基于Bayes理论的计算机辅助诊断系统在孤立性肺 .doc

基于Bayes理论的计算机辅助诊断系统在孤立性肺结节CT诊断中的应用 陈伟 刘进康 李文政 熊曾 龙学颖 周晖 （中南大学湘雅医院放射科，湖南长沙410008） 摘要： 目的 初步探讨基于Bayes理论的计算机辅助诊断（computer-aided diagnosis, CAD）系统在孤立性肺结节（solitary pulmonary nodule, SPN）Application of Bayesian theory based computer-aided diagnosis system in the CT diagnosis of solitary pulmonary nodules CHEN Wei, LIU Jin-kang, LI Wen-zheng, XIONG Zeng, LONG Xue-ying, ZHOU Hui. (Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China) Abstract Objective To preliminarily explore the value of computer-aided diagnosis (CAD)system based on Bayesian theory in the diagnosis of solitary pulmonary nodules (SPNs) with CT. Methods Three hundred and fifty-two consecutive SPN cases (malignancy n=135, benignity n=217) were collected retrospectively to form the training set. According to Bayesian theory, the prior odds of malignant SPNs and the likelihood ratios of clinical and CT findings were derived from the training set firstly, then a Bayesian theory based CAD system was constructed and the probability of malignancy in each SPN was obtained from this system. SPs with ≥50% calculated probability were judged as malignancy and those with 50% calculated probability was judged as benignity. On the test set (malignancy n=61, benignity n=71), the Bayesian theory based CAD system was tested prospectively for its diagnostic validation, compared with the performance of the two chest radiologists and two radiologic residents using routine diagnostic method. Results The Bayesian theory based CAD system was constructed successfully. The sensitivity, specificity, accuracy of this system for the training set were 88.9%, 93.1%, 91.5% respectively. On the test set, the sensitivity, specificity, accuracy, positive predictive value, negative predictive value of this system were 88.5%, 85.9%, 87.1%, 84.4% and 89.7% respectively, its accuracy showed no statistically significant with chest radiologist A (80.3%, χ2=2.37, P=0.122) and B (79.5%,χ2=3.12, P=0.076),