Sono-Contrast Induced Functional Imaging/Spectroscopy

描述（由申请人提供）： 提出了一种用于相对浅表恶性肿瘤的检测和病理生理学分类的体内成像的创新方法。该技术，声造影诱导功能成像和光谱（SCIFIS），建立在现有的漫反射光谱（DRS）在近红外区域，但使用驻波聚焦超声沉积作为造影剂，以提高灵敏度和特异性的DRS。总体目标是开发一种非侵入性的，实时的模式，癌症诊断，预后和治疗后监测。 所提出的体内成像方法的基础是由形成驻波的聚焦超声递送的声辐射力引起脉管系统的可逆压缩和/或血流停滞，并且因此引起血红蛋白浓度的时间变化，其在近红外光谱的动态（声穿透前和声穿透后）观察中被记录。通过使光路和声路以一定角度相交并减去声造影剂沉积前后的测量光谱，可以在相交位置处识别血管病理生理学的特征。 在R21阶段，在免疫组织化学检查之前，对左腿接种鼠乳腺肿瘤的C3 H小鼠进行体内SCIFIS测量。主成分分析神经网络（PCANN）将用于数据分析/相关性，通过肿瘤检测和血管分布预测的受试者操作特征分析和统计相关性对潜在假设进行明确检验。 在R33阶段，SCIFIS技术的临床评价将在通过Monte Carlo模拟进行系统优化后开始。将在因疑似乳腺癌而接受活检的患者中收集SCIFIS光谱的非侵入性体内采集。将基于PCANN的预测模型与病理组织诊断进行定量比较。此外，对放射治疗的反应和结果将与那些被发现具有恶性诊断并最终接受放射治疗的患者的声造影频谱中的血管生成和/或肿瘤缺氧发现相关。

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.