Physics-based tissue simulator to model multicellular systems: A study of liver regeneration and hepatocellular carcinoma recurrence

TL;DR

This paper introduces a multiagent-based, physics-inspired tissue simulator that models liver regeneration and cancer recurrence, matching clinical data and potentially aiding personalized treatment planning.

Contribution

The study presents a novel multiagent model that accurately reproduces tissue dynamics and tumor growth patterns, calibrated with patient-specific data for clinical relevance.

Findings

Successfully simulates liver regeneration post-hepatectomy

Predicts hepatocellular carcinoma recurrence with clinical accuracy

Aligns simulation outcomes with experimental and clinical data

Abstract

We present a multiagent-based model that captures the interactions between different types of cells with their microenvironment, and enables the analysis of the emergent global behavior during tissue regeneration and tumor development. Using this model, we are able to reproduce the temporal dynamics of regular healthy cells and cancer cells, as well as the evolution of their three-dimensional spatial distributions. By tuning the system with the characteristics of the individual patients, our model reproduces a variety of spatial patterns of tissue regeneration and tumor growth, resembling those found in clinical imaging or biopsies. In order to calibrate and validate our model we study the process of liver regeneration after surgical hepatectomy in different degrees. In the clinical context, our model is able to predict the recurrence of a hepatocellular carcinoma after a 70% partial hepatectomy. The outcomes of our simulations are in agreement with experimental and clinical observations. By fitting the model parameters to specific patient factors, it might well become a useful platform for hypotheses testing in treatments protocols.