Buasawat Kanyapat, Chamchod Sasikarn, Fuangrod Todsaporn, Suntiwong Sawanee, Liamsuwan Thiansin
Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, Thailand.
Department of Radiation Oncology, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok, Thailand.
Radiat Oncol J. 2024 Mar;42(1):63-73. doi: 10.3857/roj.2023.00605. Epub 2024 Feb 21.
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.
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).
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.
Interobserver delineation variability had substantial impact on radiomic features of the parotid gland. Both manual and automated segmentation methods contributed similarly to this variation.
评估计算机断层扫描（CT）图像中腮腺的影像组学特征在不同观察者之间的勾画变异性，并评估这些特征对头颈部癌（HNC）放疗患者的相关性。
使用20例HNC患者的增强CT图像。腮腺由放射肿瘤学家（ROs）、一名选定的RO和AccuContour自动分割软件进行勾画。计算每对观察者之间的骰子相似系数（DSC）。共提取107个影像组学特征，使用组内相关系数（ICC）评估其对观察者间勾画的稳健性。计算Pearson相关系数（r）以确定特征之间的关系。研究了从正常组织并发症概率（NTCP）模型中排除稳健性差的特征对严重口腔黏膜炎（≥3级）的影响。
平均DSC为0.84（95%置信区间，0.83 - 0.86）。大多数形状特征表现出稳健性（ICC≥0.75），而一阶和纹理特征受勾画变异性影响。在所研究的三名观察者中，42个特征具有足够的稳健性，其中36个特征表现出弱相关性（|r|<0.8）。将单个RO的手动分割或自动分割与治疗RO生成的实际临床轮廓数据进行比较时，稳健性水平没有显著差异。从严重口腔黏膜炎的NTCP模型中排除稳健性差的特征不会降低模型性能。
观察者间的勾画变异性对腮腺的影像组学特征有重大影响。手动和自动分割方法对这种变异性的影响相似。
