TY - JOUR
T1 - Identifying radiotherapy target volumes in brain cancer by image analysis
AU - Cheng, Kun
AU - Montgomery, Dean
AU - Feng, Yang
AU - Steel, Robin
AU - Liao, Hanqing
AU - McLaren, Duncan B.
AU - Erridge, Sara C.
AU - McLaughlin, Stephen
AU - Nailon, William H.
N1 - Publisher Copyright:
© The Institution of Engineering and Technology 2015.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2015/10
Y1 - 2015/10
N2 - To establish the optimal radiotherapy fields for treating brain cancer patients, the tumour volume is often outlined on magnetic resonance (MR) images, where the tumour is clearly visible, and mapped onto computerised tomography images used for radiotherapy planning. This process requires considerable clinical experience and is time consuming, which will continue to increase as more complex image sequences are used in this process. Here, the potential of image analysis techniques for automatically identifying the radiation target volume on MR images, and thereby assisting clinicians with this difficult task, was investigated. A gradient-based level set approach was applied on the MR images of five patients with grades II, III and IV malignant cerebral glioma. The relationship between the target volumes produced by image analysis and those produced by a radiation oncologist was also investigated. The contours produced by image analysis were compared with the contours produced by an oncologist and used for treatment. In 93% of cases, the Dice similarity coefficient was found to be between 60 and 80%. This feasibility study demonstrates that image analysis has the potential for automatic outlining in the management of brain cancer patients, however, more testing and validation on a much larger patient cohort is required.
AB - To establish the optimal radiotherapy fields for treating brain cancer patients, the tumour volume is often outlined on magnetic resonance (MR) images, where the tumour is clearly visible, and mapped onto computerised tomography images used for radiotherapy planning. This process requires considerable clinical experience and is time consuming, which will continue to increase as more complex image sequences are used in this process. Here, the potential of image analysis techniques for automatically identifying the radiation target volume on MR images, and thereby assisting clinicians with this difficult task, was investigated. A gradient-based level set approach was applied on the MR images of five patients with grades II, III and IV malignant cerebral glioma. The relationship between the target volumes produced by image analysis and those produced by a radiation oncologist was also investigated. The contours produced by image analysis were compared with the contours produced by an oncologist and used for treatment. In 93% of cases, the Dice similarity coefficient was found to be between 60 and 80%. This feasibility study demonstrates that image analysis has the potential for automatic outlining in the management of brain cancer patients, however, more testing and validation on a much larger patient cohort is required.
UR - http://www.scopus.com/inward/record.url?scp=85041814245&partnerID=8YFLogxK
U2 - 10.1049/htl.2015.0014
DO - 10.1049/htl.2015.0014
M3 - Article
AN - SCOPUS:85041814245
SN - 2053-3713
VL - 2
SP - 123
EP - 128
JO - Healthcare Technology Letters
JF - Healthcare Technology Letters
IS - 5
ER -