Identifying radiotherapy target volumes in brain cancer by image analysis

Healthcare Technology Letters

article

Kun Cheng¹  Dean Montgomery¹  Yang Feng¹  Robin Steel¹  Hanqing Liao²  Duncan B. McLaren³  Sara C. Erridge³  Stephen McLaughlin⁴  William H. Nailon¹ 

[1] Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital;Department of Electrical Engineering and Electronics, University of Liverpool;Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital;School of Engineering and Physical Sciences, Heriot Watt University;School of Engineering, University of Edinburgh

关键词: radiation therapy;    biomedical MRI;    medical image processing;    brain;    cancer;    tumours;    image sequences;    computerised tomography;    radiotherapy target volumes;    brain cancer;    optimal radiotherapy fields;    brain cancer patients;    tumour volume;    magnetic resonance images;    MRI;    computerised tomography images;    radiotherapy planning;    complex image sequences;    image analysis techniques;    gradient-based level set approach;    malignant cerebral glioma;    radiation oncologist;    oncologist;    Dice similarity coefficient;    automatic outlining;   

DOI  :  10.1049/htl.2015.0014

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

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.

【 授权许可】

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