| Objective To analyze the risk factors of malignant small pulmonary nodules and establish the mathematical prediction model of malignant lesions, so as to provide theoretical basis for effective detection of malignant lesions.Methods The clinical data of 100 patients with small pulmonary nodules,underwent thoracic operation in our hospital from Jan 2017 to Jun 2017, were retrospectively analyzed. Univariate analysis of such indexes as gender, age, tumor markers, and chest CT image features of all patients were performed. Multivariate logistic regression analysis was used to screen the risk factors of pulmonary malignant nodules, and with it the corresponding mathematical prediction model was established.Results Multivariatelogistic regression analysis showed that lesion location (OR=4.218;P=0.042), ground-glass nodule (GGN)of lesion type(OR=24.625;P=0.000), partial solid nodule (PSN) of lesion type (OR=6.228;P=0.052), vascular breakthrough sign (OR=10.646;P=0.036), lobulation sign (OR=18.162;P=0.027) and spiculation sign (OR=8.054;P=0.018) were independent risk factors formalignant pulmonary nodules. And their malignancy predicted value (P) was ez/(1+ez), whereas×Z×equaled to-2.761+(3.204×GGN)+(1.829×PSN)+(1.439×position)+(2.086×spiculation sign)+(2.899×lobulation sign)+(2.365×vascular breakthrough sign). Based on the ROC curve, predictive probability of 0.64 was chosen as the critical value of benign neoplasm and malignancy judgment, with accuracy rate of model prediction 87%, sensitivity of 97.4%, specificity of 54.2%, and positive predicted value of 87.1% and negative predicted value of 86.7%.Conclusion If those small pulmonary lesions locate in the upper lobe and the CT images are characterized by GGN, PSN, and nodules with vascular breakthrough sign, spiculation and lobulation signs,the probability of malignancy would be higher. The mathematical prediction model established by logistic regression may have higher accuracy in the prediction of malignant probability of small pulmonary nodules. |
