Predicting malignancy of pulmonary ground-glass nodules and their invasiveness by random forest

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...

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Dettagli Bibliografici
Pubblicato in:J Thorac Dis
Autori principali: Mei, Xueyan, Wang, Rui, Yang, Wenjia, Qian, Fangfei, Ye, Xiaodan, Zhu, Li, Chen, Qunhui, Han, Baohui, Deyer, Timothy, Zeng, Jingyi, Dong, Xiaomeng, Gao, Wen, Fang, Wentao
Natura: Artigo
Lingua:Inglês
Pubblicazione: AME Publishing Company 2018
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Original Article
Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC5863133/
https://ncbi.nlm.nih.gov/pubmed/29600078
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.21037/jtd.2018.01.88
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