# Neural parameters estimation for brain tumor growth modeling

## TL;DR

This paper introduces a learning-based method to estimate brain tumor growth model parameters from medical images, enabling personalized predictions with fewer samples and explicit posterior evaluation.

## Contribution

It presents a novel approach using a mixture-density network to estimate tumor growth parameters, relaxing functional form constraints and reducing sampling requirements.

## Key findings

- Effective on synthetic and real rat brain tumor scans
- Allows explicit posterior distribution evaluation
- Reduces number of samples needed for model calibration

## Abstract

Understanding the dynamics of brain tumor progression is essential for optimal treatment planning. Cast in a mathematical formulation, it is typically viewed as evaluation of a system of partial differential equations, wherein the physiological processes that govern the growth of the tumor are considered. To personalize the model, i.e. find a relevant set of parameters, with respect to the tumor dynamics of a particular patient, the model is informed from empirical data, e.g., medical images obtained from diagnostic modalities, such as magnetic-resonance imaging. Existing model-observation coupling schemes require a large number of forward integrations of the biophysical model and rely on simplifying assumption on the functional form, linking the output of the model with the image information. In this work, we propose a learning-based technique for the estimation of tumor growth model parameters from medical scans. The technique allows for explicit evaluation of the posterior distribution of the parameters by sequentially training a mixture-density network, relaxing the constraint on the functional form and reducing the number of samples necessary to propagate through the forward model for the estimation. We test the method on synthetic and real scans of rats injected with brain tumors to calibrate the model and to predict tumor progression.