ArticleJournal of thoracic disease2018
Predicting malignancy of pulmonary ground-glass nodules and their invasiveness by random forest.
Article in Journal of thoracic disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Significance of the Lesion-Pleura Relationship in Differentiating Peripheral Inflammatory Lesions and Lung Cancers.Journal of inflammation research · 2025Article
- Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.BMC medical imaging · 2024Article
- Evaluation of the invasiveness of pure ground-glass nodules based on dual-head ResNet technique.BMC cancer · 2024Article
- The value of predicting the invasiveness and degree of infiltration of pulmonary ground-glass nodules based on computed tomography features and enhanced quantitative analysis.Quantitative imaging in medicine and surgery · 2024Article
- Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning.The clinical respiratory journal · 2024Article
- Construction and validation of a predictive model of invasive adenocarcinoma in pure ground-glass nodules less than 2 cm in diameter.BMC surgery · 2024Article
- Computed-tomography-based radiomic nomogram for predicting the risk of indeterminate small (5-20 mm) solid pulmonary nodulesDiagnostic and interventional radiology (Ankara, Turkey) · 2023Article
- Correlations Between Inflammatory Cell Infiltration and Relative Density and the Boundary Manifestation of Pulmonary Non-Neoplastic Ground Glass Nodules.Journal of inflammation research · 2023Article
- Potential Role of Intrapulmonary Concomitant Lesions in Differentiating Non-Neoplastic and Neoplastic Ground Glass Nodules.Journal of inflammation research · 2023Article
- A No-Math Primer on the Principles of Machine Learning for Radiologists.Seminars in ultrasound, CT, and MR · 2022Article
- A Multi-Classification Model for Predicting the Invasiveness of Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules.Frontiers in oncology · 2022Article
- A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures.Frontiers in medicine · 2022Article
- A predictive model based on ground glass nodule featuresFrontiers in surgery · 2022Article
- Deep Learning-Based Stage-Wise Risk Stratification for Early Lung Adenocarcinoma in CT Images: A Multi-Center Study.Cancers · 2021Article
- Post-stroke Anxiety AnalysisFrontiers in aging neuroscience · 2021Article
- Detailed Analysis and Radiomic Prediction of First Progression Sites of First-Line Targeted Therapy for EGFR-Mutant Lung Adenocarcinoma Patients With Systemic Metastasis.Frontiers in oncology · 2021Article
- Two nomograms based on CT features to predict tumor invasiveness of pulmonary adenocarcinoma and growth in pure GGN: a retrospective analysis.Japanese journal of radiology · 2020Article
- Risk Factors of Cerebral Infarction and Myocardial Infarction after Carotid Endarterectomy Analyzed by Machine Learning.Computational and mathematical methods in medicine · 2020Article
- CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features.Frontiers in oncology · 2020Article
- [Application of machine learning models in predicting early stone-free rate after flexible ureteroscopic lithotripsy for renal stones].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2019Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe purpose of this study was to develop a predictive model that could accurately predict the malignancy of the pulmonary ground-glass nodules (GGNs) and the invasiveness of the malignant GGNs.
methodsThe authors built two binary classification models that could predict the malignancy of the pulmonary GGNs and the invasiveness of the malignant GGNs.
resultsResults of our developed model showed random forest could achieve 95.1% accuracy to predict the malignancy of GGNs and 83.0% accuracy to predict the invasiveness of the malignant GGNs.
conclusionsThe malignancy and invasiveness of pulmonary GGNs could be predicted by random forest.
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