基于 CT 纹理分析的良性和恶性亚厘米级肺磨玻璃结节（≤1cm）的无创评估。
Non-invasive evaluation for benign and malignant subcentimeter pulmonary ground-glass nodules (≤1 cm) based on CT texture analysis.
机构信息
Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.
Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.
出版信息
Br J Radiol. 2020 Oct 1;93(1114):20190762. doi: 10.1259/bjr.20190762. Epub 2020 Jul 20.
OBJECTIVES
To investigate potential diagnostic model for predicting benign or malignant status of subcentimeter pulmonary ground-glass nodules (SPGGNs) (≤1 cm) based on CT texture analysis.
METHODS
A total of 89 SPGGNs from 89 patients were included; 51 patients were diagnosed with adenocarcinoma, and 38 were diagnosed with inflamed or infected benign SPGGNs. Analysis Kit software was used to manually delineate the volume of interest of lesions and extract a total of 396 quantitative texture parameters. The statistical analysis was performed using R software. The SPGGNs were randomly divided into a training set ( = 59) and a validation set ( = 30). All pre-normalized (Z-score) feature values were subjected to dimension reduction using the LASSO algorithm,and the most useful features in the training set were selected. The selected imaging features were then combined into a Rad-score, which was further assessed by ROC curve analysis in the training and validation sets.
RESULTS
Four characteristic parameters (ClusterShade_AllDirection_offset4_SD, ShortRunEmphasis_angle45_offset1, Maximum3DDiameter, SurfaceVolumeRatio) were further selected by LASSO ( < 0.05). As a cluster of imaging biomarkers, the above four parameters were used to form the Rad-score. The AUC for differentiating between benign and malignant SPGGNs in the training set was 0.792 (95% CI: 0.671, 0.913), and the sensitivity and specificity were 86.10 and 65.20%, respectively. The AUC in the validation set was 72.9% (95% CI: 0.545, 0.913), and the sensitivity and specificity were 86.70 and 60%, respectively.
CONCLUSION
The present diagnostic model based on the cluster of imaging biomarkers can preferably distinguish benign and malignant SPGGNs (≤1 cm).
ADVANCES IN KNOWLEDGE
Texture analysis based on CT images provide a new and credible technique for accurate identification of subcentimeter pulmonary ground-glass nodules.
目的
基于 CT 纹理分析，探讨用于预测亚厘米肺磨玻璃结节（SPGGN，≤1cm）良恶性的潜在诊断模型。
方法
纳入 89 例 89 个 SPGGN 患者，其中 51 例诊断为腺癌，38 例诊断为炎性或感染性良性 SPGGN。采用分析软件手动勾画病变感兴趣区，提取 396 个定量纹理参数。使用 R 软件进行统计分析。将 SPGGN 随机分为训练集（=59）和验证集（=30）。所有归一化（Z 评分）前的特征值均通过 LASSO 算法进行降维，并选择训练集中最有用的特征。选择的影像学特征进一步组合成 Rad-score，然后在训练集和验证集中通过 ROC 曲线分析进行评估。
结果
LASSO（<0.05）进一步筛选出 4 个特征参数（ClusterShade_AllDirection_offset4_SD、ShortRunEmphasis_angle45_offset1、Maximum3DDiameter、SurfaceVolumeRatio）。上述 4 个参数构成了一组影像学生物标志物，用来形成 Rad-score。在训练集中，用于区分良恶性 SPGGN 的 AUC 为 0.792（95%CI：0.671，0.913），敏感度和特异度分别为 86.10%和 65.20%。在验证集中，AUC 为 0.729（95%CI：0.545，0.913），敏感度和特异度分别为 86.70%和 60%。
结论
基于影像生物标志物组的诊断模型能较好地区分良恶性亚厘米肺磨玻璃结节（≤1cm）。
知识进展
CT 图像纹理分析为准确识别亚厘米肺磨玻璃结节提供了一种新的可靠技术。
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