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首页> 外文期刊>Radiotherapy and oncology: Journal of the European Society for Therapeutic Radiology and Oncology >Multi-modality functional image guided dose escalation in the presence of uncertainties
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Multi-modality functional image guided dose escalation in the presence of uncertainties

机译：多模态功能图像引导剂量升级在存在不确定因素

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Background and purpose In order to increase local tumour control by radiotherapy without increasing toxicity, it appears promising to harness functional imaging (FI) to guide dose to sub-volumes of the target with a high tumour load and perhaps de-escalate dose to low risk volumes, in order to maximise the efficiency of the deposited radiation dose. Methods and materials A number of problems have to be solved to make focal dose escalation (FDE) efficient and safe: (1) how to combine ambiguous information from multiple imaging modalities; (2) how to take into account uncertainties of FI based tissue classification; (3) how to account for geometric uncertainties in treatment delivery; (4) how to add complementary FI modalities to an existing scheme. A generic optimisation concept addresses these points and is explicitly designed for clinical efficacy and for lowering the implementation threshold to FI-guided FDE. It combines classic tumour control probability modelling with a multi-variate logistic regression model of FI accuracy and an uncomplicated robust optimisation method. Results Its key elements are (1) that dose is deposited optimally when it achieves equivalent expected effect everywhere in the target volume and (2) that one needs to cap the certainty about the absence of tumour anywhere in the target region. For illustration, an example of a PET/MR-guided FDE in prostate cancer is given. Conclusions FDE can be safeguarded against FI uncertainties, at the price of a limit on the sensible dose escalation.
机译：背景和目的为了通过放射疗法增加局部肿瘤对照而不增加毒性，似乎有希望利用功能性成像（FI）以通过高肿瘤载荷引导靶标的剂量，并且可能使脱升剂量降低风险体积，以最大化沉积的辐射剂量的效率。方法和材料必须解决焦点剂量升级（FDE）有效和安全的方法和材料：（1）如何将模糊信息与多重成像方式相结合; （2）如何考虑基于组织分类的不确定性; （3）如何考虑治疗交付的几何不确定性; （4）如何为现有方案添加互补的方式。通用优化概念解决了这些点，并明确地设计用于临床功效，并降低实现阈值到除引导FDE。它结合了经典的肿瘤控制概率建模，具有多变化的FI准确性逻辑回归模型和简单的鲁棒优化方法。结果其关键要素是（1）当它在目标体积的各地实现等同的预期效果时，剂量沉积，并且（2）需要对目标区域的任何地方持续抑制肿瘤的情况。为了说明，给出了前列腺癌中的PET / MR引导FDE的一个例子。结论可防止FDE可以防止FI不确定性，以合理剂量升级的限制价格。

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