A radiobiological model of radiotherapy response and its correlation with prognostic imaging variables

Abstract

Radiobiological models of tumour control probability (TCP) can be personalized using imaging data. We propose an extension to a voxel-level radiobiological TCP model in order to describe patient-specific differences and intra-tumour heterogeneity. In the proposed model, tumour shrinkage is described by means of a novel kinetic Monte Carlo method for inter-voxel cell migration and tumour deformation. The model captures the spatiotemporal evolution of the tumour at the voxel level, and is designed to take imaging data as input. To test the performance of the model, three image-derived variables found to be predictive of outcome in the literature have been identified and calculated using the model’s own parameters. Simulating multiple tumours with different initial conditions makes it possible to perform an in silico study of the correlation of these variables with the dose for 50% tumour control (TCD50) calculated by the model. We find that the three simulated variables correlate with the calculated TCD50. In addition, we find that different variables have different levels of sensitivity to the spatial distribution of hypoxia within the tumour, as well as to the dynamics of the migration mechanism. Finally, based on our results, we observe that an adequate combination of the variables may potentially result in higher predictive power.

Quantifying Reoxygenation in Pancreatic Cancer During Stereotactic Body Radiotherapy

“…At the same time, hypoxia varies greatly amongst pancreatic cancer patients 8 , meaning that, accounting for the enhanced radio-resistance of hypoxia cells via the oxygen enhancement ratio (OER)-the true effective dose may in some cases be much less than expected, conferring a survival advantage to hypoxic-and possibly more malignant 20,[24][25][26][27] -cells. In order to accurately model the response of pancreatic tumours to hypofractionated radiotherapy and hypoxia-activated prodrugs 14,16,17,28 , and to develop strategies to improve survival, it will be important to accurately quantify hypoxia and reoxygenation during the short course of SBRT 7 .…”

“…The same assumption has been made in the radiotherapy response modeling of Jeong et al 17 (see also ref. 28 ), albeit with two hypoxic compartments corresponding to different metabolic sub-levels.…”

Validating a Predictive Atlas of Tumor Shrinkage for Adaptive Radiotherapy of Locally Advanced Lung Cancer

“…Additional metabolic information acquired from pre- and midtreatment PET scans may help us detect outliers because PET focuses on tumor characteristics of metabolism other than geometry. We are developing a PET/CT-based biological model to study the trajectory of tumor response throughout radiation therapy (20, 21). By combining our geometrical atlas with the PET-based model, we might be able to improve the predictive accuracy and dose escalate more effectively to relevant parts of the tumor.…”

Radiotherapy Side Effects: Comprehensive Proteomic Study Unraveled Neural Stem Cell Degenerative Differentiation upon Ionizing Radiation

“…Elucidating the mechanism underlying irradiation-mediated NSC injury would contribute to alleviating the side effects of radiotherapy and demystifying the induction of neural inflammation, brain development, and even neurodegeneration-associated mechanisms [ 43 , 44 , 45 ]. The substitution models employed in preclinical radiation research vary from cultured cells to small or large animals [ 46 , 47 ]; the majority of these models have been established according to the linear quadratic (LQ) model [ 48 ]. Currently, the emergence of 3D tissue models and organoids has been beneficial in understanding radiation-induced tissue response and in precision medicine [ 49 , 50 ].…”