Wang Yue, Chen Hebing, Chen Yuyang, Zhong Zhenguang, Huang Haoyu, Sun Peng, Zhang Xiaohui, Wan Yiliang, Li Lingli, Ye Tianhe, Pan Feng, Yang Lian
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
J Thorac Dis. 2023 May 30;15(5):2505-2516. doi: 10.21037/jtd-22-1605. Epub 2023 Apr 7.
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 0.932, P=0.088; 0.881 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.
近年来，光谱计算机断层扫描（CT）在磨玻璃结节（GGN）侵袭性诊断中表现出优异性能；然而，尚无研究将光谱多模态数据与放射组学分析相结合进行综合分析与探索。因此，本研究在先前研究基础上更进一步：探讨基于双层光谱CT的多模态放射组学在评估表现为GGN的肺腺癌侵袭性方面的价值。
本研究将125个经病理证实的浸润前腺癌（PIA）和肺腺癌的GGN分为训练集（n = 87）和测试集（n = 38）。每个病灶通过预训练神经网络自动检测和分割，并提取63个多模态放射组学特征。使用最小绝对收缩和选择算子（LASSO）选择目标特征，并在训练集中构建放射学评分（rad-score）。进行逻辑回归分析以建立结合年龄、性别和rad-score的联合模型。通过受试者操作特征（ROC）曲线和精确召回率曲线比较两个模型的诊断性能。通过ROC分析比较两个模型之间的差异。使用测试集评估预测性能并校准模型。
选择了5个放射组学特征。在训练集和测试集中，放射组学模型的曲线下面积（AUC）分别为0.896（95%CI：0.830 - 0.962）和0.881（95%CI：0.777 - 0.985），联合模型的AUC分别为0.932（95%CI：0.882 - 0.982）和0.887（95%CI：0.786 - 0.988）。在训练集和测试集中，放射组学模型与联合模型的AUC无显著差异（0.896对0.932，P = 0.088；0.881对0.887，P = 0.480）。
基于双层光谱CT的多模态放射组学在鉴别GGN的侵袭性方面显示出良好的预测性能，可辅助临床治疗策略的决策。
