| 闫朱敏,Zhang Jingqiao,Chen Xiaojian,等.基于放疗过程中腮腺图像纹理特征的变化早期预测头颈部肿瘤放射性口干症[J].中华放射医学与防护杂志,2019,39(4):262-267.Yan Zhumin,Zhang Jingqiao,Chen Xiaojian,et al.Study on the relationship between CT image texture changes of parotids and acute xerostomia during radiation treatment for head and neck cancer[J].Chin J Radiol Med Prot,2019,39(4):262-267 |
| 基于放疗过程中腮腺图像纹理特征的变化早期预测头颈部肿瘤放射性口干症 |
| Study on the relationship between CT image texture changes of parotids and acute xerostomia during radiation treatment for head and neck cancer |
| 投稿时间：2018-12-05 |
| DOI：10.3760/cma.j.issn.0254-5098.2019.04.004 |
| 中文关键词: 腮腺 口干症 纹理特征 CT图像 早期预测 |
| 英文关键词:Parotid gland Radiation-induced xerostomia Texture CT image Early prediction |
| 基金项目:河南省科技攻关项目（152102310156） |
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| 中文摘要: |
| 目的 分析头颈部肿瘤患者放疗过程中腮腺图像纹理特征的变化，研究其与急性放射性口干症（级别）的关系。建立数学模型，早期预测放射性口干严重性。方法 观察23例头颈部肿瘤放疗的患者，根据放射治疗肿瘤协作组（RTOG）标准评价患者每周口干程度。采集这些患者放疗中每周的验证CT图像，传至MIM系统，勾画出腮腺的轮廓，在MATLAB（R2013a）中开发内部分析程序。分析放疗过程中每周腮腺CT图像的纹理特征的变化，包括平均CT值（MCTN）、标准差（STD）、偏斜度（skewness）、峰度（kurtosis）和熵（entropy），以及体积的变化。建立数学模型，并利用KNN方法对所建模型进行优化，预测口干级别。结果 平均CT值和体积的变化与口干程度无明显相关性（P>0.05），但根据二者每周的变化建立模型，预测口干级别，准确度为99%。结论 同时基于平均CT值和相对体积变化建立模型可早期预测口干严重程度。 |
| 英文摘要: |
| Objective To investigate the relationship between parotid image texture and acute radiation xerostomia (grade) during radiotherapy in patients with head and neck cancer. The mathematical model was established to predict the severity of radiation dry mouth in the early stage. Methods 23 patients with head and neck cancer treated with radiotherapy were observed. The degree of xerostomia was evaluated according to RTOG criteria. The weekly validated CT images of these patients during radiotherapy were collected and transmitted to the MIM system to outline the parotid gland, and an internal analysis program was developed in MATLAB (R2013a). The changes of texture features of weekly parotid CT images during radiotherapy were analyzed, including mean CT value (MCTN), standard deviation (STD), skewness, kurtosis, entropy and volume. The mathematical model was established, and the KNN method was used to optimize the model and predict the level of xerostomia. Results There was no significant correlation among the changes of MCTN, volume and the degree of xerostomia(P>0.05). However, according to the weekly changes of MCTN and volume, the model was established to predict the grade of xerostomia with an accuracy of 99%. Conclusions The changes of parotid gland MCTN and volume were significantly correlated with acute radiation xerostomia during radiotherapy for head and neck cancer, and the MCTN changes can be used to predict the severity of xerostomia in the early stage. |
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