基于CT和 18 F-FDG PET/CT的肺癌风险预测模型对肺结节恶性风险的验证研究

Abstract

Objective To compare the efficacy of the CT and 18F-FDG PET/CT models in predicting the malignant risk of pulmonary nodules and to verify the predictive accuracy of model. Methods A retrospective analysis of 120 patients with pulmonary nodules confirmed by pathological diagnosis or follow-up were conducted in this study. Among these patients, 59 patients with suspected malignancy received 18F-FDG PET/CT. The corresponding receiver operating characteristic curve for each model was plotted, and the area under the curve(AUC) was calculated. The malignant risk of patients with pulmonary nodules(4 –30 mm in diameter) was measured. Model accuracy was verified based on the exclusion criteria for each model and the total cohort of all patients. MedCalc software was used for correlation analysis, and DeLong method was used for two-way comparison. Results All 120 patients with pulmonary nodules were examined. Among them, 49(40.8%) had malignant nodules(31.6% primary lung cancer and 8.2% metastatic disease). The AUC of the Brock and VA models were 0.887 and 0.758, respectively, the difference was statistically significant(Z=6.483, P=0.006). In patients receiving 18F-FDG PET/CT, the AUC of the Herder model was 0.937, which was significantly more accurate than those of the other two models. When testing the model for all patients in the cohort(i.e., patients including the original model’ s inclusion criteria), the AUC value decreased but was not significant. For the Herder model, the AUC was 0.923, and the two types of cohorts were not significant(Z=21.357, P=0.121). For subcentimeter nodules, the AUC values for the Brock and VA models were 0.846 and 0.536, respectively, and the Brock model was significantly better than the VA model(Z=8.768, P=0.0026). Conclusion The Brock model showed good accuracy and was used to predict the likelihood of malignancy in nodules detected by CT scan. The Herder model was the most accurate for patients who underwent 18F-FDG PET/CT for nodule evaluation. Key words: Solitary pulmonary nodules; Positron-emission tomography; Tomography, X-ray computed; Lung cancer risk prediction model