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Classification decision tree algorithm assisting in diagnosing solitary pulmonary nodule by SPECT CT fusion imaging


Objective To develop a classification tree algorithm to improve diagnostic performances of 99mTc-MIBI SPECT/CT fusion imaging in differentiating solitary pulmonary nodules (SPNs). Methods Forty-four SPNs, including 30 malignant cases and 14 benign ones that were eventually pathologically identified, were included in this prospective study. All patients received 99Tcm-MIBI SPECT/CT scanning at an early stage and a delayed stage before operation. Thirty predictor variables, including 11 clinical variables, 4 variables of emission and 15 variables of transmission information from SPECT/CT scanning, were analyzed independently by the classification tree algorithm and radiological residents. Diagnostic rules were demonstrated in tree-topology, and diagnostic performances were compared with Area under Curve (AUC) of Receiver Operating Characteristic Curve (ROC). Results A classification decision tree with lowest relative cost of 0.340 was developed for 99Tcm-MIBI SPECT/CT scanning in which the value of Target/Normal region of 99Tcm-MIBI uptake in the delayed stage and in the early stage, age, cough and specula sign were five most important contributors. The sensitivity and specificity were 93.33% and 78. 57e, respectively, a little higher than those of the expert. The sensitivity and specificity by residents of Grade one were 76.67% and 28.57%, respectively, and AUC of CART and expert was 0.886±0.055 and 0.829±0.062, respectively, and the corresponding AUC of residents was 0.566±0.092. Comparisons of AUCs suggest that performance of CART was similar to that of expert (P=0.204), but greater than that of residents (P<0.001). Conclusion Our data mining technique using classification decision tree has a much higher accuracy than residents. It suggests that the application of this algorithm will significantly improve the diagnostic performance of residents.......

【作者名称】: Qiang Yongqian, Guo Youmin, Jin Chenwang , Liu Min, Yang Aimin, Wang Qiuping
【作者单位】: Imaging Center, the First Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an 710061, Imaging Center, the Second Affiliated Hospital, Medical School of Xi'an Jiaotong University,Xi'an 710004, China, Imaging Center, the First Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an 710061, Imaging Center, the First Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an 710061, Imaging Center, the First Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an 710061, Imaging Center, the First Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an 710061
【关 键 词】: decision tree, solitary pulmonary nodule, Technetium-99m, fusion imaging
【期刊名称】: 西安医科大学学报(英文版)
【期刊论文数据库】: [DBS_Articles_01]
【期刊论文编号】: 105,448,794
【摘要长度】: 1,878
【上篇论文】: 中文期刊 - Photoluminescence and Electroluminescence Studies on Tb-Doped Silicon Rich Oxide Materials and Devices
【下篇论文】: 中文期刊 - GIS-based risk analysis of debris flow: an application in Sichuan, southwest China

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