Subei People's Hospital, Yangzhou, Jiangsu, China.
Subei People's Hospital, Yangzhou, Jiangsu, China.
Clin Radiol. 2022 Oct;77(10):e741-e748. doi: 10.1016/j.crad.2022.06.004. Epub 2022 Jul 12.
To investigate and compare the performance of conventional, radiomic, combined, and delta-radiomic features to predict the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules (GGNs).
The present retrospective study included 216 GGNs confirmed surgically as pulmonary adenocarcinomas. All the thin-section computed tomography (CT) images were imported into the software of the United Imaging Intelligence research portal, and radiomic features were extracted with three-dimensional (3D) regions of interest. Least Absolute Shrinkage and Selection Operator was used to select the optimal radiomic features. Four models were constructed, including conventional, radiomic, combined conventional and radiomic, and delta-radiomic models. The receiver operating characteristic curves were built to evaluate the validity of these.
The type, long diameter, shape, margin, vacuole, air bronchus, vascular convergence, and pleural traction exhibited significant differences between pre-invasive lesions (PILs)/minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) groups were selected for conventional model building. Nine radiomic features were selected to build the radiomic model. The four models indicated optimal performance (AUC > 0.7). The radiomic and combined models exhibited the highest diagnostic efficiency, and their AUC were 0.89 and 0.88 in the training set, and 0.87 and 0.88 in the validation set, respectively. The delta-radiomic model indicated that the AUC was 0.83 in the training set, and 0.76 in the validation set. Finally, the conventional model exhibited an AUC in the training and validation sets of 0.78 and 0.76.
The radiomic model and combined model, in particular, and the delta-radiomic model all demonstrated improved diagnostic efficiency in differentiating IA from PIL/MIA than that of the conventional model.
探讨和比较常规、放射组学、联合和 delta 放射组学特征在预测表现为磨玻璃结节（GGN）的肺腺癌侵袭性方面的性能。
本回顾性研究纳入了 216 个经手术证实为肺腺癌的 GGN。所有的薄层 CT 图像均被导入到联影智慧医疗研究平台的软件中，并通过三维（3D）感兴趣区提取放射组学特征。使用最小绝对收缩和选择算子（LASSO）选择最佳的放射组学特征。构建了 4 种模型，包括常规模型、放射组学模型、常规与放射组学联合模型和 delta 放射组学模型。通过构建受试者工作特征曲线来评估这些模型的有效性。
在术前病变（PIL）/微浸润腺癌（MIA）和浸润性腺癌（IA）组之间，病变的类型、长径、形态、边缘、空泡、空气支气管征、血管聚集和胸膜牵拉存在显著差异，这些差异被选择用于常规模型的构建。9 个放射组学特征被选择用于构建放射组学模型。这 4 种模型均表现出最佳的性能（AUC>0.7）。放射组学和联合模型表现出最高的诊断效率，其在训练集和验证集中的 AUC 分别为 0.89 和 0.88，在训练集和验证集中的 AUC 分别为 0.87 和 0.88。Delta 放射组学模型显示在训练集中的 AUC 为 0.83，在验证集中的 AUC 为 0.76。最后，常规模型在训练集和验证集中的 AUC 分别为 0.78 和 0.76。
放射组学模型和联合模型，特别是 delta 放射组学模型，在区分 IA 与 PIL/MIA 方面的诊断效率均优于常规模型。
