Xueyan Mei1, Rui Wang2, Wenjia Yang3, Fangfei Qian3, Xiaodan Ye4, Li Zhu4, Qunhui Chen4, Baohui Han3, Timothy Deyer5,6, Jingyi Zeng7, Xiaomeng Dong8, Wen Gao2, Wentao Fang2. 1. Department of Applied Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA. 2. Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China. 3. Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China. 4. Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China. 5. East River Medical Imaging, New York, NY, USA. 6. Department of Radiology, Weill Cornell Medicine, New York, NY, USA. 7. Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, USA. 8. Department of Data Science Analytics, University of Oklahoma, Norman, OK, USA.
Abstract
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.
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.
Entities:
Keywords:
Ground-glass nodule (GGN); random forest
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