Liu Chunmei, He Yuzheng, Luo Jianmin
Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, Hebei Province, China.
Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, Hebei Province, China.
BMC Cancer. 2025 Apr 17;25(1):728. doi: 10.1186/s12885-025-14094-z.
Extracting image features can predict the prognosis and treatment effect of non-small cell lung cancer, which has been increasingly confirmed. However, the specific operation using 3D-Slicer still lacks standardization. For example, image segmentation is manually performed based on the lung window or automatically performed through the mediastinal window. The images used for feature extraction are either enhanced or plain scanned. It is questionable whether these influencing factors will affect the extraction results and which results will be affected. This article intends to preliminarily explore the above issues.
This article downloaded images of 22 patients with lung cancer from The Cancer Imaging Archive (TCIA), including 11 cases of adenocarcinoma and 11 cases of squamous cell carcinoma. Perform tumor image segmentation on the lung window and mediastinal window of the plain scan image, and the lung window and mediastinal window of the enhanced image. Manual drawing is used on the lung window, and automatic drawing is used on the mediastinal window and make manual modifications. Extracting radiomics features using Python radiomics. Firstly, analyze the image features of the original sequence and perform the Shapiro test. If it follows a normal distribution, perform an analysis of variance. If it does not follow a normal distribution, perform the Friedman test. Compare the significantly different image features pairwise. Then, a preliminary analysis was conducted on the differences between squamous cell carcinoma and adenocarcinoma in each group.
A total of 88 sets of imaging features were extracted, with 107 features in each group. Among them, 33 features showed significant differences. Continuing with pairwise repeated testing, it was found that there were 2 significant differences between enhanced and plain lung windows. There were 12 significant differences between enhanced lung windows and plain mediastinal windows. There is one significant difference between plain scanning and enhancement mediastinal window. There are 14 significant differences between the plain lung window and the enhanced mediastinal window groups. There are 14 significant differences between the lung window and the mediastinal window in the plain scan. There are 13 significant differences between the enhanced lung window and the mediastinal window. According to pathological grouping testing, it was found that there 54 significant differences between squamous cell carcinoma and adenocarcinoma.
The enhancement of lung CT has a relatively small impact on extracting image features, while selecting lung or mediastinal windows for image segmentation has a significant impact on extracting image features. Therefore, choosing lung or mediastinal windows for feature extraction should be carefully considered, as the size of the image segmentation range has a significant impact on image features. The impact of lung squamous cell carcinoma and adenocarcinoma on imaging features is also significant, indicating a high possibility of distinguishing between squamous cell carcinoma and adenocarcinoma based on radiomics (Liu C, He Y, Luo J, The Influence of Image Selection and Segmentation on the Extraction of Lung Cancer Imaging Radiomics Features Using 3D-Slicer Software, 2024).
提取图像特征可预测非小细胞肺癌的预后和治疗效果，这一点已得到越来越多的证实。然而，使用3D-Slicer进行的具体操作仍缺乏标准化。例如，图像分割是基于肺窗手动进行的，或者通过纵隔窗自动进行。用于特征提取的图像要么是增强扫描的，要么是平扫的。这些影响因素是否会影响提取结果以及哪些结果会受到影响尚存在疑问。本文旨在初步探讨上述问题。
本文从癌症影像存档库（TCIA）下载了22例肺癌患者的图像，其中腺癌11例，鳞癌11例。对平扫图像的肺窗和纵隔窗以及增强图像的肺窗和纵隔窗进行肿瘤图像分割。肺窗采用手动绘制，纵隔窗采用自动绘制并进行手动修正。使用Python影像组学提取影像组学特征。首先，分析原始序列的图像特征并进行夏皮罗检验。如果服从正态分布，则进行方差分析。如果不服从正态分布，则进行弗里德曼检验。对差异显著的图像特征进行两两比较。然后，对每组中鳞癌和腺癌之间的差异进行初步分析。
共提取了88组影像特征，每组107个特征。其中，33个特征显示出显著差异。继续进行两两重复检验，发现增强肺窗和平扫肺窗之间有2个显著差异。增强肺窗和平扫纵隔窗之间有12个显著差异。平扫纵隔窗和增强纵隔窗之间有1个显著差异。平扫肺窗组和增强纵隔窗组之间有14个显著差异。平扫时肺窗和纵隔窗之间有14个显著差异。增强肺窗和纵隔窗之间有13个显著差异。根据病理分组检验，发现鳞癌和腺癌之间有54个显著差异。
肺部CT增强对提取图像特征的影响相对较小，而选择肺窗或纵隔窗进行图像分割对提取图像特征有显著影响。因此，在选择肺窗或纵隔窗进行特征提取时应谨慎考虑，因为图像分割范围的大小对图像特征有显著影响。肺鳞癌和腺癌对影像特征的影响也很显著，表明基于影像组学区分鳞癌和腺癌的可能性很大（刘C，何Y，罗J，《图像选择和分割对使用3D-Slicer软件提取肺癌影像组学特征的影响》，2024）。
