ArticleEuropean radiology2021
Prediction of mediastinal lymph node metastasis based on
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 27 citations in OpenAlex.
- Automated FDG uptake/PET-CT fused scan diagnosis of various lymph node tumors using object detection AI techniques.Scientific reports · 2026Article
- Article
- Prediction of solitary ≤3-cm small adrenal metastasis based onOncology letters · 2026Article
- Machine learning-based prediction of N2 lymph node metastasis in non-small cell lung cancer.BMC pulmonary medicine · 2025Article
- Predictive value of radiomics modeling based on dynamic and staticFrontiers in oncology · 2025Article
- 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
- GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer.Physics in medicine and biology · 2024Article
- Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics.Cancer medicine · 2024Article
- Prediction of metastases in confusing mediastinal lymph nodes based on flourine-18 fluorodeoxyglucose (Quantitative imaging in medicine and surgery · 2024Article
- Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.Journal of imaging · 2024Review
- Predicting lymph node metastasis of clinical T1 non-small cell lung cancer: a brief review of possible methodologies and controversies.Frontiers in oncology · 2024Review
- PretreatmentCancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Predictive value ofEuropean journal of nuclear medicine and molecular imaging · 2023Article
- [Research Progress in Imaging-based Diagnosis of Benign and Malignant Enlarged Lymph Nodes in Non-small Cell Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2023Review
- Efficacy of radiomics model based on the concept of gross tumor volume and clinical target volume in predicting occult lymph node metastasis in non-small cell lung cancer.Frontiers in oncology · 2023Article
- The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges.Frontiers in oncology · 2023Review
- Predictive Value ofDiagnostics (Basel, Switzerland) · 2022Article
- Lung Cancer Prediction from Text Datasets Using Machine Learning.BioMed research international · 2022Article
- Pre-Operative Prediction of Mediastinal Node Metastasis Using Radiomics Model Based onFrontiers in medicine · 2021Article
- Development and Validation of aFrontiers in oncology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe purpose of this study was to develop a classification method based on support vector machine (SVM) to improve the diagnostic performance of
methodTwo hundred nineteen lymph nodes (37 metastatic) from 71 patients were evaluated in this study. SVM models were developed with 7 LN features. The area under the curve (AUC) and accuracy of 9 models were compared to select the best model. The best SVM model was simplified on the basis of the feature weights and value distribution to further suit the clinical application.
resultsThe maximum, minimum, and mean accuracy of the best model was 91.89% (68/74, 95% CI 83.11~96.54%), 66.22% (49/74, 95% CI 54.85~75.98%), and 80.09% (59,266/74,000, 95% CI 70.27~89.19%), respectively, with an AUC of 0.94, 0.66, and 0.81, respectively. The best SVM model was finally simplified into a score rule: LNs with scores more than 3.0 were considered as malignant ones, whereas LNs with scores less than 1.5 tended to be benign ones. For the LNs with scores within a range of 1.5-3.0, metastasis was suspected.
conclusionAn SVM model based on KEY POINTS: • The SVM model based on
Indexed as
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What OpenQuestion holds
Registered trials
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.