Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.20804 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916949520285696 |
|---|---|
| author | Amoudruz, Lucas Buti, Gregory Rivetti, Luciano Ajdari, Ali Sharp, Gregory Koumoutsakos, Petros Spohn, Simon Grosu, Anca L Bortfeld, Thomas |
| author_facet | Amoudruz, Lucas Buti, Gregory Rivetti, Luciano Ajdari, Ali Sharp, Gregory Koumoutsakos, Petros Spohn, Simon Grosu, Anca L Bortfeld, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20804 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ising energy model for the stochastic prediction of tumor islets Amoudruz, Lucas Buti, Gregory Rivetti, Luciano Ajdari, Ali Sharp, Gregory Koumoutsakos, Petros Spohn, Simon Grosu, Anca L Bortfeld, Thomas Medical Physics Computational Physics Quantitative Methods 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. |
| title | Ising energy model for the stochastic prediction of tumor islets |
| topic | Medical Physics Computational Physics Quantitative Methods |
| url | https://arxiv.org/abs/2508.20804 |