Image-Guided Molecular Optical Spectroscopy for Tumor-Targeted Prostate Cancer Interventions

由于结构和表型的异质性，确定前列腺癌的位置、范围和分级以进行诊断和治疗仍然具有挑战性。在盲系统超声引导下的活检中，这导致30%以上的假阴性率，而对于肿瘤靶向（局灶性）治疗，如近距离放疗，其成功与否取决于精确的MRI肿瘤靶点定位，以提供肿瘤的大致位置，但缺乏治疗位置的分子信息，没有准确的肿瘤分级，并可能导致毒性平衡不佳。因此，目前尚无医学技术能够在治疗过程中及时确认肿瘤的位置/分级。我们的目标是开发一种结合实时分子组织表征和光学拉曼光谱（RS）仪器的图像引导系统，用于肿瘤靶向前列腺癌干预，提高肿瘤定位和表征。具体而言，**我们将开发并进行首次人体研究，使用实时导航平台**技术，允许在活检过程中高精度和肿瘤特异性采集样本**。为了确保在3年内将该技术转化为我们的合作伙伴Elekta和Philips Canada，我们将完成四个研究目标：(1)开发用于组织表征的光纤RS针装置，以指导前列腺干预；(2)从光谱RS特征中训练有监督的机器学习算法来推断生物特性；(3)实现具有实时RS组织分类和针迹跟踪的集成图像引导平台，识别具有前列腺运动补偿的MRI目标；**(4)对MRI可见的局部PCa患者**进行HDR近距离治疗的前期临床研究。最终，我们将拥有一项高端技术，在**前列腺干预中进行临床试验，用于实际的分子鉴定和癌症检测。

Confirming the location, extent, and grade of prostate cancer for diagnostic and therapeutic**purposes remains challenging due to the architectural and phenotypic heterogeneity. In the**case of blind systematic ultrasound-guided biopsies, this leads to more than 30% of false**negative rates, while for tumor-targeted (focal) therapies such as brachytherapy, their**success hinge on precise mapping of MRI cancer targets to provide the gross tumor location,**but lack molecular information at the treatment location, without accurate grading of the tumor**and can lead to a poor balance of toxicities. Therefore, there is no existing medical**technologies addressing the timely confirmation of tumor location/grade during therapy. Our**objective is to develop an image-guidance system integrating real-time molecular tissue**characterization with an optical Raman spectroscopy (RS) instrument for tumor-targeted**prostate cancer interventions, improving tumor localization and characterization. Specifically,**we will develop and conduct first in-human studies using a real-time navigation platform**technology allowing for high-precision and tumor-specific harvesting of samples during biopsy**procedures. Four research aims will be completed to insure in-human translation of the**technology into to our partners Elekta and Philips Canada within 3 years: (1) Develop a fiber**optics RS needle device for tissue characterization to guide prostate interventions; (2) Train**supervised machine-learning algorithms from spectral RS signatures to infer biological**properties; (3) Implement an integrated image-guidance platform with real-time RS tissue**classification and needle tracking to identified MRI targets with prostate motion compensation;**(4) Conduct a pilot clinical study patients with localized PCa visible on MRI and who will**undergo HDR brachytherapy. Ultimately, we will have a high-end technique clinically tested in**prostate interventions for practical molecular identification and cancer detection.