Séna Apeke, Laurent Gaubert, Nicolas Boussion,
Philippe Lambin, Dimistris Visvikis, Vincent Rodin and
Pascal Redou.
Multi-scale modeling and oxygen impact on tumoral temporal evolution:
Application on rectal cancer during radiotherapy.
IEEE Transactions on Medical Imaging (TMI), IEEE,
37(4):871-880, April 2018.
Abstract:
We present a multi-scale approach of tumor modelling in order to predict
its evolution during radiotherapy. Within this context we focus on three
different scales of tumor modelling: microscopic (individual cells in a voxel),
mesoscopic (population of cells in a voxel) and macroscopic (whole tumor),
with transition interfaces between these three scales.
At the cellular level, the description is based on phase transfer
probabilities in the cellular cycle.
At the mesoscopic scale we represent populations of cells according to
different stages in a cell cycle.
Finally, at the macroscopic scale, the tumor description is based on the use
of FDG PET image voxels.
These three scales exist naturally: biological data are collected at the
macroscopic scale, but the pathological behaviour of the tumor is based on
an abnormal cell-cycle at the microscopic scale.
On the other hand, the introduction of a mesoscopic scale is essential in
order to reduce the gap between the two extreme, in terms of resolution,
description levels.
It also reduces the computational burden of simulating a large number of
individual cells.
As an application of the proposed multi-scale model, we simulate the effect
of oxygen on tumor evolution during radiotherapy.
Two consecutive FDG PET images of 17 rectal cancer patients undergoing
radiotherapy are used to simulate the tumor evolution during treatment.
The simulated results are compared to those obtained on a third FDG PET
image acquired two weeks after the beginning of the treatment.
Keywords:
Model, tumor, treatment response, discrete, stochastic, FDG PET image,
microscopic, mesoscopic, macroscopic, simulation.
[doi:10.1109/TMI.2017.2771379]
[Apeke18a.pdf]