ArticleEJNMMI research2017
Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from
Article in EJNMMI research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 1 of them a synthesis that pooled it.
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Who cites it
95 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Pooled it
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- The Current Landscape of Artificial Intelligence in Positron Emission Tomography (PET) Imaging Across the Cancer Continuum.Journal of clinical medicine · 2026Review
- Artificial intelligence for perioperative precision in surgical oncology.Frontiers in surgery · 2026Review
- Secure and interpretable lung cancer prediction model using mapreduce private blockchain federated learning and XAI.Scientific reports · 2025Article
- The study of dual-phaseScientific reports · 2025Article
- Machine learning-based prediction of N2 lymph node metastasis in non-small cell lung cancer.BMC pulmonary medicine · 2025Article
- Review and recommendations for using artificial intelligence in intracoronary optical coherence tomography analysis.European heart journal. Digital health · 2025Review
- An adaptive and lightweight YOLOv5 detection model for lung tumor in PET/CT images.Scientific reports · 2024Article
- A Hybrid CNN-Transformer Model for Predicting N Staging and Survival in Non-Small Cell Lung Cancer Patients Based on CT-Scan.Tomography (Ann Arbor, Mich.) · 2024Article
- Machine Learning and Texture Analysis of [Biomedicines · 2024Article
- LNAS: a clinically applicable deep-learning system for mediastinal enlarged lymph nodes segmentation and station mapping without regard to the pathogenesis using unenhanced CT images.La Radiologia medica · 2024Article
- Machine learning approaches in the interpretation of endobronchial ultrasound images: a comparative analysis.Surgical endoscopy · 2023Article
- Enhancing the Evidence with Algorithms: How Artificial Intelligence Is Transforming Forensic Medicine.Diagnostics (Basel, Switzerland) · 2023Review
- A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare.Journal of personalized medicine · 2023Review
- Recent Advances in Deep Learning and Medical Imaging for Head and Neck Cancer Treatment: MRI, CT, and PET Scans.Cancers · 2023Review
35 more citing papers are in PubMed but not listed here.
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8 authors.
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Abstract
backgroundThis study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from
resultsFor the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN's sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors.
conclusionsThe present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.
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