KONG, Vickie, TAYLOR, Amy and ROSEWALL, Tara (2016). Adaptive radiotherapy for bladder cancer – a systematic review. Journal of Medical Imaging and Radiation Sciences, 48 (2), 199-206. [Article]
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14339:94067
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Taylor - Kong et al - Adaptive radiotherapy for bladder cancer (AM).pdf - Accepted Version
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Taylor - Kong et al - Adaptive radiotherapy for bladder cancer (AM).pdf - Accepted Version
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14339:94068
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Taylor - Kong et al - Adaptive radiotherapy for bladder cancer (figure 1).pdf - Supplemental Material
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Taylor - Kong et al - Adaptive radiotherapy for bladder cancer (figure 1).pdf - Supplemental Material
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Taylor - Kong et al - Adaptive radiotherapy for bladder cancer (table 1).pdf - Supplemental Material
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Taylor - Kong et al - Adaptive radiotherapy for bladder cancer (table 1).pdf - Supplemental Material
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Abstract
Radiotherapy has been offered as a multi-modality treatment for bladder cancer patients. Due to the significant variation of bladder volume observed throughout the course of treatment, large margins in the range of 20 – 30 mm have been used, unnecessarily irradiating a large volume of normal tissue. With the capability of visualizing soft tissue in Cone Beam Computerized Tomography, there is opportunity to modify or to adapt the plan based on the variation observed during the course of treatment for quality improvement. A literature search was conducted in May 2016, with the aim of examining the adaptive strategies that have been developed for bladder cancer and assessing the efficacy in improving treatment quality. Among the 18 identified publications, three adaptive strategies were reported: Plan of the Day, patient-specific planning target volume and daily reoptimization. Overall, any of the adaptive strategies achieved a significant improvement in reducing the irradiated volume compared to the non-adaptive approach, outweighing the additional resource required for its execution. The amount and the type of resource required vary from strategy to strategy, suggesting the need for the individual institution to assess feasibility based on the existing infrastructure in order to identify the most appropriate strategy for implementation.
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