ChemImage Corporation, Pittsburgh, Pennsylvania, United States.
Allegheny General Hospital, Pittsburgh, Pennsylvania, United States.
J Biomed Opt. 2020 Feb;25(2):1-18. doi: 10.1117/1.JBO.25.2.026003.
A key risk faced by oncological surgeons continues to be complete removal of tumor. Currently, there is no intraoperative imaging device to detect kidney tumors during excision.
We are evaluating molecular chemical imaging (MCI) as a technology for real-time tumor detection and margin assessment during tumor removal surgeries.
In exploratory studies, we evaluate visible near infrared (Vis-NIR) MCI for differentiating tumor from adjacent tissue in ex vivo human kidney specimens, and in anaesthetized mice with breast or lung tumor xenografts. Differentiation of tumor from nontumor tissues is made possible with diffuse reflectance spectroscopic signatures and hyperspectral imaging technology. Tumor detection is achieved by score image generation to localize the tumor, followed by application of computer vision algorithms to define tumor border.
Performance of a partial least squares discriminant analysis (PLS-DA) model for kidney tumor in a 22-patient study is 0.96 for area under the receiver operating characteristic curve. A PLS-DA model for in vivo breast and lung tumor xenografts performs with 100% sensitivity, 83% specificity, and 89% accuracy.
Detection of cancer in surgically resected human kidney tissues is demonstrated ex vivo with Vis-NIR MCI, and in vivo on mice with breast or lung xenografts.
肿瘤外科医生面临的一个关键风险仍然是彻底切除肿瘤。目前,在切除过程中没有用于检测肾肿瘤的术中成像设备。
我们正在评估分子化学成像 (MCI) 作为一种在肿瘤切除手术中实时检测肿瘤和评估边缘的技术。
在探索性研究中,我们评估了可见近红外 (Vis-NIR) MCI 用于区分离体人肾标本中的肿瘤与邻近组织,以及麻醉小鼠的乳腺癌或肺癌异种移植物中的肿瘤。通过漫反射光谱特征和高光谱成像技术,可以区分肿瘤与非肿瘤组织。通过生成分数图像来定位肿瘤来实现肿瘤检测,然后应用计算机视觉算法来定义肿瘤边界。
在一项 22 名患者的研究中,对肾脏肿瘤的偏最小二乘判别分析 (PLS-DA) 模型的性能为 0.96 的受试者工作特征曲线下面积。体内乳腺癌和肺癌异种移植物的 PLS-DA 模型具有 100%的灵敏度、83%的特异性和 89%的准确性。
在离体人肾组织中,通过 Vis-NIR MCI 以及在麻醉小鼠的乳腺癌或肺癌异种移植物中,已经证明了手术切除组织中癌症的检测。