Study of application of radiomics model in predicting radiation pneumontis in patients with lung cancer and esophageal cancer
10.3760/cma.j.cn113030-20210119-00032
- VernacularTitle:利用放射组学模型预测肺癌和食管癌患者发生放射性肺炎的研究
- Author:
Jiaqi YU
1
;
Zhen ZHANG
;
Kai REN
;
Wei WANG
;
Ying LIU
;
Qian LI
;
Zhaoxiang YE
;
Lujun ZHAO
Author Information
1. 天津医科大学肿瘤医院放疗科 国家肿瘤临床医学研究中心 天津市"肿瘤防治"重点实验室 天津市恶性肿瘤临床医学研究中心 300060
- Keywords:
Lung neoplasm;
Esophageal neoplasm;
Radiomics;
Radiation pneumonitis
- From:
Chinese Journal of Radiation Oncology
2021;30(11):1111-1116
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze and explore the common radiomics features of radiation pneumonitis (RP) in patients with lung cancer and esophageal cancer, and then establish a prediction model that can predict the occurrence of RP in two types of cancer after radiotherapy.Methods:Clinical data of 100 patients with stage Ⅲ lung cancer and 100 patients with stage Ⅲ esophageal cancer who received radical radiotherapy were retrospectively analyzed. The RP was graded by imaging data and clinical information during follow-up, and the planning CT images were collected. The whole lung was used as the volume of interest to extract radiomics features. The radiomics features, clinical and dosimetric parameters related to RP were analyzed, and the model was constructed by machine learning.Results:A total of 1691 radiomics features were extracted from CT images. After ANOVA and LASSO dimensionality reduction in lung cancer and esophageal cancer patients, 8 and 6 radiomics features associated with RP were identified, and 5 of them were the same. Using the random forest to construct the prediction model, lung cancer and esophageal cancer were alternately used as the training and validation sets. The AUC values of esophageal cancer and lung cancer as the independent validation set were 0.662 and 0.645.Conclusions:It is feasible to construct a common prediction model of RP in patients with lung cancer and esophageal cancer. Nevertheless, it is necessary to further expand the sample size and include clinical and dosimetric parameters to increase its accuracy, stability and generalization ability.
