基于剂量组学预测肺癌患者放射性肺炎发生的研究

doi:10.3760/cma.j.cn113030-20211115-00466

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Abstract Objective To explore the potential of dosiomics in predicting the incidence of radiation pneumonitis by extracting dosiomic features of definitive radiotherapy for lung cancer, and building a machine learning model. Methods The clinical data, dose files of radiotherapy, planning CT and follow‐up CT of 314 patients with lung cancer undergoing definitive radiotherapy were collected retrospectively. According to the clinical data and follow‐up CT, the radiation pneumonia was graded, and the dosiomic features of the whole lung were extracted to establish a machine learning model. Dosiomic features associated with radiation pneumonia by LASSO‐LR with 1000 bootstrap and AIC backward method with 1000 bootstraps were selected. Training cohort and validation cohort were randomly divided on the basis of 7:3.Logistic regression was used to establish the prediction model, and ROC curve and calibration curve were adopted to evaluate the performance of the model. Results A total of 120 dosiomic features were extracted. After LASSO‐LR dimensionality reduction, 12 features were selected into the "feature pool".After AIC, 6 dosiomic features were finally selected for model construction. The AUC of training cohort was 0.77(95%CI: 0.65 to 0.87), and the AUC of validation cohort was 0.72 (95%CI: 0.64 to 0.81). Conclusion The dosiomics prediction model has the potential to predict the incidence of radiation pneumonia, but it still needs to include multicenter data and prospective data.

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	Yan Meng
	Zhang Zhen
	Yu Jiaqi
	Wang Wei
	Wang Qingxin
	Zhao Lujun

Key words： Lung neoplasms Dosiomics Radiation pneumonitis Machine learning

Received: 15 November 2021

Fund:National Natural Science Foundation of China(81872472)

Corresponding Authors: Wang Qingxin, Email: mpwangqx@163.com

Cite this article:

Yan Meng,Zhang Zhen,Yu Jiaqi et al. Dosiomics‐based prediction of incidence of radiation pneumonitis in lung cancer patients[J]. Chinese Journal of Radiation Oncology, 2022, 31(8): 698-703.

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http://journal12.magtechjournal.com/Jweb_fszlx/EN/10.3760/cma.j.cn113030-20211115-00466 OR http://journal12.magtechjournal.com/Jweb_fszlx/EN/Y2022/V31/I8/698

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