Although mathematical modelling and computational simulation have been applied in the field of cancer research for over five decades, representation of clinically relevant tumours remains an elusive goal. By “clinically relevant” is meant simulation of tumour conditions that reflect observable parameters in human patients, including growth, size, and biological characteristics. Yet a clinically relevant representation seems indicated to advance the goal of mathematical modelling and computational simulation providing insight that can complement clinical practice. With this goal in mind, we describe a parallelized framework that maintains intra-tumour cellular-scale data resolution while efficiently applying neural networks to extend the data to the tumour tissue-scale. Solely relying on computational advances to achieve a practically realistic mechanistic model implementation may be unrealistic given the inherent biological complexity of tumours. In contrast, using physically informed neural networks (PINN) would provide a smaller memory footprint but may struggle with dynamic treatment evaluation and obtaining physiologically relevant ranges for key parameters. Here, the proposed framework builds upon work to represent tumour growth in 3D space [1,2,3]. Input to this framework is a tumour shape that is restricted from a centimetre -scale to a 1283 domain. Simulations require a single GPU using a 1283 domain, with a neural network called upon to compute the correction up to a 5123 solution from the restricted form. Statistics are computed on each patch relating to tumour growth. Multiple simulations run in parallel on different GPUs enable dynamic evaluation of efficacy of therapeutic strategies. Upon clinical implementation, we envision that the proposed approach would enable personalized assessment of cancer treatment response.