Establishment of the NTCP model for radiation-induced lung injury in patients with non-small cell lung cancer / 中华放射肿瘤学杂志
Chinese Journal of Radiation Oncology
; (6): 1376-1380, 2017.
Article
en Zh
| WPRIM
| ID: wpr-663824
Biblioteca responsable:
WPRO
ABSTRACT
Objective To examine the incidence of radiation-induced lung injury(RILI)after involved-field intensity-modulated radiation therapy(IMRT)in patients with locally advanced non-small cell lung cancer(NSCLC),and to evaluate the predictability of different models.Methods The clinical data of 242 inoperable or unresectable stage Ⅲ NSCLC patients treated in our hospital from 2007 to 2011 were reviewed. Grade 2 and grade 3 RILI that occurred within 6 months after IMRT were selected as outcome events in this study. The principal component analysis(PCA)model,Lyman-Kutcher-Burman(LKB)model,and mean lung dose(MLD)model were each used to establish a predictive model of normal tissue complication probability (NTCP)for evaluating the dosimetric parameters of IMRT. Results Four principal components were used in the PCA model. The areas under the receiver operating characteristic curve(AUCs)of grade 2 and grade 3 RILI were 0.652 and 0.611,respectively. For the LKB model, the fitted parameters were m=0.46, n=1.35, and D50=23.59 Gy for grade 2 RILI,and m=0.36,n=0.27,and D50=72.67 Gy for grade 3 RILI. The AUCs of grade 2 and grade 3 RILI in the LKB model were 0.607 and 0.585, respectively. For the MLD model, the estimated parameters were γ50=1.073 and D50=24.66 Gy for grade 2 RILI,and γ50=0.97 and D50=48.45 Gy for grade 3 RILI.The AUCs of grade 2 and grade 3 RILI in the MLD model were 0.604 and 0.569,respectively. Conclusions The use of large data set from a single patient population with the same mode of treatment is very important for improving model predictability and stability. Both the LKB model and PCA model can predict the probability of RILI,whereas the MLD model is less effective in predicting grade 3 RILI.
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Banco de datos:
WPRIM
Tipo de estudio:
Prognostic_studies
Idioma:
Zh
Año:
2017
Tipo del documento:
Article