An imaging-based tumour growth and treatment response model: investigating the effect of tumour oxygenation on radiation therapy response

Abstract

A multiscale tumour simulation model employing cell-line-specific biological parameters and functional information derived from pre-therapy PET/CT imaging data was developed to investigate effects of different oxygenation levels on the response to radiation therapy. For each tumour voxel, stochastic simulations were performed to model cellular growth and therapeutic response. Model parameters were fitted to published preclinical experiments of head and neck squamous cell carcinoma (HNSCC). Using the obtained parameters, the model was applied to a human HNSCC case to investigate effects of different uniform and non-uniform oxygenation levels and results were compared for treatment efficacy. Simulations of the preclinical studies showed excellent agreement with published data and underlined the model’s ability to quantitatively reproduce tumour behaviour within experimental uncertainties. When using a simplified transformation to derive non-uniform oxygenation levels from molecular imaging data, simulations of the clinical case showed heterogeneous tumour response and variability in radioresistance with decreasing oxygen levels. Once clinically validated, this model could be used to transform patient-specific data into voxel-based biological objectives for treatment planning and to investigate biologically optimized dose prescriptions.

Perfusion CT in Squamous Cell Carcinoma of the Upper Aerodigestive Tract: Long-Term Predictive Value of Baseline Perfusion CT Measurements

“…Reinforcing this evidence, the weak predictive value (P Ͻ .05) of the T stage for local tumor control in the oropharynx for patients treated with curative surgery or radiation therapy 24,25 and the weak or nonsignificant correlation between the T stage and perfusion-associated parameters 4,11,13 make it hard to predict only on the basis of T. This difficulty highlights the role of perfusion measurements in patients with neoadjuvant chemoradiation. [26][27][28] Finally, the perfusion estimations in our study did not have any predictive value for OS. This is in essential agreement with the results of Hermans et al 11 regarding regional control and cause-specific survival.…”

Inter-Relationship between Low-Dose Hyper-Radiosensitivity and Radiation-Induced Bystander Effects in the Human T98G Glioma and the Epithelial HaCaT Cell Line

“…where pO2(x) is the oxygen concentration at position x, OER is the ratio of the radiation doses needed for the same cell kill under anoxic and oxic conditions, OER m = 3 is the maximum ratio and K m = 3 mm Hg is the pO2 at half the increase from 1 to OER m [12,35]. The model also assumes that after a low dose exposure to irradiation (< 5Gy), about 50% of the DNA damage is likely to be repaired within a few hours, increasing the survival chances of the cells and hence the final survival probability is written as:…”

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

“…Patient-specific MRI data taken before and after the first cycle of neoadjuvant therapy in patients with breast cancer were coupled with a biomechanistic mathematical model to predict whether a patient would achieve a complete pathological response [155]. PET/CT imaging data from patients with head and neck carcinoma were used to inform a dynamic voxel-based model that predicted radiation therapy response under uniform and non-uniform tumour tissue oxygenation patterns [156]. PET/CT scans from patients with pancreatic tumours were used to predict tumour growth dynamics with a model using an elastic-growth decomposition technique to represent possible deformations of the pancreas [157].…”