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



Alirezaei Z1 ; Amouheidari A2 ; Iraji S3 ; Hassanpour M4 ; Hejazi SH5 ; Davanian F6 ; Nami MT7 ; Rastaghi S8 ; Shokrani P1 ; Tsien CI9 ; Nazemzadeh MR3, 4
Authors
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Authors Affiliations
  1. 1. Medical Physics Department, Isfahan University of Medical Science, Isfahan, Iran
  2. 2. Research & Education, Department of Radiation Oncology, Isfahan Milad Hospital, Isfahan, Iran
  3. 3. Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. Skin Diseases and Leishmaniosis Research Center, Department of Parasitology and Mycology, School of Medicine, Isfahan University of Medical Science, Isfahan, Iran
  6. 6. Radiology Department, School of Medicine, Isfahan University of Medical Science, Isfahan, Iran
  7. 7. Dana Health Center, Shiraz University of Medical Sciences, Shiraz, Iran
  8. 8. Biostatistics Department, Mashhad University of Medical Sciences, Mashhad, Iran
  9. 9. Radiation Oncology Department, Washington University, St. Louis, MO, United States

Source: Journal of Molecular Neuroscience Published:2023


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. Fifteen patients with primary brain tumors prospectively participated in this study and underwent radiation therapy. Magnetic resonance imaging (MRI) was 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 XLIIIrd Sir Peter Freyer Memorial Lecture and Surgical Symposium (2018). Changes in AD, RD, and S100B from baseline up to the 6 months after treatment had an increasing trend and were significant in some time points (P-value < 0.05). The model resulting from RD changes in the 6 months after treatment was significantly 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. Changes 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 findings 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. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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Mohammadreza Nazemzadeh (1)