^(18)F-FDG PET/CT联合CT三维重建鉴别肺结节良恶性的回顾性队列研究

^(18)F-FDG PET/CT combined with CT three-dimensional reconstruction in the differentiation of benign and malignant pulmonary nodules:A retrospective cohort study

目的探讨^(18)F-FDG正电子发射计算机体层显像(positron emission tomography/computed tomography,PET/CT)联合CT三维重建(CT three-dimensional reconstruction,CT-3D)对肺结节良恶性鉴别诊断的准确性。方法回顾性分析2020年7月—2021年8月于苏北人民医院胸外科行手术治疗的肺结节患者。提取患者术前^(18)F-FDG PET/CT和胸部增强CT-3D等影像学资料,通过受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under the curve,AUC)筛选具有诊断意义的参数,通过二元logistic回归分别建立PET/CT预测模型(PET/CT prediction model,MOD PET)、CT-3D预测模型(CT-3D prediction model,MOD CT-3D)和PET/CT联合CT-3D的预测模型(MOD combination),再通过ROC曲线验证模型的诊断效能。结果共纳入125例患者,其中男57例、女68例,平均年龄(61.16±8.57)岁。良性病变46例,恶性肿瘤79例。共提取2个PET/CT参数和5个CT-3D参数,其中PET/CT参数中SUVmax≥1.5(AUC=0.688)和肺门/纵隔淋巴结代谢摄取异常(AUC=0.671)被纳入回归模型,CT-3D参数中CT直方图峰(AUC=0.694)和三维形态(AUC=0.652)被纳入回归模型。最后验证MOD PET的AUC为0.738[95%CI(0.651,0.824)],敏感性为74.7%,特异性为60.9%;MOD CT-3D的AUC为0.762[95%CI(0.677,0.848)],敏感性为51.9%,特异性为87.0%;MOD combination的AUC为0.857[95%CI(0.789,0.925)],敏感性77.2%,特异性82.6%,差异有统计学意义(P<0.001)。结论^(18)F-FDG PET/CT联合CT-3D可提高肺结节的诊断效能,其特异性和敏感性均优于单种影像学诊断方法;联合预测模型对肺结节手术时机和手术方式的选择具有重要意义,为肺结节诊断人工智能化提供理论依据。

Objective To investigate the accuracy of ^(18)F-FDG positron emission tomography/computed tomography(PET/CT)combined with CT three-dimensional reconstruction(CT-3D)in the differential diagnosis of benign and malignant pulmonary nodules.Methods The clinical data of patients who underwent pulmonary nodule surgery in the Department of Thoracic Surgery,Northern Jiangsu People's Hospital from July 2020 to August 2021 were retrospectively analyzed.The preoperative ^(18)F-FDG PET/CT and chest enhanced CT-3D and other imaging data were extracted.The parameters with diagnostic significance were screened by the area under the receiver operating characteristic(ROC)curve(AUC).Three prediction models,including PET/CT prediction model(MOD PET),CT-3D prediction model(MOD CT-3D),and PET/CT combined CT-3D prediction model(MOD combination),were established through binary logistic regression,and the diagnostic performance of the models were validated by ROC curve.Results A total of 125 patients were enrolled,including 57 males and 68 females,with an average age of 61.16±8.57 years.There were 46 patients with benign nodules,and 79 patients with malignant nodules.A total of 2 PET/CT parameters and 5 CT-3D parameters were extracted.Two PET/CT parameters,SUVmax≥1.5(AUC=0.688)and abnormal uptake of hilar/mediastinal lymph node metabolism(AUC=0.671),were included in the regression model.Among the CT-3D parameters,CT value histogram peaks(AUC=0.694)and CT-3D morphology(AUC=0.652)were included in the regression model.Finally,the AUC of the MOD PET was verified to be 0.738[95%CI(0.651,0.824)],the sensitivity was 74.7%,and the specificity was 60.9%;the AUC of the MOD CT-3D was 0.762[95%CI(0.677,0.848)],the sensitivity was 51.9%,and the specificity was 87.0%;the AUC of the MOD combination was 0.857[95%CI(0.789,0.925)],the sensitivity was 77.2%,the specificity was 82.6%,and the differences were statistically significant(P<0.001).Conclusion ^(18)F-FDG PET/CT combined with CT-3D can improve the diagnostic performance of pulmonary nodules,and its specificity and sensitivity are better than those of single imaging diagnosis method.The combined prediction model is of great significance for the selection of surgical timing and surgical methods for pulmonary nodules,and provides a theoretical basis for the application of artificial intelligence in the pulmonary nodule diagnosis.