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...

ver descrição completa

Na minha lista:
Detalhes bibliográficos
Publicado no:J Thorac Dis
Main Authors: 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
Formato: Artigo
Idioma:Inglês
Publicado em: AME Publishing Company 2018
Assuntos:
Original Article
Acesso em linha: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
Tags: Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!

Registos relacionados