# From images to physics-based computational models to digital twins: a framework for personalized cancer therapies

## TL;DR

This paper presents a framework using computational models and medical images to improve personalized cancer treatments.

## Contribution

The novel framework integrates patient-specific data and machine learning to develop digital twins for cancer therapies.

## Key findings

- Multi-physics models simulate drug interactions in the tumor microenvironment.
- Machine learning improves model accuracy and enables real-time treatment planning.

## Abstract

In this work, we highlight recent advances in computational modeling that have significantly enhanced prospects of personalized cancer therapies by enabling insightful integration of patient-specific data, including medical images. Computational models, encompassing multi-physics and multi-scale approaches, can simulate drug transport and interactions within tissues and environments, including the tumor microenvironment, and facilitate the development of targeted diagnostic and therapeutic strategies. The incorporation of machine learning algorithms has further refined modeling, improving predictive accuracy and enabling real-time adaptive treatment planning. Although challenges remain in model validation and clinical translation, ongoing advancements are steadily bridging these gaps, bringing computational models and technologies closer to routine clinical application for the improvement of patient outcomes.