随机剂量率诱导恶性肿瘤增强消除而不增加剂量的策略

Subhadip Paul;Prasun Kumar Roy
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摘要

放射治疗是癌症治疗的一种主要方式,其疗效一般取决于治疗过程中对肿瘤的总放射剂量。然而,所传递的辐射也会照射正常组织,并且剂量递增程序通常也会增加正常组织的消除。在本文中,我们在线性-二次-线性(LQL)模型的前提下,利用随机微分方程和Jensen不等式建立了理论框架,以探索在规定的总辐射剂量范围内,分次放射治疗中增加前列腺肿瘤细胞的消除和增强正常组织的相对保留这两个治疗性能目标的可能性。我们的研究预测,辐射剂量率的随机时间调节明显增强前列腺肿瘤细胞的消除,而不需要在放射治疗中增加剂量。然而,持续较高的剂量率也可以增强肿瘤细胞的消除。在这种情况下,我们已经表明,正常组织与随机剂量率的节省是相当多的正常组织与同等常数较高的剂量率的节省。此外,通过对比LQL和线性二次(LQ)模型下的随机剂量率效应,我们还表明LQ模型高估了肿瘤中的随机剂量率效应,而低估了正常组织中的随机剂量率效应。我们的研究表明利用辐射剂量率的随机调制来设计癌症强化放射治疗方案的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Strategy for stochastic dose-rate induced enhanced elimination of malignant tumour without dose escalation
The efficacy of radiation therapy, a primary modality of cancer treatment, depends in general upon the total radiation dose administered to the tumour during the course of therapy. Nevertheless, the delivered radiation also irradiates normal tissues and dose escalation procedure often increases the elimination of normal tissue as well. In this article, we have developed theoretical frameworks under the premise of linear-quadratic-linear (LQL) model using stochastic differential equation and Jensen's inequality for exploring the possibility of attending to the two therapeutic performance objectives in contraposition—increasing the elimination of prostate tumour cells and enhancing the relative sparing of normal tissue in fractionated radiation therapy, within a prescribed limit of total radiation dose. Our study predicts that stochastic temporal modulation in radiation dose-rate appreciably enhances prostate tumour cell elimination, without needing dose escalation in radiation therapy. However, constant higher dose-rate can also enhance the elimination of tumour cells. In this context, we have shown that the sparing of normal tissue with stochastic dose-rate is considerably more than the sparing of normal tissue with the equivalent constant higher dose-rate. Further, by contrasting the stochastic dose-rate effects under LQL and linear-quadratic (LQ) models, we have also shown that the LQ model over-estimates stochastic dose-rate effect in tumour and under-estimates the stochastic dose-rate effect in normal tissue. Our study indicates the possibility of utilizing stochastic modulation of radiation dose-rate for designing enhanced radiation therapy protocol for cancer.
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