Prediction model of radiation pneumonitis after chemoradiotherapy for esophageal cancer based on dosiomics
Bai Xue1,2,3, Yang Jing1,2, Zhuang Lei4, Zhang Danhong2,5, Chen Ying2,5, Du Xianghui2,5, Sheng Liming2,5
1Department of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China; 2Zhejiang Key Laboratory of Thoracic Oncology, Hangzhou 310022, China; 3Zhejiang Key Laboratory of Radiation Oncology, Hangzhou 310022, China; 4the Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China; 5Department of Thoracic Radiotherapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China
Abstract：Objective To study the risk factors and prediction model of radiation pneumonitis (RP) after radical chemoradiotherapy for locally advanced esophageal cancer based on dosiomics. Methods Clinical data of 105 patients with esophageal cancer undergoing radical chemoradiotherapy at Zhejiang Cancer Hospital between January 2020 and August 2021 were retrospectively analyzed. RP was scored using the National Cancer Institute's Common Terminology Criteria for Adverse Events version 5.0 (CTCAE 5.0). Clinical factors, traditional dosimetric features and dosiomics features were collected, respectively. The features for predicting PR were analyzed by limma package. Support vector machine, k-nearest neighbor, decision tree, random forest and extreme gradient boosting were used to establish the prediction model, and the ten-fold cross-validation method was employed to evaluate the performance of the model. The differences of this model when different features were chosen were analyzed by delong test. Results The incidence of RP in the whole group was 21.9%. One clinical factor, 6 traditional dosimetric features and 42 dosiomics features were significantly correlated with the occurrence of RP (all P<0.05). Support vector machine using linear kernel function yielded the optimal prediction performance, and the area under the receiver operating characteristic (ROC) without and with dosiomics features was 0.72 and 0.75, respectively. The models established by support vector machine, random forest and extreme gradient boosting were significantly different with and without dosiomics features (all P<0.05). Conclusion The addition of dosiomics features can effectively improve the performance of the prediction model of RP after radiotherapy for esophageal cancer.
Bai Xue,Yang Jing,Zhuang Lei et al. Prediction model of radiation pneumonitis after chemoradiotherapy for esophageal cancer based on dosiomics[J]. Chinese Journal of Radiation Oncology, 2023, 32(7): 620-625.
[1] 韩水云, 孙晓江, 许亚萍. 放射性肺损伤的风险预测研究进展[J].中华放射肿瘤学杂志,2015,24(2):222-224. DOI: 10.3760/cma.j.issn.1004-4221.2015.02.029. Han SY, Sun XJ, Xu YP. Research progress in risk prediction of radiation induced lung injury[J]. Chin J Radiat Oncol,2015,24(2):222-224. DOI: 10.3760/cma.j.issn.1004-4221.2015.02.029. [2] Palma DA, Senan S, Tsujino K, et al. Predicting radiation pneumonitis after chemoradiation therapy for lung cancer: an international individual patient data meta-analysis[J]. Int J Radiat Oncol Biol Phys, 2013,85(2):444-450. DOI: 10.1016/j.ijrobp.2012.04.043. [3] Jain V, Berman AT.Radiation pneumonitis: old problem, new tricks[J]. Cancers (Basel), 2018,10(7):222. DOI: 10.3390/cancers10070222. [4] Shintani T, Kishi N, Matsuo Y, et al. Incidence and risk factors of symptomatic radiation pneumonitis in non-small-cell lung cancer patients treated with concurrent chemoradiotherapy and consolidation durvalumab[J]. Clin Lung Cancer, 2021,22(5):401-410. DOI: 10.1016/j.cllc.2021.01.017. [5] 王谨, 包勇, 庄婷婷, 等. 非小细胞肺癌同期放化疗后重度急性放射性肺炎的预测模型研究[J].中华放射肿瘤学杂志,2013,22(6):455-459. DOI: 10.3760/cma.j.issn.1004-4221.2013.06.009. Wang J, Bao Y, Zhuang TT, et al. Study on prediction models for severe acute radiation pneumonitis in patients with non-small cell lung cancer after concurrent chemoradiotherapy[J]. Chin J Radiat Oncol,2013,22(6):455-459. DOI: 10.3760/cma.j.issn.1004-4221.2013. 06.009. [6] Ren WT, Liang B, Sun C, et al. Dosiomics-based prediction of radiation-induced hypothyroidism in nasopharyngeal carcinoma patients[J]. Phys Med, 2021,89:219-225. DOI: 10.1016/j.ejmp.2021.08.009. [7] Liang B, Yan H, Tian Y, et al.Dosiomics: extracting 3D spatial features from dose distribution to predict incidence of radiation pneumonitis[J]. Front Oncol, 2019,9:269. DOI: 10.3389/fonc.2019.00269. [8] Rossi L, Bijman R, Schillemans W, et al. Texture analysis of 3D dose distributions for predictive modelling of toxicity rates in radiotherapy[J]. Radiother Oncol, 2018,129(3):548-553. DOI: 10.1016/j.radonc.2018.07.027. [9] Lyman JT. Complication probability as assessed from dose-volume histograms[J].Radiat Res Suppl, 1985, 8:S13-S19. [10] Seppenwoolde Y, Lebesque JV, de Jaeger K, et al. Comparing different NTCP models that predict the incidence of radiation pneumonitis. Normal tissue complication probability[J]. Int J Radiat Oncol Biol Phys, 2003,55(3):724-735. DOI: 10.1016/s0360-3016(02)03986-x. [11] van Griethuysen J, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype[J]. Cancer Res, 2017,77(21):e104-e107. DOI: 10.1158/0008-5472.CAN-17-0339. [12] Ritchie ME, Phipson B, Wu D, et al.limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Res, 2015,43(7):e47. DOI: 10.1093/nar/gkv007. [13] Shen WT, Song ZG, Zhong X, et al.Sangerbox: a comprehensive, interaction-friendly clinical bioinformatics analysis platform[J]. iMeta, 2022,1(3):e36. DOI:10.1002/imt2.36. [14] Buizza G, Paganelli C, D'Ippolito E, et al. Radiomics and dosiomics for predicting local control after carbon-ion radiotherapy in skull-base chordoma[J]. Cancers (Basel), 2021,13(2):339. DOI: 10.3390/cancers13020339. [15] Smyth GK. Limma: linear models for microarray data[M]//Gentleman R, Carey VJ, Huber W, et al. Bioinformatics and Computational Biology Solutions Using R Bioconductor. Berlin: Springer, 2010:397-420. [16] 白雪, 王升晔, 王彬冰, 等. 全卷积网络放疗三维剂量分布预测研究[J].中华放射肿瘤学杂志,2020,29(8):666-670. DOI: 10.3760/cma.j.cn113030-20191018-00427. Bai X, Wang SY, Wang BB, et al. Study of three-dimensional dose distribution prediction model in radiotherapy planning based on full convolution network[J].Chin J Radiat Oncol,2020,29(8):666-670. DOI: 10.3760/cma.j.cn113030-20191018-00427.
2023, Vol. 32 
