Journal
JOURNAL OF THORACIC DISEASE
Volume 10, Issue 1, Pages 458-463Publisher
AME PUBLISHING COMPANY
DOI: 10.21037/jtd.2018.01.88
Keywords
Ground-glass nodule (GGN); random forest
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Funding
- Shanghai Science and Technology Commission Foundation project [14411950800]
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Background: The 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. Methods: The authors built two binary classification models that could predict the malignancy of the pulmonary GGNs and the invasiveness of the malignant GGNs Results: Results 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. Conclusions: The malignancy and invasiveness of pulmonary GGNs could be predicted by random forest.
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