An imaging-based computational model for simulating angiogenesis and tumour oxygenation dynamics

Abstract

Tumour growth, angiogenesis and oxygenation vary substantially among tumours and significantly impact their treatment outcome. Imaging provides a unique means of investigating these tumour-specific characteristics. Here we propose a computational model to simulate tumour-specific oxygenation changes based on the molecular imaging data. Tumour oxygenation in the model is reflected by the perfused vessel density. Tumour growth depends on its doubling time (Td) and the imaged proliferation. Perfused vessel density recruitment rate depends on the perfused vessel density around the tumour (sMVDtissue) and the maximum VEGF concentration for complete vessel dysfunctionality (VEGFmax). The model parameters were benchmarked to reproduce the dynamics of tumour oxygenation over its entire lifecycle, which is the most challenging test. Tumour oxygenation dynamics were quantified using the peak pO2 (pO2peak) and the time to peak pO2 (tpeak). Sensitivity of tumour oxygenation to model parameters was assessed by changing each parameter by 20%. tpeak was found to be more sensitive to tumour cell line related doubling time (~30%) as compared to tissue vasculature density (~10%). On the other hand, pO2peak was found to be similarly influenced by the above tumour- and vasculature-associated parameters (~30–40%). Interestingly, both pO2peak and tpeak were only marginally affected by VEGFmax (~5%). The development of a poorly oxygenated (hypoxic) core with tumour growth increased VEGF accumulation, thus disrupting the vessel perfusion as well as further increasing hypoxia with time. The model with its benchmarked parameters, is applied to hypoxia imaging data obtained using a [64Cu]Cu-ATSM PET scan of a mouse tumour and the temporal development of the vasculature and hypoxia maps are shown. The work underscores the importance of using tumour-specific input for analysing tumour evolution. An extended model incorporating therapeutic effects can serve as a powerful tool for analysing tumour response to anti-angiogenic therapies.

Anti-angiogenic therapy in ovarian cancer: Current understandings and prospects of precision medicine

“…An integrated model of VEGF-Ang-2 cooperation that accurately recapitulates molecular events constituting the angiogenic switch was proposed in ovarian cancer ( Zhang et al, 2003 ). Adhikarla et al established a computational model to simulate tumor-specific oxygenation changes based on the molecular image data, which incorporating therapeutic effects might serve as a powerful tool for analyzing tumor response to anti-angiogenic therapies ( Adhikarla and Jeraj, 2016 ). Models combining biomarkers with other risk factors are also constructed to predict treatment outcomes of anti-angiogenic agents in OC.…”

Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data

“…Adhikarla et al [ 83 , 84 ] developed a modeling workflow based on ordinary differential equations to simulate temporal changes in tumor vasculature and blood oxygenation. The microvasculature was initialized with micro-CT imaging, the tumor oxygenation status was calibrated with PET imaging data sensitive to hypoxia, and tumor growth was characterized by proliferation estimated from PET imaging data.…”

Towards a Reduced In Silico Model Predicting Biochemical Recurrence After Radiotherapy in Prostate Cancer

“…Using longitudinal mpMRI [68], CBCT [24] and PET images [32] of irradiated patients, tumor volumes and cell densities can be obtained and compared with the results of in silico simulations. Additionally, PET images can be used to validate the oxygenation of the tissue [69], [70].…”