Near Infrared Diffused Light Imaging with Ultrasound Guidance: Predicting Neoadju

描述(由申请人提供)：我们建议验证我们的新型近红外(NIR)超声引导漫反射光学断层扫描(NIR/US)成像系统在评估患者对新辅助化疗(NAC)的病理反应方面的潜在作用。NIR/US是通过在手持探头上同时部署近红外光学传感器和商业超声换能器，并利用共同注册的超声来提供病变结构信息和指导光学断层成像重建来实现的。因此，光学断层扫描克服了与强光散射相关的问题，并提供了可靠的肿瘤血红蛋白分布，这与肿瘤血管生成直接相关。经NIR/US评估的32例NAC患者的初步数据表明，治疗前肿瘤总血红蛋白(THB)含量预测患者最终病理反应的敏感性为79%，特异性为80%。此外，总血红蛋白变化到治疗前水平的百分比(%THb)可用于在NAC开始后的第1周期(2-3周)结束时进一步识别应答者和无应答者。此外，结合广泛使用的肿瘤病理变量和受体状态以及在NAC开始之前获得的血红蛋白功能参数，当包括基线散射数据时，或者根据人表皮生长因子受体2(HER-2/neu)对治疗方案进行分类，或者评估在治疗周期1结束时添加%THb，可以达到100%的预测灵敏度和特异度。在这份提案中，我们将：1)升级近红外成像系统，并验证为成像大病灶而优化的近红外成像算法；2)通过招募大约80名在哈特福德医院、康涅狄格大学健康中心和沃特伯里医院接受NAC的患者来验证初步发现；以及3)进行数据分析a)确定基于周期1%THB评估不同治疗方案的疗效的最佳时间窗口；B)验证基于肿瘤病理变量(肿瘤类型、分级和有丝分裂数)、雌激素受体(ER)、孕激素受体(PR)和HER-2/neu的肿瘤分子标志物，以及基于%THB一个周期的治疗前NIR功能参数以及散射数据和应答率的预测模型。该项目的成功完成将带来一个管理个性化乳腺癌治疗的强大工具。在个性化医学的基因组时代，预测和监测早期反应以预测结果变得至关重要，我们的近红外/美国技术将被证明是无价的。

DESCRIPTION (provided by applicant): We propose to validate the potential role of our novel near-infrared (NIR) diffuse optical tomography guided by ultrasound (NIR/US) imaging system in assessing patient pathological response to neoadjuvant chemotherapy (NAC). NIR/US is implemented by simultaneously deploying NIR optical sensors and a commercial ultrasound transducer on a hand-held probe, and utilizing co-registered ultrasound to provide lesion structure information and guide optical tomography reconstruction. As a result, the optical tomography has overcome problems associated with intense light scattering and has provided reliable tumor hemoglobin distributions, which are directly related to tumor angiogenesis. Pilot data obtained from 32 patients who underwent NAC, which was assessed by NIR/US, have demonstrated that pretreatment tumor total hemoglobin (tHb) content predicts patient final pathological response with 79% sensitivity and 80% specificity. In addition, the percentage of total hemoglobin changes normalized to the pretreatment level (%tHb) can be used to further identify responders from non-responders at the end of cycle 1 (2-3 weeks) after the initiation of NAC. Furthermore, combining widely used tumor pathologic variables and receptor status with hemoglobin functional parameters obtained before the initiation of NAC can achieve 100% prediction sensitivity and specificity when baseline scatter data are included, or treatment regimens are categorized based on human epidermal growth factor receptor 2 (HER-2/neu) or the addition of %tHb at the end of treatment cycle 1 is assessed. In this proposal, we will: 1) Upgrade NIR imaging systems and validate NIR imaging algorithms optimized for imaging large lesions; 2) Validate the initial findings through the recruitment of approximately 80 patients who are undergoing NAC at the Hartford Hospital, the University of Connecticut Health Center, and the Waterbury Hospital; and 3) Perform data analysis a) to determine the best time-window to assess response based on cycle 1 %tHb for different treatment regimens; b) to validate the prediction model developed from pilot data based on tumor pathological variables (tumor type, grade and mitotic count), tumor molecular markers of estrogen receptor (ER), progesterone receptor (PR), and HER-2/neu, and pretreatment NIR functional parameters as well as scatter data and response rate based on one cycle of %tHb. The successful completion of the project will result in a powerful tool to manage personalized breast cancer treatment. In the genomic era of personalized medicine where predicting and monitoring of early responses for outcome prediction becomes crucial, our NIR/US technology will prove to be invaluable.