Classification decision tree in CT imaging:application to the differential diagnosis of solitary pulmonary nodules
- VernacularTitle:分类决策树辅助CT诊断孤立性肺结节的方法学研究
- Author:
Hongxia MA
;
Yulin GUO
;
Qiuping WANG
;
Min LIU
;
Yongqian QIANG
;
Xiaojuan GUO
;
Youmin GUO
;
Qihang CHEN
- Publication Type:Journal Article
- Keywords:
Coin lesion,pulmonary;
Diagnosis,computer-assisted;
Regression analysis
- From:
Chinese Journal of Radiology
2008;42(1):50-55
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish classification and regression tree (CART) for differentiating benign from malignant solitary pulmonary nudules (SPN).Methods One hundred and sixteen consecutive cases with 116 solitary pulmonary nodules,which finally were pathologically proven 54 malignant nodules and 62 benign nodules,were prospectively registered in this research.Twelve clinical presentations and 22 CT findings were collected as predictors.A classification tree was established to distinguish benign SPNs from malignant ones.In the observer test,two groups (one made of junior radiologists and one of senior radiologists) were independently presented with clinical information and CT images without knowing the pathologic and machine-learning results.Performance of observers and CART were compared by receiver operating characteristic analysis.Results Receiver operating characteristic analysis showed areas under the curve of CART,senior radiologists and junior radiologists respectively were 0.910±0.029,0.827±0.038,0.612±0.052. Difference between areas (DBF) between CART and junior radiologists was 0.297 (P＜0.01).DBF between CART and senior radiologists was 0.083(P＜0.05).DBF between senior and junior radiologists was 0.214(P＜0.01).CART showed a best diagnostic efficiency,followed by junior radiologists,and then senior radiologists.Conclusion Our data mining techniques using CART prove a high accuracy in differentiating benign from malignant pulmonary nodules based on clinical variables and CT findings.It will be a potentially useful tool in further application of artificial intelligence in the imaging diagnosis.
