Bayes分析指导孤立性肺结节的临床决策

目的：探讨Bayes分析指导孤立性肺结节（solitary pulmonary nodules, SPN）临床决策的可行性及其临床意义。方法：利用Bayes分析法先从352例SPN训练集（恶性135例,良性217例）中求出恶性SPN的验前比及各临床和CT表现的似然比,再以此计算每个SPN的恶性概率。比较Bayes分析与医生常规判断132例SPN测试集（恶性61例,良性71例）样本的诊断效能,并分析在不同计算概率下的实际诊断结果。结果：Bayes分析诊断测试集SPN的敏感度、特异度、符合率、阳性预测值及阴性预测值分别为88.5％,85.9％,87.1％,84.4％,89.7％,其诊断符合率与高年资甲、乙医生比较无统计学差异（均P＞0.05）,但高于低年资丙、丁医生（均P＜0.05）;Bayes分析,高年资甲、乙医生及低年资丙、丁医生的Brier值分别为0.099,0.140,0.137,0.154,0.179;除外被错判的11例孤立性肺转移瘤,Bayes分析法估算概率＜20％的假阴性率为1.0％（5/484）。结论：运用Bayes分析诊断SPN性质的符合率高,预测恶性概率的精度高,且假阴性率低,用于指导SPN的临床决策具有一定的可行性。

Bayes analysis in clinical decision-making for solitary pulmonary nodules

ObjectiveTo explore the feasibility and the value of Bayes analysis in clinical decision-making for solitary pulmonary nodules (SPNs).MethodsWe collected 352 consecutive SPN patients (malignancy, n=135; benignity, n=217) retrospectively to form the training set. Utilizing Bayes analysis, the prior odds of malignant SPNs and the likelihood ratios of clinical and CT findings were derived from the training set, which were then used to calculate the probability of malignancy in each SPN. Bayes analysis was also tested prospectively for its diagnostic validation and precision of predictive probability on the test set of 132 SPN patients (malignancy, n=61; benignity, n=71), and compared with the performance of physicians using routine judgment. The actual results of patients diagnosis were analyzed according to the scale of calculated malignant probability in SPNs. ResultsThe sensitivity, specificity, and accuracy of Bayes analysis for the training samples were 88.9%, 93.1%, and 91.5%, respectively. In the test set, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of Bayes analysis were 88.5%, 85.9%, 87.1%, 84.4%, and 89.7%, respectively. The accuracy of Bayes analysis had no statistically significant difference with that of senior physician A (80.3%, χ2=2.37, P=0.122) and B (79.5%, χ2=3.12, P=0.076), and was higher than that of junior physician C (74.2%, χ2=7.05, P=0.012) and D (74.2%, χ2=6.56, P=0.009); The Brier score was 0.099, 0.140, 0.137,0.154, and 0.179 for Bayes analysis,senior physician A, senior physician B, junior physician C, and junior physician D, respectively. Excluding the solitary metastasis (n=11) misclassified, the false negative rate of Bayes analysis was 1.0% (5/484) for SPNs with <20% estimated probability of malignancy.ConclusionBayes analysis is accurate in qualitative diagnosis, precise in forecasting the malignant probability, and has low false negative rate for SPNs. It is feasible to use Bayes analysis for the management of SPNs.