The influence of image selection and segmentation on the extraction of lung cancer imaging radiomics features using 3D-Slicer software

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).

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Publication Details

Journal
BMC Cancer
Published
2025-04-17
DOI
https://doi.org/10.1186/s12885-025-14094-z
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36
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article

The influence of image selection and segmentation on the extraction of lung cancer imaging radiomics features using 3D-Slicer software

Chunmei Liu, J M Luo, Yuzheng He
1 citations
BMC Cancer
Radiomics and Machine Learning in Medical Imaging
1.36
article

The influence of image selection and segmentation on the extraction of lung cancer imaging radiomics features using 3D-Slicer software

Chunmei Liu, J M Luo, Yuzheng He
article en
1 citations

Abstract

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).

BMC CancerVol. 25(1)
Hebei Medical University (CN), Second Hospital of Hebei Medical University (CN)
Good health and well-being
Openalex Percentile: Top 21%
Radiomics and Machine Learning in Medical Imaging
1.36
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