ArticleBMC cancer2025
The influence of image selection and segmentation on the extraction of lung cancer imaging radiomics features using 3D-Slicer software.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Development and validation of a clinical-radiomics nomogram for differentiatingFrontiers in pediatrics · 2026Article
- The influence of image selection and segmentation on the extraction of lung cancer imaging radiomics features using 3D-Slicer software.BMC cancer · 2025Article
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3 authors.
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Abstract
purposeExtracting 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.
methodsThis 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.
resultsA 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.
conclusionThe 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).
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