Abstract
The aim of this study is to develop a mathematical modelling method that
can predict individual patients’ response to radiotherapy, in terms of tumour
volume change during the treatment. The main concept is to start from a
population-average model, which is subsequently updated from an individual’s
tumour volume measurement. The model becomes increasingly personalised
and so too does the prediction it produces. This idea of adaptive prediction
was realised by using a Bayesian approach for updating the model parameters.
The feasibility of the developed method was demonstrated on the data from
25 non-small cell lung cancer patients treated with helical tomotherapy,
during which tumour volume was measured from daily imaging as part of
the image-guided radiotherapy. The method could provide useful information
for adaptive treatment planning and dose scheduling based on the patient’s
personalised response.
can predict individual patients’ response to radiotherapy, in terms of tumour
volume change during the treatment. The main concept is to start from a
population-average model, which is subsequently updated from an individual’s
tumour volume measurement. The model becomes increasingly personalised
and so too does the prediction it produces. This idea of adaptive prediction
was realised by using a Bayesian approach for updating the model parameters.
The feasibility of the developed method was demonstrated on the data from
25 non-small cell lung cancer patients treated with helical tomotherapy,
during which tumour volume was measured from daily imaging as part of
the image-guided radiotherapy. The method could provide useful information
for adaptive treatment planning and dose scheduling based on the patient’s
personalised response.
| Original language | English |
|---|---|
| Pages (from-to) | 2145-2161 |
| Journal | Physics in Medicine and Biology |
| Volume | 61 |
| DOIs | |
| Publication status | Published - 23 Feb 2016 |
Research Beacons, Institutes and Platforms
- Manchester Cancer Research Centre
