[1]周露,王琳婧,张国前,等.基于影像组学和剂量组学的放射性肺炎预测研究[J].中国医学物理学杂志,2023,40(7):808-813.[doi:DOI:10.3969/j.issn.1005-202X.2023.07.003]
　ZHOU Lu,WANG Linjing,ZHANG Guoqian,et al.Prediction of radiation pneumonitis based on radiomics and dosiomics[J].Chinese Journal of Medical Physics,2023,40(7):808-813.[doi:DOI:10.3969/j.issn.1005-202X.2023.07.003]
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基于影像组学和剂量组学的放射性肺炎预测研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第7期
页码:
808-813
栏目:
其他（激光医学等）
出版日期:
2023-07-15

文章信息/Info

Title:
Prediction of radiation pneumonitis based on radiomics and dosiomics
文章编号:
1005-202X（2023）07-0808-06
作者:
周露; 王琳婧; 张国前; 李慧君; 廖煜良; 吴书裕
广州医科大学附属肿瘤医院放疗科， 广东 广州 510095
Author(s):
ZHOU Lu;  WANG Linjing;  ZHANG Guoqian;  LI Huijun;  LIAO Yuliang;  WU Shuyu
Department of Radiation Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou 510095, China
关键词:
非小细胞肺癌; 影像组学; 剂量组学; 放射性肺炎; 机器学习
Keywords:
non-small-cell lung cancer radiomics dosiomics radiation pneumonitis machine learning
分类号:
R312；R818.7
DOI:
DOI:10.3969/j.issn.1005-202X.2023.07.003
文献标志码:
A
摘要:
目的：旨在利用影像组学和剂量组学的多组学方法，建立并验证一个有效的基于CT图像的放射性肺炎（RP）预测模型。方法：对2019年至2021年在广州医科大学附属肿瘤医院接受放疗的91例非小细胞肺癌患者进行回顾性分析。将除去临床靶区的全肺（Lung-CTV）作为感兴趣区域，从Lung-CTV区域的CT图像和剂量分布中提取影像组学和剂量组学特征。将单独的剂量体积直方图（DVH）特征、影像组学结合DVH（radio+DVH）特征、影像组学结合剂量组学（radio+dose）特征，分别输入11个不同的分类器来构建预测模型，采用五倍交叉验证法来完成分类实验。利用接受者操作特征（ROC）曲线下的面积（AUC）、准确性、精确性、召回率和F1值来评估预测模型的性能。结果：与DVH模型相比，radio+DVH和radio+dose的AUC值更高，差异有统计学意义（P<0.05）。与DVH和radio+DVH模型相比，radio+dose的准确率和F1值更高，差异有统计学意义（P<0.05）。结论：使用基于机器学习的影像组学和剂量组学的多组学方法预测RP的性能更好，有望为临床治疗提供指导。
Abstract:
Abstract: Objective To establish and validate an effective CT-based radiation pneumonitis (RP) prediction model using the multi-omics approach combining radiomics with dosiomics. Methods A retrospective analysis was carried out on 91 non-small-cell lung cancer patients who were treated with radiotherapy from 2019 to 2021 in Affiliated Cancer Hospital and Institute of Guangzhou Medical University. The whole lung excluding clinical target volume (Lung-CTV) was taken as the region of interest (ROI), and the radiomics and dosiomics features were extracted from the CT image and dose distribution of Lung-CTV. The dose-volume histogram (DVH) features, radiomics combined with DVH (radio+DVH) features, and radiomics combined with dosiomics (radio+dose) features were imported into 11 different classifiers for constructing prediction models. The 5-fold cross-validation was used to complete the classification experiment. The area under the curve (AUC) of the receiver operating characteristics (ROC), accuracy, precision, recall and F1-score were calculated to assess the model performances. Results The AUC of radio+DVH and radio+dose was higher than that of DVH model (P<0.05) and radio+dose had higher accuracy and F1-score than DVH and radio+DVH (P<0.05). Conclusion The multi-omics approach using machine learning-based radiomics and dosiomics to predict RP exhibits better performance and is expected to provide guidance for clinical treatment.

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备注/Memo

备注/Memo:
【收稿日期】2023-04-07 【基金项目】广东省医学科研基金（A2023291） 【作者简介】周露，工程师，研究方向：肿瘤放射物理，E-mail: zhoulu6105189@126.com 【通信作者】吴书裕，工程师，研究方向：肿瘤放射物理，E-mail: wsyeasy@outlook.com
更新日期/Last Update: 2023-07-15