ArticleAging2020
Development of a machine learning-based multimode diagnosis system for lung cancer.
Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.
- Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types.Cancer medicine · 2025Pooled it
- The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis.PloS one · 2023Pooled it
- Research progress of machine learning applications in gastric cancer diagnosis and therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Assessing in-hospital mortality risk in ICU lung cancer patients using machine learning: An analysis based on the MIMIC-IV database.PloS one · 2026Article
- Peripheral blood tumor marker levels can indicate the location of lung cancer metastasis.Oncology letters · 2025Article
- Early Detection of Lung Cancer: A Review of Innovative Milestones and Techniques.Journal of clinical medicine · 2025Review
- A Decision Tree Classification Algorithm Based on Two-Term RS-Entropy.Entropy (Basel, Switzerland) · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis.Cancers · 2024Review
- Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit.PloS one · 2023Article
- Predicting pathologic complete response in locally advanced rectal cancer patients after neoadjuvant therapy: a machine learning model using XGBoost.International journal of colorectal disease · 2022Article
- Radiomics for differentiating minimally invasive adenocarcinoma from precursor lesions in pure ground-glass opacities on chest computed tomography.The British journal of radiology · 2022Article
- Diagnostic value of circulating genetically abnormal cells to support computed tomography for benign and malignant pulmonary nodules.BMC cancer · 2022Article
- Using a risk model for probability of cancer in pulmonary nodules.Thoracic cancer · 2021Article
- Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis system for lung cancer, in which three machine learning approaches including decision tree C5.0, artificial neural network (ANN) and support vector machine (SVM) were involved. The area under the curve (AUC) was employed to evaluate their decision powers. In the first layer, the AUCs of C5.0, ANN and SVM were 0.676, 0.736 and 0.640, ANN was better than C5.0 and SVM. In the second layer, ANN was similar with SVM but superior to C5.0 supported by the AUCs of 0.804, 0.889 and 0.825. Much higher AUCs of 0.908, 0.910 and 0.849 were identified in the third layer, where the highest sensitivity of 94.12% was found in C5.0. These data proposed a three-layer diagnosis system for lung cancer: ANN was used as a broad-spectrum screening subsystem basing on 14 epidemiological data and clinical symptoms, which was firstly adopted to screen high-risk groups; then, combining with additional 5 tumor biomarkers, ANN was used as an auxiliary diagnosis subsystem to determine the suspected lung cancer patients; C5.0 was finally employed to confirm lung cancer patients basing on 22 CT nodule-based radiomic features.
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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.