[an error occurred while processing this directive] | [an error occurred while processing this directive]
Dosiomics‐based prediction of incidence of radiation pneumonitis in lung cancer patients
Yan Meng1, Zhang Zhen1,2, Yu Jiaqi1, Wang Wei1, Wang Qingxin1, Zhao Lujun1
1Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital / National Clinical Research Center for Cancer / Key Laboratory of Cancer Prevention and Therapy, Tianjin / Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China; 2Department of Radiation Oncology(MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht 6229ET, The Netherlands
AbstractObjective 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.
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.
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.
[1] Giuranno L, Ient J, De Ruysscher D, et al.Radiation‐induced lung injury(RILI)[J]. Front Oncol, 2019,9:877.DOI: 10.3389/fonc.2019.00877.
[2] Hanania AN, Mainwaring W, Ghebre YT, et al.Radiation‐induced lung injury: assessment and management[J]. Chest, 2019,156(1): 150‐162.DOI: 10.1016/j.chest.2019.03.033.
[3] Jain V, Berman AT.Radiation pneumonitis: old problem, new tricks[J]. Cancers(Basel), 2018,10(7).DOI: 10.3390/cancers10070222.
[4] Stenmark MH, Cai XW, Shedden K, et al.Combining physical and biologic parameters to predict radiation‐induced lung toxicity in patients with non‐small‐cell lung cancer treated with definitive radiation therapy[J]. Int J Radiat Oncol Biol Phys, 2012,84(2): e217‐222.DOI: 10.1016/j.ijrobp.2012.03.067.
[5] Boonyawan K, Gomez DR, Komaki R, et al.Clinical and dosimetric factors predicting grade ≥2 radiation pneumonitis after postoperative radiotherapy for patients with non‐small cell lung carcinoma[J]. Int J Radiat Oncol Biol Phys, 2018,101(4): 919‐926.DOI: 10.1016/j.ijrobp.2018.04.012.
[6] Meng Y, Luo W, Wang W, et al.Intermediate dose‐volume parameters, not low‐dose bath, is superior to predict radiation pneumonitis for lung cancer treated with intensity‐modulated radiotherapy[J]. Front Oncol, 2020,10:584756.DOI: 10.3389/fonc.2020.584756.
[7] Yu JH, Zhao QY, Liu Y, et al.The plasma levels and polymorphisms of vitronectin predict radiation pneumonitis in patients with lung cancer receiving thoracic radiation therapy[J]. Int J Radiat Oncol Biol Phys, 2021,110(3): 757‐765.DOI: 10.1016/j.ijrobp.2021.01.018.
[8] 汪盛,王彩莲.放射性肺炎临床相关预测因素的研究进展[J]. 中华放射肿瘤学杂志,2021,30(3): 305‐310.DOI: 10.3760/cma.j.cn113030‐20200604‐00292.
Wang S, Wang CL.Research progress on clinical factors for predicting radiation pneumonitis[J]. Chin J Radiat Oncol, 2021,30(3): 305‐310.DOI: 10.3760/cma.j.cn113030‐20200604‐00292.
[9] Jiang W, Song Y, Sun Z, et al.Dosimetric factors and radiomics features within different regions of interest in planning CT images for improving the prediction of radiation pneumonitis[J]. Int J Radiat Oncol Biol Phys, 2021,110(4): 1161‐1170.DOI: 10.1016/j.ijrobp.2021.01.049.
[10] Gabryś HS, Buettner F, Sterzing F, et al.Design and selection of machine learning methods using radiomics and dosiomics for normal tissue complication probability modeling of xerostomia[J]. Front Oncol, 2018,8:35.DOI: 10.3389/fonc.2018.00035.
[11] Shi Z, Traverso A, van Soest J, et al.Technical note: ontology‐guided radiomics analysis workflow(O‐RAW)[J]. Med Phys, 2019,46(12): 5677‐5684.DOI: 10.1002/mp.13844.
[12] Compter I, Verduin M, Shi Z, et al.Deciphering the glioblastoma phenotype by computed tomography radiomics[J]. Radiother Oncol, 2021,160:132‐139.DOI: 10.1016/j.radonc.2021.05.002.
[13] Amar D, Zhang H, Tan KS, et al.A brain natriuretic peptide‐based prediction model for atrial fibrillation after thoracic surgery: development and internal validation[J]. J Thorac Cardiovasc Surg, 2019,157(6): 2493‐2499.e1.DOI: 10.1016/j.jtcvs.2019.01.075.
[14] Mehta V.Radiation pneumonitis and pulmonary fibrosis in non‐small‐cell lung cancer: pulmonary function, prediction,prevention[J]. Int J Radiat Oncol Biol Phys, 2005,63(1): 5‐24.DOI: 10.1016/j.ijrobp.2005.03.047.
[15] Arpin D, Perol D, Blay JY, et al.Early variations of circulating interleukin‐6 and interleukin‐10 levels during thoracic radiotherapy are predictive for radiation pneumonitis[J]. J Clin Oncol, 2005,23(34): 8748‐8756.DOI: 10.1200/JCO.2005.01.7145.
[16] Chen Y, Hyrien O, Williams J, et al.Interleukin(IL)‐1A and IL‐6:applications to the predictive diagnostic testing of radiation pneumonitis[J]. Int J Radiat Oncol Biol Phys, 2005,62(1): 260‐266.DOI: 10.1016/j.ijrobp.2005.01.041.
[17] Parashar B, Edwards A, Mehta R, et al.Chemotherapy significantly increases the risk of radiation pneumonitis in radiation therapy of advanced lung cancer[J]. Am J Clin Oncol, 2011,34(2): 160‐164.DOI: 10.1097/COC.0 b013e3181 d6 b40f.
[18] 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.
[19] Wang S, Campbell J, Stenmark MH, et al.Plasma levels of IL‐8 and TGF‐β1 predict radiation‐induced lung toxicity in non‐small cell lung cancer: a validation study[J]. Int J Radiat Oncol Biol Phys, 2017,98(3): 615‐621.DOI: 10.1016/j.ijrobp.2017.03.011.
[20] 张臻,赵路军,王伟,等.基于放射组学预测放射性肺炎的初步研究[J]. 中华放射肿瘤学杂志,2020,29(6): 427‐431.DOI: 10.3760/cma.j.cn113030‐20190225‐00063.
Zhang Z, Zhao LJ, Wang W, et al.Preliminary study of predicting radiation pneumonitis based on radiomics technology[J]. Chin J Radiat Oncol, 2020,29(6): 427‐431.DOI: 10.3760/cma.j.cn113030‐20190225‐00063.
[21] 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.
[22] Liang B, Tian Y, Chen X, et al.Prediction of radiation pneumonitis with dose distribution: a convolutional neural network(CNN)based model[J]. Front Oncol, 2019,9:1500.DOI: 10.3389/fonc.2019.01500.
[23] Bin L, Yuan T, Zhaohui S, et al.A deep learning‐based dual‐omics prediction model for radiation pneumonitis[J]. Med Phys, 2021,48(10): 6247‐6256.DOI: 10.1002/mp.15079.
[24] 张永,于甬华,刘希军,等.放射性肺炎临床相关因素的研究进展[J]. 中华放射肿瘤学杂志,2008,17(4): 325‐328.DOI: 10.3321/j.issn: 1004‐4221.2008.04.023.
Zhang Y, Yu YH, Liu XJ, et al.Research progress on clinical related factors of radiation pneumonia[J]. Chin J Radiat Oncol, 2008,17(4): 325‐328.DOI: 10.3321/j.issn: 1004‐4221.2008.04.023.
2022, Vol. 31 
