Comparison of Kinetic Models for Dual-Tracer Receptor Concentration Imaging in Tumors.

Nazanin Hamzei, Kimberley S Samkoe, Jonathan T Elliott, Robert W Holt, Jason R Gunn, Tayyaba Hasan, Brian W Pogue, Kenneth M Tichauer
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Abstract

Molecular differences between cancerous and healthy tissue have become key targets for novel therapeutics specific to tumor receptors. However, cancer cell receptor expression can vary within and amongst different tumors, making strategies that can quantify receptor concentration in vivo critical for the progression of targeted therapies. Recently a dual-tracer imaging approach capable of providing quantitative measures of receptor concentration in vivo was developed. It relies on the simultaneous injection and imaging of receptor-targeted tracer and an untargeted tracer (to account for non-specific uptake of the targeted tracer). Early implementations of this approach have been structured on existing "reference tissue" imaging methods that have not been optimized for or validated in dual-tracer imaging. Using simulations and mouse tumor model experimental data, the salient findings in this study were that all widely used reference tissue kinetic models can be used for dual-tracer imaging, with the linearized simplified reference tissue model offering a good balance of accuracy and computational efficiency. Moreover, an alternate version of the full two-compartment reference tissue model can be employed accurately by assuming that the K1s of the targeted and untargeted tracers are similar to avoid assuming an instantaneous equilibrium between bound and free states (made by all other models).

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肿瘤双示踪受体浓度成像动力学模型的比较。
癌变组织和健康组织之间的分子差异已成为针对肿瘤受体的新疗法的关键靶点。然而,癌细胞受体的表达在不同肿瘤内部和不同肿瘤之间可能存在差异,因此,能够量化体内受体浓度的策略对于靶向治疗的进展至关重要。最近开发了一种双示踪成像方法,能够提供体内受体浓度的定量测量。它依赖于受体靶向示踪剂和非靶向示踪剂的同时注射和成像(以解释靶向示踪剂的非特异性摄取)。该方法的早期实现是基于现有的“参考组织”成像方法,这些方法尚未针对双示踪成像进行优化或验证。通过模拟和小鼠肿瘤模型实验数据,本研究的突出发现是所有广泛使用的参考组织动力学模型都可以用于双示踪成像,线性化简化的参考组织模型在准确性和计算效率之间取得了很好的平衡。此外,通过假设靶向和非靶向示踪剂的K1s相似,可以准确地采用全双室参考组织模型的另一种版本,以避免假设束缚状态和自由状态之间的瞬时平衡(由所有其他模型产生)。
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