Near Infrared Diffused Light Imaging with Ultrasound Guidance: Predicting Neoadjuvant Chemotherapy Response

描述（由申请人提供）：我们建议验证我们的新型近红外（NIR）漫射光学断层扫描超声（NIR/US）成像系统在评估患者对新辅助化疗（NAC）的病理反应中的潜在作用。NIR/US通过在手持式探头上同时部署NIR光学传感器和商业超声换能器来实现，并利用共配准超声来提供病变结构信息并引导光学断层成像重建。因此，光学层析成像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。从接受NAC的32例患者中获得的初步数据（通过NIR/US进行评估）表明，治疗前肿瘤总血红蛋白（tHb）含量预测患者最终病理学反应的灵敏度为79%，特异性为80%。此外，标准化至治疗前水平的总血红蛋白变化百分比（%tHb）可用于在NAC开始后第1周期结束时（2-3周）进一步鉴别应答者与非应答者。此外，将广泛使用的肿瘤病理学变量和受体状态与NAC开始前获得的血红蛋白功能参数相结合，当包括基线分散数据时，或基于人表皮生长因子受体2（HER-2/neu）对治疗方案进行分类或评估治疗周期1结束时添加%tHb时，可以实现100%的预测灵敏度和特异性。在本提案中，我们将：1）升级近红外成像系统并验证针对大型病变成像而优化的近红外成像算法; 2）通过招募约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;和3）进行数据分析a）以确定基于不同治疗方案的周期1%tHb评估响应的最佳时间窗; B）验证基于肿瘤病理变量从试点数据开发的预测模型（肿瘤类型、分级和有丝分裂计数）、雌激素受体（ER）、孕激素受体（PR）和HER-2/neu的肿瘤分子标志物，和基于%tHb的一个循环的预处理NIR功能参数以及散射数据和响应率。该项目的成功完成将成为管理个性化乳腺癌治疗的强大工具。在个性化医疗的基因组时代，预测和监测早期反应以预测结果变得至关重要，我们的NIR/US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.