ArticleRadiology2014
Neural networks for nodal staging of non-small cell lung cancer with FDG PET and CT: importance of combining uptake values and sizes of nodes and primary tumor.
Article in Radiology, 2014. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
19 citing papers in PubMed, 50 citations in OpenAlex.
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- 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
- A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer.Indian journal of surgical 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
- AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis.Cancers · 2024Review
- A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [European journal of nuclear medicine and molecular imaging · 2023Article
- A machine-learning-based combination of criteria to detect bladder cancer lymph node metastasis on [European radiology · 2023Article
- Artificial Neural Networks in Lung Cancer Research: A Narrative Review.Journal of clinical medicine · 2023Review
- Application of Artificial Neural Network to PreoperativeFrontiers in medicine · 2021Article
- Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network.Scientific reports · 2019Article
- Automated AJCC (7th edition) staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN).Health information science and systems · 2019Article
- [An Overview of the Application of Artificial Neural Networks in Lung Cancer Research].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2019Review
- A Prediction Model to Help with the Assessment of Adenopathy in Lung Cancer: HAL.American journal of respiratory and critical care medicine · 2017Article
- An overview of the use of artificial neural networks in lung cancer research.Journal of thoracic disease · 2017Article
- Prediction of medial tibiofemoral compartment joint space loss progression using volumetric cartilage measurements: Data from the FNIH OA biomarkers consortium.European radiology · 2017Article
- Vascular endothelial growth factor C complements the ability of positron emission tomography to predict nodal disease in lung cancer.The Journal of thoracic and cardiovascular surgery · 2015Article
- External validation of a prediction model for pathologic N2 among patients with a negative mediastinum by positron emission tomography.Journal of thoracic disease · 2015Article
- Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology.Molecular imagingReview
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo evaluate the effect of adding lymph node size to three previously explored artificial neural network (ANN) input parameters (primary tumor maximum standardized uptake value or tumor uptake, tumor size, and nodal uptake at N1, N2, and N3 stations) in the structure of the ANN. The goal was to allow the resulting ANN structure to relate lymph node uptake for size to primary tumor uptake for size in the determination of the status of nodes as human readers do. MATERIALS AND
methodsThis prospective study was approved by the institutional review board, and informed consent was obtained from all participants. The authors developed a back-propagation ANN with one hidden layer and eight processing units. The data set used to train the network included node and tumor size and uptake from 133 patients with non-small cell lung cancer with surgically proved N status. Statistical analysis was performed with the paired t test.
resultsThe ANN correctly predicted the N stage in 99.2% of cases, compared with 72.4% for the expert reader (P < .001). In categorization of N0 and N1 versus N2 and N3 disease, the ANN performed with 99.2% accuracy versus 92.2% for the expert reader (P < .001).
conclusionThe ANN is 99.2% accurate in predicting surgical-pathologic nodal status with use of four fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT)-derived parameters. Malignant and benign inflammatory lymph nodes have overlapping appearances at FDG PET/CT but can be differentiated by ANNs when the crucial input of node size is used.
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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.