| 孔燕,吴佳,魏贤顶,孔旭东,鲍而文,孙宗琼,黄建锋.肺癌放疗患者症状性放射性肺炎预测的CT影像组学研究[J].中华放射医学与防护杂志,2022,42(2):115-120 |
| 肺癌放疗患者症状性放射性肺炎预测的CT影像组学研究 |
| Application of CT radiomics analysis to predict symptomatic radiation pneumonitis for lung cancer |
| 投稿时间：2021-07-30 |
| DOI：10.3760/cma.j.cn112271-20210730-00301 |
| 中文关键词: 肺癌 放射治疗 放射性肺炎 影像组学 计算机体层成像 |
| 英文关键词:Lung cancer Radiotherapy Radiation pneumonitis Radiomics CT |
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| 中文摘要: |
| 目的 基于肺癌患者放疗前的CT影像组学特征，综合临床信息与放疗剂量学特征，利用机器学习方法构建症状性放射性肺炎的预测模型。方法 回顾性收集2018年11月至2020年4月在江南大学附属医院接受放疗的103例肺癌患者的临床与剂量学资料。获取这些患者放疗前胸部CT影像，勾画双侧正常肺组织结构，提取250种影像组学特征。用单因素分析研究临床、剂量学特征与放射性肺炎发生的相关性。收集所有影像组学特征、临床和剂量学特征作为潜在预测因子，通过LASSO回归机器学习方法筛选特征，并得到肺炎预测模型。然后根据筛选的特征建立放射性肺炎发生风险的列线图。结果 单因素分析结果表明，症状性放射性肺炎与双侧正常肺组织的平均肺剂量（MLD）、V20 Gy和V30 Gy的相关性具有统计学意义（t=2.20、2.34、2.93，P<0.05）。在综合所有影像组学特征、临床和放疗剂量学特征后，本研究共筛选出4个特征，为肺的剂量体积百分数V30 Gy，和3个影像组学特征，包括灰度共生矩阵类别的熵特征、小波变换直方图类别的均值及中位数特征。基于这些特征所构建的肺炎预测模型的曲线下面积（AUC）为0.757。绘制了可根据特征值给予个体化的风险预测与提前干预的列线图。结论 放疗前的CT影像组学结合剂量学特征可用于预测症状性肺炎的发生，可望为临床提前干预提供帮助。 |
| 英文摘要: |
| Objective To build a predictive model for symptomatic radiation pneumonitis(RP) using the pretreatment CT radiomics features, clinical and dosimetric data of lung cancer patients by using machine learning method. Methods A retrospective analysis of 103 lung cancer patients who underwent radiotherapy in the Affiliated Hospital of Jiangnan University from November 2018 to April 2020 was performed. Total normal lung tissues were segmented as an interested volume in pretreatment CT images, and then 250 radiomics features were extracted. The correlations of RP and clinical or dosimetric features were firstly investigated with univariate analysis. Then all clinical data, dosimetric data and CT radiomics features were collected and considered as predictors for modeling of RP grade ≥ 2. Features were selected through LASSO machine learning method, and the predictive model was built. Finally, nomogram for risk of RP were obtained according to the selected features. Results The result of univariate analysis showed that symptomatic RP was significantly correlated with lung dosimetric parameters including mean lung dose (MLD), V20 Gy and V30 Gy(t=2.20, 2.34 and 2.93, P<0.05). Four features, including lung dose volume percentage V30 Gyand three radiomics features, entropy feature of GLCM, mean and median feature of wavelet histogram were selected among all clinical, dosimetric features and radiomics features. AUC of the predicted model obtained from selected features reached 0.757. For convenient clinical use, the nomogram were obtained, and then personalized RP risk prediction and early intervention could be performed according to this nomogram. Conclusions Pretreatment CT radiomics and dosimetric features can be used in predicting symptomatic RP, which will be useful for advanced intervention treatment. |
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