Prognostic variables for temporal lobe injury after intensity modulated-radiotherapy of nasopharyngeal carcinoma

Abstract

To determine predictive factors for temporal lobe injury (TLI) in nasopharyngeal carcinoma patient (NPC) treated with intensity-modulated radiation therapy (IMRT). A total of 695 NPC cases treated with IMRT were retrospectively analyzed. TLI was diagnosed on MRI images. Volume-dose histograms for 870 evaluable temporal lobes were analyzed, and the predictive factors for the occurrence of TLI was evaluated. Receiver operating characteristic curve (ROC) and Logistic regression analysis was used to determine volume-dose parameters that predict temporal lobe injury (TLI). Univariate and multivariate analysis were used to analyze the predictive factors for TLI. The radiation dose-tolerance model of temporal lobe was calculated by logistic dose-response model. The median follow-up time was 73 months. A total of 8.5% patients were diagnosed with TLI. Among all the volume-dose parameters, logistic regression model showed D2cc (the dose Gray delivered to 2 cubic centimeter volume) was an only independent predictive factor. Multivariate analysis showed D2cc of temporal lobe, fraction size of prescription, T stage, and chemotherapy were the independent predictive factors for TLI. Logistic dose-response model has indicated the TD_5/5 and TD_50/5 of D2cc are 60.3 Gy and 76.9 Gy, respectively. D2cc of temporal lobe, fraction size of prescription, T stage, and chemotherapy were the possible independent predictive factors for TLI after IMRT of NPC. Biologic effective doses (TD_5/5 and TD_50/5) of D2cc are considered to prevent TLI.

A total of 695 NPC cases treated with IMRT were retrospectively analyzed with the radiation-induced temporal lobe injury on MRI images. D2cc of temporal lobe, fraction size of prescription, T stage, and chemotherapy were the possible independent predictive factors for TLI after IMRT of NPC. Biologic effective doses (TD5/5 and TD50/5) of D2cc are considered to prevent TLI.

from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/2EJDitj
via IFTTT