(Radio)biological optimization of external-beam radiotherapy.

(Radio)biological optimization of external-beam radiotherapy.
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DOI:
10.1155/2012/329214
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发表时间:
2012
影响因子:
--
通讯作者:
Uzan J
Uzan J
中科院分区:
工程技术4区
文献类型:
--
作者:
Nahum AE;Uzan J

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“生物优化”(BIOP)是指使用(无线电)生物标准和模型,即肿瘤控制概率和正常组织并发症概率来规划治疗。确定了四个不同水平的BIOP:水平I是处方剂量的“等毒性”个体化,D是固定分数的presc。D presc是变化的,以保持处于危险中的器官的NTCP恒定。预计非小细胞肺肿瘤的局部控制会有显著改善。II级包括确定D presc和分级方案的个体化等毒性组合。这种方法适用于“平行”OAR(肺、腮腺)。使用我们的BioSuite软件给出示例。用于早期NSCLC的大分割SABR有效地为II级BIOP。III级BIOP使用放射生物学功能作为IMRT逆向计划的一部分,例如,最大化TCP,同时不超过给定的NTCP。这导致不均匀的目标剂量。NTCP模型参数(反映组织“架构”)驱动优化器强调DVH的不同区域,例如,惩罚准连续OAR(如直肠)的高剂量。IV级BIOP将功能成像信息(例如,缺氧或克隆原位置)添加到III级;我们的前列腺“剂量绘制”方案BioProp给出了示例。强调了放射生物学模型的局限性和固有的不确定性。
“Biological optimization” (BIOP) means planning treatments using (radio)biological criteria and models, that is, tumour control probability and normal-tissue complication probability. Four different levels of BIOP are identified: Level I is “isotoxic” individualization of prescription dose D presc at fixed fraction number. D presc is varied to keep the NTCP of the organ at risk constant. Significant improvements in local control are expected for non-small-cell lung tumours. Level II involves the determination of an individualized isotoxic combination of D presc and fractionation scheme. This approach is appropriate for “parallel” OARs (lung, parotids). Examples are given using our BioSuite software. Hypofractionated SABR for early-stage NSCLC is effectively Level-II BIOP. Level-III BIOP uses radiobiological functions as part of the inverse planning of IMRT, for example, maximizing TCP whilst not exceeding a given NTCP. This results in non-uniform target doses. The NTCP model parameters (reflecting tissue “architecture”) drive the optimizer to emphasize different regions of the DVH, for example, penalising high doses for quasi-serial OARs such as rectum. Level-IV BIOP adds functional imaging information, for example, hypoxia or clonogen location, to Level III; examples are given of our prostate “dose painting” protocol, BioProp. The limitations of and uncertainties inherent in the radiobiological models are emphasized.
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