Preliminary study of predicting radiation pneumonitis based on radiomics technology
10.3760/cma.j.cn113030-20190225-00063
- VernacularTitle:基于放射组学预测放射性肺炎的初步研究
- Author:
Zhen ZHANG
1
;
Lujun ZHAO
;
Wei WANG
;
Jingjing CUI
;
Qi WANG
;
Ying LIU
;
Qingxin WANG
;
Daguang ZHANG
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
- From:
Chinese Journal of Radiation Oncology
2020;29(6):427-431
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To identify the radiomics features related to the occurrence of radiation pneumonitis based on localized CT images of the chest in lung cancer patients, establish a machine learning model and investigate the value of radiomics technology in predicting the incidence of radiation pneumonitis.Methods:Clinical data of 86 patients with stage Ⅲ non-small cell lung cancer who received radical intensity-modulated radiation therapy (IMRT) were retrospectively analyzed. The radiation pneumonitis was graded by follow-up imaging data and clinical information. The planning CT images were collected. The lung was used as the volume of interest for extraction of radiomics features. The radiomics features, clinical and dosimetric parameters associated with the incidence of radiation pneumonitis were analyzed. Using the support vector machine to construct the model, the prediction performance of the model was evaluated by the five-fold verification method.Results:A total of 1029 radiomics features were extracted from CT images and 5 features were selected by ANOVA and LASSO. Two validation sets showed differences between adopting radiomics features alone and incorporating clinical and dosimetric parameters and radiomics features (AUC=0.67 and 0.71, respectively).Conclusions:The radiomics model constructed by planning CT images of lung cancer patients has the potential to predict the occurrence of radiation pneumonitis. Addition of clinical and dosimetric parameters can further improve the prediction performance of the model.
