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IMAGING SYSTEMS AND METHODS FOR PARTICLE-DRIVEN, KNOWLEDGE-BASED, AND PREDICTIVE CANCER RADIOGENOMICS

机译:粒子驱动的,基于知识的和预测性癌症放射基因组学的成像系统和方法

摘要

Described herein are particle-driven radiogenomics systems and methods that can be used to identify imaging features for prediction of intratumoral and interstitial nanoparticle distributions in cancers (e.g., in low grade and/or high-grade brain cancers (e.g., gliomas, e.g., primary gliomas)). In certain embodiments, the systems and methods described herein extract and combine quantitative multi-dimensional data generated from structural, functional, and/or metabolic imaging. In certain embodiments, the combined multidimensional data is linked to intratumoral and interstitial nanoparticle distributions. For example, this linked data can be used to determine quantitative functional-metabolic multimodality particle-based imaging features and to predict treatment efficacy. These techniques provide an improved quantitative ability to measure treatment response and determine tumor progressions compared to traditional size-based imaging methods.
机译:本文描述的是颗粒驱动的放射基因组学系统和方法,其可用于识别成像特征以预测癌症(例如,低度和/或高级脑癌(例如,神经胶质瘤,例如原发性)中的肿瘤内和间质纳米颗粒分布胶质瘤))。在某些实施例中,本文描述的系统和方法提取并组合从结构,功能和/或代谢成像产生的定量多维数据。在某些实施方案中,组合的多维数据与肿瘤内和组织间纳米颗粒分布相关。例如,此链接的数据可以用于确定定量的功能代谢多峰基于颗粒的成像特征,并预测治疗效果。与传统的基于尺寸的成像方法相比,这些技术提供了改进的定量能力,可以测量治疗反应并确定肿瘤进展。

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