The impact of Inter-observation variation on radiomic features of pulmonary nodules
Segmentation results indicated that while IOV influenced radiomic features of pulmonary nodules, most (85.96%) of the features were well stabilized and relatively unaffected. Enhancing segmentation ROI consistency helps minimize the impact of IOV on the radiomic features of pulmonary nodule images. Original and LOG features demonstrated high stability, whereas Wavelet features were more susceptible to IOV.
Authors
- Shaoyu Huang
- Dingping Huang
- Hongjie Hu (ORCID: https://orcid.org/0000-0002-9859-5860)
- Kaihua Lou (ORCID: https://orcid.org/0000-0003-4689-3548)
- Fangyi Xu
- Xia Qiu (ORCID: https://orcid.org/0000-0003-1639-9725)
- Dong Xie (ORCID: https://orcid.org/0000-0002-3715-1801)
- Wenchao Zhu (ORCID: https://orcid.org/0009-0001-2639-6643)
Institutions
- Sir Run Run Shaw Hospital (CN)
- China International Science and Technology Cooperation (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-04-24
- DOI
- https://doi.org/10.3389/fonc.2025.1567028
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Zhejiang Province