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ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1567028
The impact of Inter-observation variation on radiomic features of pulmonary nodules
Provisionally accepted- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
Objective: In this study, we aimed to comprehensively and systematically analyze the radiomic features of pulmonary nodules and explore the influence of inter-observation variation (IOV) in segmentation regions of interest (ROI) on radiomic features, providing reference information for pulmonary nodule radiomics research. Method: Six clinicians with varying experience and expertise manually outlined ROIs for 232 pulmonary nodules, while an artificial intelligence (AI) algorithm was trained for automated segmentation. The segmentation by the most experienced cardiothoracic diagnostician (Doctor A) served as the reference standard. Inter-observer variability was assessed through diameter measurements, segmentation ROI consistency analysis, and radiomic features stability analysis. Results: Of all radiomics features analyzed, 1071 (85.96%) demonstrated good stability (overall concordance correlation coefficient [OCCC] ≥ 0.75), with 766 (61.48%) exhibiting very good stability (OCCC ≥ 0.90). Among the eight radiomic feature types, Original _first-order, Original_GLCM, Original_GLRLM, Original_GLSZM, LOG, and wavelet features all achieved stability rates exceeding 80.00%, with 91.59% of the LOG features having good stability. The Original features showed good stability (median OCCC: 0.92-0.95, IQR: 0.12-0.19), both in the overall distribution and in the different feature categories. The median OCCC value for the LOG features (median: 0.94, IQR: 0.08) was significantly higher than that for the Wavelet features (median: 0.91, IQR: 0.13). There was no statistically significant difference in stability between the Original and LOG feature subgroups (P > 0.05). In contrast, statistically significant differences were observed between the wavelet feature subgroups (P < 0.05), with Wavelet_LLL and Wavelet_LLH transformation yielding higher stability. Conclusion: 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.
Keywords: Inter-observation variation (IOV), Nodule segmentation, Radiomics, Overall concordance correlation coefficient (OCCC), pulmonary nodules
Received: 26 Jan 2025; Accepted: 03 Apr 2025.
Copyright: © 2025 Zhu, Xu, Lou, Qiu, Huang, Huang, Xie and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hongjie Hu, Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.