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
Department of Applied Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China.
J Thorac Dis. 2018 Jan;10(1):458-463. doi: 10.21037/jtd.2018.01.88.
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.
The authors built two binary classification models that could predict the malignancy of the pulmonary GGNs and the invasiveness of the malignant GGNs.
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.
The malignancy and invasiveness of pulmonary GGNs could be predicted by random forest.
本研究的目的是开发一种预测模型，该模型能够准确预测肺磨玻璃结节（GGN）的恶性程度以及恶性GGN的侵袭性。
作者构建了两个二元分类模型，用于预测肺GGN的恶性程度以及恶性GGN的侵袭性。
我们开发的模型结果显示，随机森林在预测GGN恶性程度方面的准确率可达95.1%，在预测恶性GGN侵袭性方面的准确率为83.0%。
随机森林可用于预测肺GGN的恶性程度和侵袭性。
