ArticleFrontiers in oncology2025
The impact of Inter-observation variation on radiomic features of pulmonary nodules.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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.
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