Prediction of Normal Tissue Complication Probability (NTCP) After Radiation Therapy Using Imaging and Molecular Biomarkers and Multivariate Modelling

Abstract

The aim of this study was to design a predictive radiobiological model of normal brain tissue in low-grade glioma following radiotherapy based on imaging and molecular biomarkers. MethodsFifteen patients with primary brain tumors prospectively participated in this study and underwent radiation therapy. Magnetic resonance imaging (MRI) were obtained from the patients, including T1 and T2 weighted imaging and diffusion tensor imaging (DTI), and a generalized equivalent dose (gEUD) was calculated. The radiobiological model of the normal tissue complication probability (NTCP) was performed using the variables gEUD; axial diffusivity (AD) and radial diffusivity (RD) of the corpus callosum; and serum protein S100B by univariate and multivariate logistic regression. ResultsChanges in AD, RD, and S100B from baseline up to the six months after treatment had an increasing trend and were signi cant in some time points (P-Value < 0.05). The model resulting from RD changes in the six months after treatment was signi cantly more predictable of necrosis than other univariate models. The bivariate model combining RD changes in Gy40 dose-volume and gEUD, as well as the trivariate model obtained using gEUD, RD and S100B, had a higher predictive value among multivariate models at the sixth month of the treatment. ConclusionChanges in RD diffusion indices and in serum protein S100B value were used in the early delayed stage as reliable biomarkers for predicting late-delayed damage (necrosis) caused by radiation in the corpus callosum. Current ndings could pave the way for intervention therapies to delay the severity of damage to white matter structures, minimize cognitive impairment, and improve the quality of life of patients with low-grade glioma.