ArticleEuropean journal of nuclear medicine and molecular imaging2023
A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [
Article in European journal of nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancer.European journal of medical research · 2023Trial
- FDG-PET/CT data enhances machine learning prediction of occult lymph-node metastasis in lung adenocarcinoma.Journal of thoracic disease · 2026Article
- Article
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- 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
- Machine learning-based prediction of N2 lymph node metastasis in non-small cell lung cancer.BMC pulmonary medicine · 2025Article
- Prediction of Lung Cancer Metastasis Using Machine Learning Models Based on Clinical Laboratory Data.Cancer reports (Hoboken, N.J.) · 2025Article
- Review
- MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer.Clinical and translational science · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Prediction of lymph node metastasis in lung adenocarcinoma using a PET/CT radiomics-based ensemble learning model and its pathological basis.Frontiers in oncology · 2025Article
- Balancing oncological control and immune preservation in the immunotherapy era: revisiting lymph node dissection in non-small cell lung cancer.Frontiers in oncology · 2025Review
- Lymph nodes rather than pleural metabolic activity inTranslational lung cancer research · 2024Article
- The ratio of lymph node eluate/serum cytokeratin 21-1 is a potential predictor of lymph node metastasis in lung adenocarcinoma.Journal of thoracic disease · 2024Article
- Prediction of metastases in confusing mediastinal lymph nodes based on flourine-18 fluorodeoxyglucose (Quantitative imaging in medicine and surgery · 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
- Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.Cancer management and research · 2024Review
- Lung Cancer Staging: Imaging and Potential Pitfalls.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn patients with non-small cell lung cancer (NSCLC), accuracy of [
methodsMonocentric retrospective analysis of pretherapeutic [
resultsN2/3 disease was present in 190 patients (39%; training + test, 37%; validation, 46%; p = 0.09). A gradient boosting classifier (GBM) with 10 features was selected as the final model based on test AUC of 0.91 (95% confidence interval, 0.87-0.94). Validation AUC was 0.94 (0.89-0.98). At a target sensitivity of approx. 90%, test/validation accuracy of the GBM was 0.78/0.87. This was significantly higher than the accuracy based on "mediastinal LN uptake > mediastinum" (0.7/0.75; each p < 0.05) or combined PET/CT criteria (PET positive and/or LN short axis diameter > 10 mm; 0.68/0.75; each p < 0.001). Harmonization of PET images between the two scanners affected SUVmax and visual assessment of the LNs but did not diminish the AUC of the GBM.
conclusionsA machine learning model based on routinely available variables from [
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.