医学
无线电技术
放射科
核医学
接收机工作特性
Lasso(编程语言)
组内相关
逻辑回归
腺癌
射线照相术
癌症
内科学
计算机科学
临床心理学
万维网
心理测量学
作者
Yuting Zheng,Xiaoyu Han,Xi Jia,Chengyu Ding,Kailu Zhang,Hanting Li,Cao Xue-Xiang,Xiao‐Hui Zhang,Xin Zhang,Heshui Shi
标识
DOI:10.3389/fonc.2023.1208758
摘要
Objectives To explore the value of radiomics based on Dual-energy CT (DECT) for discriminating preinvasive or MIA from IA appearing as GGNs before surgery. Methods The retrospective study included 92 patients with lung adenocarcinoma comprising 30 IA and 62 preinvasive-MIA, which were further divided into a training (n=64) and a test set (n=28). Clinical and radiographic features along with quantitative parameters were recorded. Radiomics features were derived from virtual monoenergetic images (VMI), including 50kev and 150kev images. Intraclass correlation coefficients (ICCs), Pearson’s correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were conducted to eliminate unstable and redundant features. The performance of the models was evaluated by area under the curve (AUC) and the clinical utility was assessed using decision curve analysis (DCA). Results The DECT-based radiomics model performed well with an AUC of 0.957 and 0.865 in the training and test set. The clinical-DECT model, comprising sex, age, tumor size, density, smoking, alcohol, effective atomic number, and normalized iodine concentration, had an AUC of 0.929 in the training and 0.719 in the test set. In addition, the radiomics model revealed a higher AUC value and a greater net benefit to patients than the clinical-DECT model. Conclusion DECT-based radiomics features were valuable in predicting the invasiveness of GGNs, yielding a better predictive performance than the clinical-DECT model.
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