Establishment of prediction model for symptomatic radiation pneumonitis: based on a longitudinal cohort
10.3760/cma.j.cn113030-20231230-00222
- VernacularTitle:症状性放射性肺炎预测模型的建立：基于纵向队列的研究
- Author:
Li WANG
1
;
Han BAI
;
Fei LU
;
Yaoxiong XIA
;
Man LI
;
Na PENG
;
Zhe ZHANG
;
Simeng TAN
;
Bo LI
;
Chengshu GONG
;
Jingyan GAO
;
Qian AN
;
Lan LI
;
Wenhui LI
Author Information
1. 北京大学肿瘤医院云南医院 云南省肿瘤医院 昆明医科大学第三附属医院放射治疗科，昆明 650000
- Keywords:
Thoracic neoplasms;
Radiotherapy;
Dosimetry;
Variation of dose interval;
Prediction model
- From:
Chinese Journal of Radiation Oncology
2024;33(10):915-921
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a prediction model for symptomatic radiation pneumonitis (SRP) after radiotherapy for thoracic cancer based on a longitudinal cohort and dose interval variations.Methods:Clinical data of 587 patients who received thoracic radiotherapy in Department of Radiotherapy of Yunnan Cancer Hospital from July 2022 to June 2023 were retrospectively analyzed. The National Cancer Institute common terminology criteria for adverse events (CTCAE) version 5.0 was used to grade radiation pneumonitis, and clinical factors, traditional independent dosimetric characteristics and dose interval variation characteristics were collected. Features used to predict the occurrence of SRP were screened using genetic algorithms and analyzed the correlation between the selected features and SRP occurrence. Predictive models for SRP occurrence were established using the selected features and evaluated, and the optimal predictive model was visualized using a column chart.Results:The incidence of SRP was 35.94%. Five clinical factors, seven independent dosimetric features and six dose interval variation features were screened out by genetic algorithms to effectively predict the occurrence of SRP. The area under ROC curve (AUC) of clinical factors combined with traditional independent dosimetric factors and dose interval variation factors was 76%. The AUC of clinical factors combined with traditional independent dosimetric factors and that of clinical factors combined with dose interval variation factors was 69% and 67%, respectively. The addition of the characteristics of dose interval variation factors significantly improved the effectiveness of the prediction model.Conclusions:The supplement of the characteristics of dose interval variation factors can significantly improve the performance of the SRP prediction model for thoracic tumors after radiotherapy. The SRP prediction model based on dose interval variations can effectively predict the occurrence of SRP.
