biostudies-literatureHormuth DANCI NIH HHSNINDS NIH HHS1270-1279https://www.ebi.ac.uk/biostudies/studies/S-EPMC5934308biostudies-literatureUnknown100(5)<h4>Purpose</h4>To develop and investigate a set of biophysical models based on a mechanically coupled reaction-diffusion model of the spatiotemporal evolution of tumor growth after radiation therapy.<h4>Methods and materials</h4>Post-radiation therapy response is modeled using a cell death model (M_d), a reduced proliferation rate model (M_p), and cell death and reduced proliferation model (M_dp). To evaluate each model, rats (n = 12) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging (MRI) and contrast-enhanced MRI at 7 time points over 2 weeks. Rats received either 20 or 40 Gy between the third and fourth imaging time point. Diffusion-weighted MRI was used to estimate tumor cell number within enhancing regions in contrast-enhanced MRI data. Each model was fit to the spatiotemporal evolution of tumor cell number from time point 1 to time point 5 to estimate model parameters. The estimated model parameters were then used to predict tumor growth at the final 2 imaging time points. The model prediction was evaluated by calculating the error in tumor volume estimates, average surface distance, and voxel-based cell number.<h4>Results</h4>For both the rats treated with either 20 or 40 Gy, significantly lower error in tumor volume, average surface distance, and voxel-based cell number was observed for the M_dp and M_p models compared with the M_d model. The M_dp model fit, however, had significantly lower sum squared error compared with the M_p and M_d models.<h4>Conclusions</h4>The results of this study indicate that for both doses, the M_p and M_dp models result in accurate predictions of tumor growth, whereas the M_d model poorly describes response to radiation therapy.International journal of radiation oncology, biology, physicsBiophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer.PMC5934308U01 CA142565R01 CA186193R25 CA092043K25 CA204599R01 CA138599P30 CA068485R21 CA169387U01 CA174706R01 NS049251Miga MIQuaranta VBarnes SLWeis JAHormuth DAYankeelov TEfalseBiophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer.<h4>Purpose</h4>To develop and investigate a set of biophysical models based on a mechanically coupled reaction-diffusion model of the spatiotemporal evolution of tumor growth after radiation therapy.<h4>Methods and materials</h4>Post-radiation therapy response is modeled using a cell death model (M_d), a reduced proliferation rate model (M_p), and cell death and reduced proliferation model (M_dp). To evaluate each model, rats (n = 12) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging (MRI) and contrast-enhanced MRI at 7 time points over 2 weeks. Rats received either 20 or 40 Gy between the third and fourth imaging time point. Diffusion-weighted MRI was used to estimate tumor cell number within enhancing regions in contrast-enhanced MRI data. Each model was fit to the spatiotemporal evolution of tumor cell number from time point 1 to time point 5 to estimate model parameters. The estimated model parameters were then used to predict tumor growth at the final 2 imaging time points. The model prediction was evaluated by calculating the error in tumor volume estimates, average surface distance, and voxel-based cell number.<h4>Results</h4>For both the rats treated with either 20 or 40 Gy, significantly lower error in tumor volume, average surface distance, and voxel-based cell number was observed for the M_dp and M_p models compared with the M_d model. The M_dp model fit, however, had significantly lower sum squared error compared with the M_p and M_d models.<h4>Conclusions</h4>The results of this study indicate that for both doses, the M_p and M_dp models result in accurate predictions of tumor growth, whereas the M_d model poorly describes response to radiation therapy.2018-01-01T00:00:00Z2018 Apr2024-11-09T09:46:52.686Z2019-08-04T07:30:55ZS-EPMC59343082939812910.1016/j.ijrobp.2017.12.004