Machine Learning for Automatic Prediction of Tumour
Growth from CT Images
1.
Background for Image Guided Radiotherapy
Radiation treatment plans for the
treatment of cancers are designed primarily from CT datasets. The tumour within
the patient is imaged using a cross-sectional scanner (CT) creating a
three-dimensional electron density map (Figure 1). The radiation oncologist,
using fused magnetic resonance imaging (MRI) and positron emission tomography
(PET) scans as well as clinical information creates a gross tumour volume (GTV
outlined in red) (Figure 2).
Figure
1
Figure 2
Using geometric expansion tools, a
margin around the GTV is created and modified according to the likelihood of
tumour involvement by microscopic disease. This region is called the Clinical
Target Volume (CTV outlined in blue in Figure 3). The physician must judge the
probability that the tumour will spread into surrounding regions, which depends
on the density of surrounding objects. For example, it is more difficult for a
tumour to traverse bone than fat. The creation of
the CTV is time consuming and would require several hours of contouring to
create repeated treatment plans during a course of treatment as well as the
original initial treatment plan.
Figure 3:
Planning CT scan with GTV (red), CTV (blue) and isodose
lines from conventional radiotherapy beam arrangement.
The density of tissue is proportional to
proton density, which in turn is proportional to electron density. Higher
molecular structures such as calcium containing bone are denser than fat which
contains a higher proportion of water on a CT scan (e.g. bone is white and air
is black with fat being grey).
The aim of modern radiotherapy is to
treat the tumour yet causing the least amount of damage to the surrounding
normal structures. Adapting the treatment and reducing the clinical target
volume may reduce the dose that surrounding tissues receive, allowing sparing
of normal tissue functions such as saliva production in the treatment of head
and neck cancers. With the introduction of image-guided radiotherapy, images of
the patient in their treatment position are recorded intermittently during their
treatment. Responding tumours shrink during their treatment and so the original
tumour size and position change with respect to their original status. Their
relationship to surrounding structures also changes temporally.
This daily adaptation of the GTV and CTV
is time consuming and would rely upon daily physician adjustment of the
contoured tissue. Automation of this process would increase the feasibility of
DART (Dynamic adaptive radiotherapy).
2. Summary and Research Objectives
The aim of modern radiotherapy is to
treat the tumour whilst causing the least amount of damage to the surrounding
normal structures. The region for tumour treatment is called the Clinical
Target Volume (CTV), which is a drawn manually using CT scan images. The creation of the CTV is very time
consuming and requires several hours of manual contouring to create an initial
treatment plan.
Adaptive radiotherapy, in which the
treated region is modified as treatment progresses, remains the holy grail of
personalised radiotherapy, but as yet is out of reach of daily practice because
of the extensive cost of repeated manual definition of the CTV. The recent development of machine
learning and the availability of powerful computers hold great promise that
this research question can be addressed systematically.
The purpose of this project is to develop and assess novel machine learning algorithms to automate the creation of CTVs. This project will initiate a collaborative group comprising a clinical oncologist from Royal Devon and Exeter Hospital and computer scientists from the University of Exeter, with aims to develop intelligent machine learning algorithms for personalised radiotherapy. In summary, the main objectives of the project are:
· Developing image
segmentation algorithms to automatically detect tumour regions from CT images.
· Designing
dynamic and sequential machine learning algorithms to model tumour growth from
CT images and therefore automatically produce CTVs.
· Validating the
proposed methods using the available CTVs created by radiation oncologists.
People: David Hwang (Royal
Devon Hospital)
Yiming Ying (Exeter University)
Richard Everson (Exeter University)
Return to Yiming Ying's home page