Sono-Contrast Induced Functional Imaging/Spectroscopy

描述(由申请人提供)： 提出了一种体内成像的创新方法，用于检测相对浅表的恶性肿瘤并进行病理生理分类。这项名为声对比诱导功能成像和光谱(SCIFIS)的技术建立在现有的近红外漫反射光谱(DRS)的基础上，但使用驻波聚焦超声沉积作为造影剂来提高DRS的灵敏度和特异性。总体目标是开发一种用于癌症诊断、预后和治疗后监测的非侵入性、实时模式。 所提出的活体成像方法的基础是聚焦超声发射的声辐射力形成驻波，导致血管系统的可逆压缩和/或血液流动停滞，从而导致血红蛋白浓度的时间变化，这在近红外光谱的动态(辐射前后)观测中被记录下来。通过将光路和声路以一定角度相交，并减去声学造影剂沉积前后的测量频谱，可以在相交位置识别血管病理生理学的特征。 在R21期，接种小鼠左腿乳腺肿瘤的C3H小鼠将在免疫组织化学检查之前进行体内SCIFIS测量。主成分分析神经网络(PCANN)将用于数据分析/关联，通过接收器操作特性分析以及肿瘤检测和血管分布预测的统计关联，最终检验基本假设。 在R33阶段，SCIFIS技术的临床评估将在通过蒙特卡洛模拟进行系统优化后开始。SCIFIS光谱的非侵入性活体采集将在因怀疑乳腺癌而接受活检的患者中收集。基于PCANN的预测模型将与病理组织诊断进行定量比较。此外，对于那些被发现患有恶性肿瘤并最终接受放射治疗的患者，对放射治疗的反应和结果将与超声对比频谱中的血管生成和/或肿瘤缺氧结果相关。

DESCRIPTION (provided by applicant): An innovative method of in vivo imaging for detection and pathophysiological classification of relatively superficial malignancies is proposed. The technique, sono-contrast induced functional imaging and spectroscopy (SCIFIS), builds upon existing diffuse reflectance spectroscopy (DRS) in the near infrared region but uses standing wave focused ultrasound deposition as a contrast agent to improve the sensitivity and specificity of DRS. The overall goal is to develop a non-invasive, real-time modality for cancer diagnosis, prognosis and post-treatment surveillance. The basis of the proposed in vivo imaging method is that acoustic radiation force delivered by focused ultrasound forming a standing wave causes reversible compression of the vasculature and/or blood flow stasis, and thus temporal changes in hemoglobin concentration, which are registered in the dynamic (pre- and post-insonification) observation of near-infrared spectra. By intersecting the optical path and the acoustic path at an angle and subtracting measured spectra pre- and post-deposition of sono-contrast, signatures of vascular pathophysiology can be identified at the intersecting location. In the R21 phase, C3H mice inoculated with murine mammary tumors on the left legs will undergo in vivo SCIFIS measurement prior to immunohistochemical examination. Principal component analysis neural networks (PCANN) will be used in data analysis/correlation, leading to definitively tests of the underlying hypotheses via receiver operating characteristics analysis and statistical correlation of tumor detection and vascular distribution predictions. In the R33 phase, clinical evaluation of the SCIFIS technique will commence following system optimization by Monte Carlo simulation. Non-invasive, in vivo acquisition of SCIFIS spectra will be collected in patients undergoing biopsy for suspicion of breast cancers. The predictive model based on PCANN will be quantitatively compared with pathological tissue diagnoses. In addition, response to radiation treatment and outcome will be correlated with angiogenesis and/or tumor hypoxia findings in the sonocontrast spectra for those patients who are found to have malignant diagnoses and eventually receive radiotherapy.