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

由于结构和表型的异质性，为了诊断和治疗目的，对前列腺癌的位置、程度和分级进行描述仍然具有挑战性。在盲法系统超声引导活检的情况下，这导致超过30%的假阴性率，而对于肿瘤靶向（局灶性）治疗，如近距离放射治疗，其成功取决于MRI癌症靶点的精确映射，以提供大体肿瘤位置，但缺乏治疗位置的分子信息，没有准确的肿瘤分级，并可能导致不良的毒性平衡。因此，现有的医疗技术无法解决治疗期间及时确认肿瘤位置/级别的问题。我们的目标是开发一种图像引导系统，将实时分子组织表征与光学拉曼光谱（RS）仪器相结合，用于肿瘤靶向前列腺癌干预，改善肿瘤定位和表征。具体来说，我们将开发和进行第一次人体研究，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收获样本。我们将在3年内完成四个研究目标，以确保将该技术转化为我们的合作伙伴Elekta和Philips Canada：（1）开发用于组织表征的光纤RS针设备，以指导前列腺干预;（2）从光谱RS签名中训练监督机器学习算法，以推断生物学特性;（3）实现具有实时RS组织分类和针跟踪的集成图像引导平台，以识别具有前列腺运动补偿的MRI目标;（4）对MRI上可见局限性PCa并将接受HDR近距离放射治疗的患者进行初步临床研究。最终，我们将有一个高端的技术在前列腺干预的临床测试，用于实际的分子识别和癌症检测。

Confirming the location, extent, and grade of prostate cancer for diagnostic and therapeuticpurposes remains challenging due to the architectural and phenotypic heterogeneity. In thecase of blind systematic ultrasound-guided biopsies, this leads to more than 30% of falsenegative rates, while for tumor-targeted (focal) therapies such as brachytherapy, theirsuccess 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 tumorand can lead to a poor balance of toxicities. Therefore, there is no existing medicaltechnologies addressing the timely confirmation of tumor location/grade during therapy. Ourobjective is to develop an image-guidance system integrating real-time molecular tissuecharacterization with an optical Raman spectroscopy (RS) instrument for tumor-targetedprostate cancer interventions, improving tumor localization and characterization. Specifically,we will develop and conduct first in-human studies using a real-time navigation platformtechnology allowing for high-precision and tumor-specific harvesting of samples during biopsyprocedures. Four research aims will be completed to insure in-human translation of thetechnology into to our partners Elekta and Philips Canada within 3 years: (1) Develop a fiberoptics RS needle device for tissue characterization to guide prostate interventions; (2) Trainsupervised machine-learning algorithms from spectral RS signatures to infer biologicalproperties; (3) Implement an integrated image-guidance platform with real-time RS tissueclassification 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 willundergo HDR brachytherapy. Ultimately, we will have a high-end technique clinically tested inprostate interventions for practical molecular identification and cancer detection.