PreprintCC BY-NC-SA 4.0HAL
A Review of Computational Modeling of Cancer Metastasis
Abstract
The metastatic disease is largely unobservable at the earliest stages of cancers: when dissemination occurred, how many occult lesions exist and which will grow cannot be directly measured. We review computational frameworks developed to reconstruct these hidden biological states (deterministic, stochastic, agent based, hybrid mechanistic models, and machine learning approaches), examining their key insights, identifiability and prospective validation as translation barriers, and the potential of emerging multimodal data to constrain next generation digital twins.
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BibTeX
@misc{zakrajsek2026review,
title = {{A Review of Computational Modeling of Cancer Metastasis}},
author = {Romain Zakrajsek and David Boulate and Sébastien Benzekry},
howpublished = {HAL},
year = {2026},
month = sep,
url = {https://inria.hal.science/hal-05752241v1},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - A Review of Computational Modeling of Cancer Metastasis
AU - Romain Zakrajsek
AU - David Boulate
AU - Sébastien Benzekry
PY - 2026
DA - 2026/09/16
PB - HAL
UR - https://inria.hal.science/hal-05752241v1
AB - The metastatic disease is largely unobservable at the earliest stages of cancers: when dissemination occurred, how many occult lesions exist and which will grow cannot be directly measured. We review computational frameworks developed to reconstruct these hidden biological states (deterministic, stochastic, agent based, hybrid mechanistic models, and machine learning approaches), examining their key insights, identifiability and prospective validation as translation barriers, and the potential of emerging multimodal data to constrain next generation digital twins.
ER -