基于计算机断层扫描的放射组学模型评估表现为磨玻璃密度结节的肺腺癌侵袭性。

A computerized tomography-based radiomic model for assessing the invasiveness of lung adenocarcinoma manifesting as ground-glass opacity nodules.

机构信息

Chinese People's Liberation Army Medical School, Beijing, 100853, China.

Department of Respiratory Medicine, First Medical Center of Chinese People's Liberation Army General Hospital, Beijing, 100853, China.

出版信息

Respir Res. 2022 Apr 16;23(1):96. doi: 10.1186/s12931-022-02016-7.

BACKGROUND

Clinically differentiating preinvasive lesions (atypical adenomatous hyperplasia, AAH and adenocarcinoma in situ, AIS) from invasive lesions (minimally invasive adenocarcinomas, MIA and invasive adenocarcinoma, IA) manifesting as ground-glass opacity nodules (GGOs) is difficult due to overlap of morphological features. Hence, the current study was performed to explore the diagnostic efficiency of radiomics in assessing the invasiveness of lung adenocarcinoma manifesting as GGOs.

METHODS

A total of 1018 GGOs pathologically confirmed as lung adenocarcinoma were enrolled in this retrospective study and were randomly divided into a training set (n = 712) and validation set (n = 306). The nodules were delineated manually and 2446 intra-nodular and peri-nodular radiomic features were extracted. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used for feature selection. Clinical and semantic computerized tomography (CT) feature model, radiomic model and a combined nomogram were constructed and compared. Decision curve analysis (DCA) was used to evaluate the clinical value of the established nomogram.

RESULTS

16 radiomic features were selected and used for model construction. The radiomic model exhibited significantly better performance (AUC = 0.828) comparing to the clinical-semantic model (AUC = 0.746). Further analysis revealed that peri-nodular radiomic features were useful in differentiating between preinvasive and invasive lung adenocarcinomas appearing as GGOs with an AUC of 0.808. A nomogram based on lobulation sign and radiomic features showed the best performance (AUC = 0.835), and was found to have potential clinical value in assessing nodule invasiveness.

CONCLUSIONS

Radiomic model based on both intra-nodular and peri-nodular features showed good performance in differentiating between preinvasive lung adenocarcinoma lesions and invasive ones appearing as GGOs, and a nomogram based on clinical, semantic and radiomic features could provide clinicians with added information in nodule management and preoperative evaluation.

背景

由于形态学特征的重叠，临床上很难将表现为磨玻璃密度结节（GGOs）的癌前病变（非典型腺瘤样增生，AAH 和原位腺癌，AIS）与表现为 GGOs 的浸润性病变（微浸润腺癌，MIA 和浸润性腺癌，IA）区分开来。因此，本研究旨在探讨放射组学在评估表现为 GGOs 的肺腺癌侵袭性方面的诊断效率。

方法

本回顾性研究共纳入 1018 例经病理证实为肺腺癌的 GGO 患者，并将其随机分为训练集（n=712）和验证集（n=306）。手动勾画结节，并提取 2446 个结节内和结节旁放射组学特征。采用单变量分析和最小绝对值收缩和选择算子（LASSO）进行特征选择。构建并比较临床语义计算机断层扫描（CT）特征模型、放射组学模型和联合列线图。采用决策曲线分析（DCA）评估建立的列线图的临床价值。

结果

选择了 16 个放射组学特征用于模型构建。与临床语义模型（AUC=0.746）相比，放射组学模型的表现明显更好（AUC=0.828）。进一步分析显示，在区分表现为 GGO 的浸润前和浸润性肺腺癌方面，结节旁放射组学特征有用，AUC 为 0.808。基于分叶征和放射组学特征的列线图表现最佳（AUC=0.835），并在评估结节侵袭性方面具有潜在的临床价值。