year":"2005","journal-title":"Ann. Thorac. Surg."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1001\/jama.2015.2365","article-title":"FDG-PET\/CT Imaging for Mediastinal Staging in Patients with Potentially Resectable Non\u2013Small Cell Lung Cancer","volume":"313","author":"Baldwin","year":"2015","journal-title":"JAMA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1007\/s12149-023-01888-z","article-title":"Impact of preoperative [18F]FDG PET\/CT vs. contrast-enhanced CT in the staging and survival of patients with clinical stage I and II non-small cell lung cancer: A 10-year follow-up study","volume":"38","author":"Lin","year":"2023","journal-title":"Ann. Nucl. Med."},{"key":"ref_12","unstructured":"NIH (2026, May 04). Cancer Stat Facts: Lung and Bronchus Cancer, Available online: https:\/\/seer.cancer.gov\/statfacts\/html\/lungb.html."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Krishna, S., Sertic, A., Liu, Z., Liu, Z., Darling, G.E., Yeung, J., Wong, R., Chen, E.X., Kalimuthu, S., and Allen, M.J. (2023). Combination of clinical, radiomic, and \u201cdelta\u201d radiomic features in survival prediction of metastatic gastroesophageal adenocarcinoma. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.892393"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s40644-019-0214-8","article-title":"Quantitative computed tomography texture analysis: Can it improve diagnostic accuracy to differentiate malignant lymph nodes?","volume":"19","author":"Shin","year":"2019","journal-title":"Cancer Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1007\/s00330-014-3420-6","article-title":"Quantitative CT texture and shape analysis: Can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer?","volume":"25","author":"Bayanati","year":"2014","journal-title":"Eur. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s00464-018-6288-8","article-title":"Nodal upstaging evaluation in NSCLC patients treated by robotic lobectomy","volume":"33","author":"Zirafa","year":"2018","journal-title":"Surg. Endosc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109","DOI":"10.2214\/AJR.17.19074","article-title":"Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis of Lung Adenocarcinoma","volume":"211","author":"Zhong","year":"2018","journal-title":"Am. J. Roentgenol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6049","DOI":"10.1007\/s00330-019-06084-0","article-title":"Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT?","volume":"29","author":"Wang","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106832","DOI":"10.1016\/j.crad.2025.106832","article-title":"A machine learning-based 18F-FDG PET\/CT multi-modality fusion radiomics model to predict Mediastinal-Hilar lymph node metastasis in NSCLC: A multi-centre study","volume":"83","author":"Zhai","year":"2025","journal-title":"Clin. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1016\/j.acra.2024.11.037","article-title":"Non-invasive Prediction of Lymph Node Metastasis in NSCLC Using Clinical, Radiomics, and Deep Learning Features From 18F-FDG PET\/CT Based on Interpretable Machine Learning","volume":"32","author":"Duan","year":"2024","journal-title":"Acad. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3838","DOI":"10.1007\/s00259-025-07740-y","article-title":"Development and external validation of a FDG PET-based radiomics model predicting occult lymph node metastasis in non-small cell lung cancer patients","volume":"53","author":"Bourbonne","year":"2026","journal-title":"Eur. J. Nucl. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.crad.2022.08.140","article-title":"Preoperative prediction of mediastinal lymph node metastasis in non-small cell lung cancer based on 18F-FDG PET\/CT radiomics","volume":"78","author":"Huang","year":"2022","journal-title":"Clin. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4786","DOI":"10.1158\/0008-5472.CAN-18-0125","article-title":"LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity","volume":"78","author":"Nioche","year":"2018","journal-title":"Cancer Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.ymeth.2020.05.023","article-title":"2-[18F]FDG PET\/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease","volume":"188","author":"Vali","year":"2021","journal-title":"Methods"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1002\/cncr.35128","article-title":"Lung cancer statistics, 2023","volume":"130","author":"Kratzer","year":"2024","journal-title":"Cancer"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1038\/s41598-017-00426-y","article-title":"PET Radiomics in NSCLC: State of the art and a proposal for harmonization of methodology","volume":"7","author":"Sollini","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.lungcan.2013.04.012","article-title":"Risk factors for predicting the occult nodal metastasis in T1\u20132N0M0 NSCLC patients staged by PET\/CT: Potential value in the clinic","volume":"81","author":"Li","year":"2013","journal-title":"Lung Cancer"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1093\/ejcts\/ezs604","article-title":"The maximum standardized uptake value of fluorodeoxyglucose positron emission tomography of the primary tumour is a good predictor of pathological nodal involvement in clinical N0 non-small-cell lung cancer","volume":"44","author":"Miyasaka","year":"2012","journal-title":"Eur. J. Cardio-Thorac. Surg."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3961","DOI":"10.1007\/s00259-021-05242-1","article-title":"Structural and functional radiomics for lung cancer","volume":"48","author":"Wu","year":"2021","journal-title":"Eur. J. Nucl. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2051","DOI":"10.1007\/s00259-014-2831-6","article-title":"Metabolic parameters using 18F-FDG PET\/CT correlate with occult lymph node metastasis in squamous cell lung carcinoma","volume":"41","author":"Kim","year":"2014","journal-title":"Eur. J. Nucl. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e07629","DOI":"10.1002\/advs.202507629","article-title":"Integration of Genetic and Imaging Data for Alzheimer\u2019s Disease Diagnosis and Interpretation","volume":"12","author":"Wang","year":"2025","journal-title":"Adv. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e13696","DOI":"10.1002\/acm2.13696","article-title":"Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy","volume":"23","author":"Falahatpour","year":"2022","journal-title":"J. Appl. Clin. Med. Phys."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/15\/2470\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:25:56Z","timestamp":1785918356000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/15\/2470"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,31]]},"references-count":32,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["cancers18152470"],"URL":"https:\/\/doi.org\/10.3390\/cancers18152470","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,31]]}}}