DECT 鉴别混合性磨玻璃结节型肺肿瘤亚型 的价值.

Objective To explore the value of dual energy computed tomography (DECT) iodine uptake in differentiating the types of mixed ground glass nodules (mGGN) in pulmonary adenocarcinoma. Methods DECT images of 134 mGGN of pathologically confirmed pulmonary adenocarcinoma from February 2021 to May 2022 were analyzed retrospectively. There were 75 invasive adenocarcinomas (IAC) and 59 adenocarcinomas in situ or minimally invasive adenocarcinomas (AIS/MIA). The maximum diameter of the solid component, maximum diameter of the nodule, and DECT quantitative indicator differences of mGGN between the IAC and AIS/MIA groups were compared by univariate analysis. Binary logistic regression analysis was used to establish a predictive model. The diagnostic performance of different parameters was compared by receiver operating characteristic (ROC) curves and Z test. Results The maximum diameter of solid component, maximum diameter of nodule, consolidation-to-tumor ratio, and tumor enhancement value of mGGN in IAC group were significantly higher than those in AIS/MIAgroup (P<0.05). The iodine density in the solid part of the nodule and the standardized iodine density in the solid part of the nodule in IAC group were significantly lower than those in ASI/MIA group (P<0.05). The maximum diameter of solid component, maximum diameter of nodule, and the enhancement value of tumor were independent predictors (OR=21.019, 35.878, 1.132; 95%CI: 1.957-225.786, 5.116-251.596, 1.062-1.206; P<0.05). The area under ROC curve (AUC) of the combined prediction model was 0.913 (95%CI: 0.852-0.955) with 81.3% accuracy, 77.3% specificity, and 94.9% sensitivity. The AUC of the combined prediction model was significantly higher than the AUCs of the maximum diameter of solid component, maximum diameter of nodule, and tumor enhancement value (P<0.05). Conclusion The maximum diameter of solid component and maximum diameter of mGGN combined with DECT quantitative parameters are effective for preoperative differentiation of pathological subtypes of mGGN lung adenocarcinoma with high accuracy of prediction. [ABSTRACT FROM AUTHOR]