A semiautomated radiomics model based on multimodal dual-layer spectral CT for preoperative discrimination of the invasiveness of pulmonary ground-glass nodules

接收机工作特性 医学 无线电技术 腺癌 试验装置 人工智能 Lasso(编程语言) 放射科 逻辑回归 模式识别(心理学) 计算机科学 癌症 内科学 万维网
作者
Yue Wang,Hebing Chen,Yuyang Chen,Zhenguang Zhong,H. K. Huang,Peng Sun,Xiaohui Zhang,Yiliang Wan,Lingli Li,Tianhe Ye,Feng Pan,Yang Lian
出处
期刊:Journal of Thoracic Disease [AME Publishing Company]
卷期号:15 (5): 2505-2516 被引量:2
标识
DOI:10.21037/jtd-22-1605
摘要

In recent years, spectral computed tomography (CT) has shown excellent performance in the diagnosis of ground-glass nodules (GGNs) invasiveness; however, no research has combined spectral multimodal data and radiomics analysis for comprehensive analysis and exploration. Therefore, this study goes a step further on the basis of the previous research: to investigate the value of dual-layer spectral CT-based multimodal radiomics in accessing the invasiveness of lung adenocarcinoma manifesting as GGNs.In this study, 125 GGNs with pathologically confirmed preinvasive adenocarcinoma (PIA) and lung adenocarcinoma were divided into a training set (n=87) and a test set (n=38). Each lesion was automatically detected and segmented by the pre-trained neural networks, and 63 multimodal radiomic features were extracted. The least absolute shrinkage and selection operator (LASSO) was used to select target features, and a rad-score was constructed in the training set. Logistic regression analysis was conducted to establish a joint model which combined age, gender, and the rad-score. The diagnostic performance of the two models was compared by the receiver operating characteristic (ROC) curve and precision-recall curve. The difference between the two models was compared by the ROC analysis. The test set was used to evaluate the predictive performance and calibrate the model.Five radiomic features were selected. In the training and test sets, the area under the curve (AUC) of the radiomics model was 0.896 (95% CI: 0.830-0.962) and 0.881 (95% CI: 0.777-0.985) respectively, and the AUC of the joint model was 0.932 (95% CI: 0.882-0.982) and 0.887 (95% CI: 0.786-0.988) respectively. There was no significant difference in AUC between the radiomics model and joint model in the training and test sets (0.896 vs. 0.932, P=0.088; 0.881 vs. 0.887, P=0.480).Multimodal radiomics based on dual-layer spectral CT showed good predictive performance in differentiating the invasiveness of GGNs, which could assist in the decision of clinical treatment strategies.
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