Applying physical science techniques and CERN technology to an unsolved problem in radiation treatment for cancer: the multidisciplinary 'VoxTox' research programme.

Q4 Decision Sciences
Neil G Burnet, Jessica E Scaife, Marina Romanchikova, Simon J Thomas, Amy M Bates, Emma Wong, David J Noble, Leila Ea Shelley, Simon J Bond, Julia R Forman, Andrew Cf Hoole, Gillian C Barnett, Frederic M Brochu, Michael Pd Simmons, Raj Jena, Karl Harrison, Ping Lin Yeap, Amelia Drew, Emma Silvester, Patrick Elwood, Hannah Pullen, Andrew Sultana, Shannon Yk Seah, Megan Z Wilson, Simon G Russell, Richard J Benson, Yvonne L Rimmer, Sarah J Jefferies, Nicolette Taku, Mark Gurnell, Andrew S Powlson, Carola-Bibiane Schönlieb, Xiaohao Cai, Michael Pf Sutcliffe, Michael A Parker
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引用次数: 17

Abstract

The VoxTox research programme has applied expertise from the physical sciences to the problem of radiotherapy toxicity, bringing together expertise from engineering, mathematics, high energy physics (including the Large Hadron Collider), medical physics and radiation oncology. In our initial cohort of 109 men treated with curative radiotherapy for prostate cancer, daily image guidance computed tomography (CT) scans have been used to calculate delivered dose to the rectum, as distinct from planned dose, using an automated approach. Clinical toxicity data have been collected, allowing us to address the hypothesis that delivered dose provides a better predictor of toxicity than planned dose.

Abstract Image

Abstract Image

将物理科学技术和欧洲核子研究中心技术应用于癌症放射治疗中尚未解决的问题:多学科“VoxTox”研究计划。
VoxTox研究计划将物理科学的专门知识应用于放射治疗毒性问题,汇集了来自工程、数学、高能物理学(包括大型强子对撞机)、医学物理学和放射肿瘤学的专门知识。在我们最初的109名接受根治性放射治疗的前列腺癌患者队列中,使用每日图像引导计算机断层扫描(CT)扫描来计算直肠递送剂量,与计划剂量不同,使用自动化方法。临床毒性数据已经收集,允许我们解决的假设,交付剂量提供了一个更好的预测毒性比计划剂量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CERN IdeaSquare Journal of Experimental Innovation
CERN IdeaSquare Journal of Experimental Innovation Business, Management and Accounting-Management of Technology and Innovation
CiteScore
1.70
自引率
0.00%
发文量
0
审稿时长
4 weeks
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