A Multi-Compartment Model of Glioma Response to Fractionated Radiation Therapy Parameterized via Time-Resolved Microscopy Data
Year: 2022
Type: article
Abstract: PURPOSE: Conventional radiobiology models, including the linear-quadratic model, do not explicitly account for the temporal effects of radiation, thereby making it difficult to make time-resolved predictions of tumor response to fractionated radiation. To overcome this limitation, we propose and validate an experimental-computational approach that predicts the changes in cell number over time in response to fractionated radiation. METHODS: time-resolved microscopy. Phase-contrast images and Cytotox Red images (to label dead cells) were collected every 4 to 6 hours up to 330 hours post-radiation. Using 75% of the total data (i.e., 262 9L curves and 211 C6 curves), we calibrated a two-species model describing proliferative and senescent cells. We then applied the calibrated parameters to a validation dataset (the remaining 25% of the data, i.e., 91 9L curves and 74 C6 curves) to predict radiation response. Model predictions were compared to the microscopy measurements using the Pearson correlation coefficient (PCC) and the concordance correlation coefficient (CCC). RESULTS: For the 9L cells, we observed PCCs and CCCs between the model predictions and validation data of (mean ± standard error) 0.96 ± 0.007 and 0.88 ± 0.013, respectively, across all fractionation schemes. For the C6 cells, we observed PCCs and CCCs between model predictions and the validation data were 0.89 ± 0.008 and 0.75 ± 0.017, respectively, across all fractionation schemes. CONCLUSION: , this study is the first to establish a framework for quantitative characterization and prediction of the dynamic radiobiological response of 9L and C6 gliomas to fractionated radiotherapy.
Source: Frontiers in Oncology
Authors: Junyan Liu, David A. Hormuth, Jianchen Yang, Thomas E. Yankeelov
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,950
Awards: G1361462235, G1424053875, Quantitative Multiscale Imaging to Optimize Cancer Treatment Strategies, Clinical Development of Rhenium Nanoliposomes (RNL186) for Glioblastoma, Systems Approaches to Understanding Subpopulation Heterogeneity in Therapeutic Resistance, G5179685801, Recruitment of Established Investigators, Personalizing immunotherapy in HER2+ breast cancer through quantitative imaging, G8141834623
OpenAlex ID: W4210658288