# Ising energy model for the stochastic prediction of tumor islets

**Authors:** Lucas Amoudruz, Gregory Buti, Luciano Rivetti, Ali Ajdari, Gregory Sharp, Petros Koumoutsakos, Simon Spohn, Anca L Grosu, Thomas Bortfeld

arXiv: 2508.20804 · 2025-09-16

## TL;DR

This paper introduces an Ising energy model to predict tumor islet distribution in prostate cancer, capturing histological features and aiding clinical decision-making in treatment planning.

## Contribution

It develops a physically based, Ising-like model that accurately simulates tumor islet size, shape, and distribution based on clinical data.

## Key findings

- Model effectively describes tumor islet size and number
- Simulated islets are spherical, matching histology
- Model aids in calculating tumor involvement probability

## Abstract

A major challenge in diagnosing and treating cancer is the infiltrative growth of tumors into surrounding tissues.   This microscopic spread of the disease is invisible on most diagnostic imaging modalities and can often only be detected histologically in biopsies.   The purpose of this paper is to develop a physically based model of tumor spread that captures the histologically observed behavior in terms of seeding small tumor islets in prostate cancer.   The model is based on three elementary events: a tumor cell can move, duplicate, or die.   The propensity of each event is given by an Ising-like Hamiltonian that captures correlations between neighboring cells.   The model parameters were fitted to clinical data obtained from surgical specimens taken from 23 prostate cancer patients.   The results demonstrate that this straightforward physical model effectively describes the distribution of the size and the number of tumor islets in prostate cancer.   The simulated tumor islets exhibit a regular, approximately spherical shape, correctly mimicking the shapes observed in histology.   This is due to the Ising interaction term between neighboring cells acting as a surface tension that gives rise to regularly shaped islets.   The model addresses the important clinical need of calculating the probability of tumor involvement in specific sub-volumes of the prostate, which is required for radiation treatment planning and other applications.

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/2508.20804/full.md

## Figures

7 figures with captions in the complete paper: https://tomesphere.com/paper/2508.20804/full.md

## References

26 references — full list in the complete paper: https://tomesphere.com/paper/2508.20804/full.md

---
Source: https://tomesphere.com/paper/2508.20804