Thermal ablation is an increasingly utilized treatment modality for both secondary and primary hepatic tumors. However, it presents significant challenges in treatment planning, particularly when employing multiple applicators. Numerical methods for evaluating the effectiveness of an ablation procedure plan can assist in this task, but they are often computationally intensive or too simplistic, making them impractical for interaction or fast optimization loops in automatic planning. This paper introduces Chained Neural Cellular Automata (C-NCA), a deep learning approach that allows to quickly estimate cell death in thermal ablation procedures. The C-NCA model is trained on a dataset generated by a numerical simulation. When compared to existing methods, the C-NCA achieves comparable accuracy with substantially reduced computation time, thereby making it suitable for interactive planning, instant visualisation, fast automatic planning or even real-time surgical replanning, and potentially enhancing clinical workflows.