Radiat Oncol J.  2024 Mar;42(1):63-73. 10.3857/roj.2023.00605.

Interobserver delineation variability of computed tomography-based radiomic features of the parotid gland

• Buasawat K
• Chamchod S
• Fuangrod T
• Suntiwong S
• Liamsuwan T

• KMID: 2553865
• DOI: http://doi.org/10.3857/roj.2023.00605

Abstract

Purpose
To assess the interobserver delineation variability of radiomic features of the parotid gland from computed tomography (CT) images and evaluate the correlation of these features for head and neck cancer (HNC) radiotherapy patients.
Materials and Methods
Contrast-enhanced CT images of 20 HNC patients were utilized. The parotid glands were delineated by treating radiation oncologists (ROs), a selected RO and AccuContour auto-segmentation software. Dice similarity coefficients (DSCs) between each pair of observers were calculated. A total of 107 radiomic features were extracted, whose robustness to interobserver delineation was assessed using the intraclass correlation coefficient (ICC). Pearson correlation coefficients (r) were calculated to determine the relationship between the features. The influence of excluding unrobust features from normal tissue complication probability (NTCP) modeling was investigated for severe oral mucositis (grade ≥3).
Results
The average DSC was 0.84 (95% confidence interval, 0.83–0.86). Most of the shape features demonstrated robustness (ICC ≥0.75), while the first-order and texture features were influenced by delineation variability. Among the three observers investigated, 42 features were sufficiently robust, out of which 36 features exhibited weak correlation (|r|<0.8). No significant difference in the robustness level was found when comparing manual segmentation by a single RO or automated segmentation with the actual clinical contour data made by treating ROs. Excluding unrobust features from the NTCP model for severe oral mucositis did not deteriorate the model performance.
Conclusion
Interobserver delineation variability had substantial impact on radiomic features of the parotid gland. Both manual and automated segmentation methods contributed similarly to this variation.

Keyword

Radiomic features; Parotid gland; Interobserver variability; Autosegmentation; Head and neck cancer