Niu Rong, Gao Jianxiong, Shao Xiaoliang, Wang Jianfeng, Jiang Zhenxing, Shi Yunmei, Zhang Feifei, Wang Yuetao, Shao Xiaonan
Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Changzhou Key Laboratory of Molecular Imaging, Changzhou, China.
Front Oncol. 2021 Dec 17;11:727094. doi: 10.3389/fonc.2021.727094. eCollection 2021.
To investigate whether the maximum standardized uptake value (SUVmax) of F-deoxyglucose (FDG) PET imaging can increase the diagnostic efficiency of CT radiomics-based prediction model in differentiating benign and malignant pulmonary ground-glass nodules (GGNs). We retrospectively collected 190 GGNs from 165 patients who underwent F-FDG PET/CT examination from January 2012 to March 2020. Propensity score matching (PSM) was performed to select GGNs with similar baseline characteristics. LIFEx software was used to extract 49 CT radiomic features, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to select parameters and establish the Rad-score. Logistic regression analysis was performed combined with semantic features to construct a CT radiomics model, which was combined with SUVmax to establish the PET + CT radiomics model. Receiver operating characteristic (ROC) was used to compare the diagnostic efficacy of different models. After PSM at 1:4, 190 GGNs were divided into benign group (n = 23) and adenocarcinoma group (n = 92). After texture analysis, the Rad-score with three CT texture features was constructed for each nodule. Compared with the Rad-score and CT radiomics model (AUC: 0.704 (95%CI: 0.562-0.845) and 0.908 (95%CI: 0.842-0.975), respectively), PET + CT radiomics model had the best diagnostic efficiency (AUC: 0.940, 95%CI: 0.889-0.990), and there was significant difference between each two of them ( = 0.001-0.030). SUVmax can effectively improve CT radiomics model performance in the differential diagnosis of benign and malignant GGNs. PET + CT radiomics might become a noninvasive and reliable method for differentiating of GGNs.
为研究¹⁸F-脱氧葡萄糖（FDG）PET成像的最大标准化摄取值（SUVmax）是否能提高基于CT影像组学的预测模型在鉴别肺磨玻璃结节（GGN）良恶性方面的诊断效率。我们回顾性收集了2012年1月至2020年3月期间接受¹⁸F-FDG PET/CT检查的165例患者的190个GGN。采用倾向评分匹配（PSM）来选择具有相似基线特征的GGN。使用LIFEx软件提取49个CT影像组学特征，并采用最小绝对收缩和选择算子（LASSO）算法选择参数并建立Rad评分。结合语义特征进行逻辑回归分析以构建CT影像组学模型，并将其与SUVmax相结合建立PET+CT影像组学模型。采用受试者操作特征（ROC）曲线比较不同模型的诊断效能。按1∶4进行PSM后，将190个GGN分为良性组（n=23）和腺癌组（n=92）。经过纹理分析，为每个结节构建了具有三个CT纹理特征的Rad评分。与Rad评分和CT影像组学模型相比（AUC分别为0.704（95%CI：0.562-0.845）和0.908（95%CI：0.842-0.975）），PET+CT影像组学模型具有最佳的诊断效率（AUC：0.940，95%CI：0.889-0.990），且两两之间存在显著差异（P=0.001-0.030）。SUVmax能有效提高CT影像组学模型在鉴别GGN良恶性方面的性能。PET+CT影像组学可能成为一种鉴别GGN的无创且可靠的方法。
