Li Wei, Wang Xuexiang, Zhang Yuwei, Li Xubin, Li Qian, Ye Zhaoxiang
Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China.
Department of Radiology, Tianjin Hongqiao Hospital, Tianjin 300130, China.
Chin J Cancer Res. 2018 Aug;30(4):415-424. doi: 10.21147/j.issn.1000-9604.2018.04.04.
To identify the differences among preinvasive lesions, minimally invasive adenocarcinomas (MIAs) and invasive pulmonary adenocarcinomas (IPAs) based on radiomic feature analysis with computed tomography (CT).
A total of 109 patients with ground-glass opacity lesions (GGOs) in the lungs determined by CT examinations were enrolled, all of whom had received a pathologic diagnosis. After the manual delineation and segmentation of the GGOs as regions of interest (ROIs), the patients were subdivided into three groups based on pathologic analyses: the preinvasive lesions (including atypical adenomatous hyperplasia and adenocarcinoma ) subgroup, the MIA subgroup and the IPA subgroup. Next, we obtained the texture features of the GGOs. The data analysis was aimed at finding both the differences between each pair of the groups and predictors to distinguish any two pathologic subtypes using logistic regression. Finally, a receiver operating characteristic (ROC) curve was applied to accurately evaluate the performances of the regression models.
We found that the voxel count feature (P<0.001) could be used as a predictor for distinguishing IPAs from preinvasive lesions. However, the surface area feature (P=0.040) and the extruded surface area feature (P=0.013) could be predictors of IPAs compared with MIAs. In addition, the correlation feature (P=0.046) could distinguish preinvasive lesions from MIAs better.
Preinvasive lesions, MIAs and IPAs can be discriminated based on texture features within CT images, although the three diseases could all appear as GGOs on CT images. The diagnoses of these three diseases are very important for clinical surgery.
基于计算机断层扫描（CT）的影像组学特征分析，鉴别浸润前病变、微浸润腺癌（MIA）和浸润性肺腺癌（IPA）之间的差异。
纳入109例经CT检查确定肺部存在磨玻璃影病变（GGO）的患者，所有患者均已接受病理诊断。在手动勾勒并分割GGO作为感兴趣区域（ROI）后，根据病理分析将患者分为三组：浸润前病变（包括非典型腺瘤样增生和腺癌）亚组、MIA亚组和IPA亚组。接下来，我们获取了GGO的纹理特征。数据分析旨在找出每组之间的差异以及使用逻辑回归区分任意两种病理亚型的预测因子。最后，应用受试者工作特征（ROC）曲线准确评估回归模型的性能。
我们发现体素计数特征（P<0.001）可作为区分IPA与浸润前病变的预测因子。然而，与MIA相比，表面积特征（P=0.040）和突出表面积特征（P=0.013）可作为IPA的预测因子。此外，相关性特征（P=0.046）能更好地区分浸润前病变与MIA。
尽管这三种疾病在CT图像上均可能表现为GGO，但基于CT图像的纹理特征可对浸润前病变、MIA和IPA进行鉴别。这三种疾病的诊断对临床手术非常重要。
