Objective: To describe the endobronchial ultrasonographic characteristics and the cut-off value for diagnosis of peripheral lung cancer, and therefore to evaluate its diagnostic value.
Methods: During June 1st, 2005 and June 30th, 2006, 78 patients with peripheral pulmonary lesions were enrolled. The lesions were all detectable by endobronchial ultrasonography (EBUS) and a final diagnosis was made. The endobronchial ultrasonographic structure of peripheral pulmonary lesions were analyzed, differentiated and classified into malignant or benign groups.
Results: According to the result of binary multivariable logistic regression analysis on the 9 variables and by calculating the area under ROC curve, 5 variables were found to be useful in predicting the presence of malignancy: (1) clear borderline; (2) internal hypoechoic echo; (3) heterogeneous pattern; (4) without internal hyperechoic dots and linear arcs; (5) adjacent blood vessels representing shift, narrow or break-off. The equation of malignancy probability for any patient was: P = 1/[1 + e(-) (6.321-3.097X(2)-1.537X(1) + 1.898X(5) + 2.390X(3) + 3.003X(4))], X(1) for borderline; X(2) for internal hyperechoic dots and linear arcs; X(3) for adjacent blood vessels; X(4) for internal echo intensity; X(5) for internal echo distribution. The areas of ROC curve illustrated that multivariable logistic regression model discriminated benign and malignant lesions better than univariable logistic regression. The optimal cut-off value of the malignancy probability, which was greater or equal to 0.52 according to the ROC curve. This model gave a sensitivity and specificity of 87.2% and 80.6%, and the accuracy was 85.9%.
Conclusions: Endobronchial ultrasonographic characteristics of peripheral lung cancer included clear borderline, internal hypoechoic echo, heterogeneous pattern, without hyperechoic dots and linear arcs, and adjacent blood vessel shift, narrow or break-off. Multivariable logistic regression model discriminated benign and malignant lesions better than univariable logistic regression. Combination of multiple variables increases the sensitivity, specificity and accuracy for prediction of malignancy.