ArticleJournal of cancer research and clinical oncology2023
Developing a primary tumor and lymph node 18F-FDG PET/CT-clinical (TLPC) model to predict lymph node metastasis of resectable T2-4 NSCLC.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Diagnostic accuracy of CT and PET/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer.European radiology · 2025Pooled it
- Predictive value of primary tumorCancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Relationship between expression of derived factor-1CXC chemokine receptor 4 in tumor tissues of patients with non-small cell lung cancer and postoperative lymph node metastasis.Journal of molecular histology · 2026Article
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- Independent Validation of a Machine Learning Classifier for Predicting Mediastinal Lymph Node Metastases in Non-Small Cell Lung Cancer Using Routinely Obtainable [Current oncology (Toronto, Ont.) · 2025Article
- Prediction of pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer usingBMC cancer · 2025Article
- Distinguishing lymphoma from benign lymph node diseases in fever of unknown origin using PET/CT radiomics.EJNMMI research · 2024Article
- Regularity and correlation analysis of regional lymph node metastasis in nonoperative patients with non-small cell lung cancer based on positron emission tomography/computed tomography images.Radiation oncology (London, England) · 2024Article
- Using artificial intelligence based imaging to predict lymph node metastasis in non-small cell lung cancer: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2024Article
- More than meets the eye: 2-[European journal of nuclear medicine and molecular imaging · 2024Article
- Predicting lymph node metastasis of clinical T1 non-small cell lung cancer: a brief review of possible methodologies and controversies.Frontiers in oncology · 2024Review
- Evaluating peritumoral and intratumoral radiomics signatures for predicting lymph node metastasis in surgically resectable non-small cell lung cancer.Frontiers in oncology · 2024Article
- Clinical application ofJapanese journal of radiology · 2024Review
- Differential diagnosis of lymphoma withJournal of cancer research and clinical oncology · 2023Observational
- Radiomic Models Predict Tumor Microenvironment Using Artificial Intelligence-the Novel Biomarkers in Breast Cancer Immune Microenvironment.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
purposeThe goal of this study was to investigate whether the combined PET/CT radiomic features of the primary tumor and lymph node could predict lymph node metastasis (LNM) of resectable non-small cell lung cancer (NSCLC) in stage T2-4.
methodsThis retrospective study included 192 NSCLC patients who underwent tumor and node dissection between August 2016 and December 2017 and underwent
resultsThe ROC analysis showed that among the three models, the TLPC model had better predictive clinical utility and efficiency in predicting LNM of NSCLC (AUC = 0.93, accuracy = 85%; sensitivity = 0.93; specificity = 0.75) than both the TPC model (AUC = 0.54, accuracy = 50%; specificity = 0.38; sensitivity = 0.59) and the LPC model (AUC = 0.82, accuracy = 70%; specificity = 0.41; sensitivity = 0.92). The TLPC model also exhibited great potential in predicting the N2 stage in NSCLC (AUC = 0.94, accuracy = 79%; specificity = 0.64; sensitivity = 0.91).
conclusionThe 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.
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