Chauhan Rajat, El-Baz Nagwa, Keynton Robert S, James Kurtis T, Malik Danial A, Zhu Mingming, El-Baz Ayman, Ng Chin K, Bates Paula J, Malik Mohammad Tariq, O'Toole Martin G
Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA.
Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY 40202, USA.
Nanomaterials (Basel). 2019 May 7;9(5):709. doi: 10.3390/nano9050709.
Gold nanoparticles (GNPs) have tremendous potential as cancer-targeted contrast agents for diagnostic imaging. The ability to modify the particle surface with both disease-targeting molecules (such as the cancer-specific aptamer AS1411) and contrast agents (such as the gadolinium chelate Gd(III)-DO3A-SH) enables tailoring the particles for specific cancer-imaging and diagnosis. While the amount of image contrast generated by nanoparticle contrast agents is often low, it can be augmented with the assistance of computer image analysis algorithms. In this work, the ability of cancer-targeted gold nanoparticle-oligonucleotide conjugates to distinguish between malignant (MDA-MB-231) and healthy cells (MCF-10A) is tested using a T1-weighted image analysis algorithm based on three-dimensional, deformable model-based segmentation to extract the Volume of Interest (VOI). The gold nanoparticle/algorithm tandem was tested using contrast agent GNP-Gd(III)-DO3A-SH-AS1411) and nontargeted c-rich oligonucleotide (CRO) analogs and control (CTR) counterparts (GNP-Gd(III)-DO3A-SH-CRO/CTR) via in vitro studies. Remarkably, the cancer cells were notably distinguished from the nonmalignant cells, especially at nanomolar contrast agent concentrations. The T1-weighted image analysis algorithm provided similar results to the industry standard Varian software interface (VNMRJ) analysis of T1 maps at micromolar contrast agent concentrations, in which the VNMRJ produced a 19.5% better MRI contrast enhancement. However, our algorithm provided more sensitive and consistent results at nanomolar contrast agent concentrations, where our algorithm produced ~500% better MRI contrast enhancement.
金纳米颗粒（GNPs）作为用于诊断成像的癌症靶向造影剂具有巨大潜力。用疾病靶向分子（如癌症特异性适配体AS1411）和造影剂（如钆螯合物Gd(III)-DO3A-SH）修饰颗粒表面的能力，使得能够针对特定的癌症成像和诊断对颗粒进行定制。虽然纳米颗粒造影剂产生的图像对比度通常较低，但可以借助计算机图像分析算法来增强。在这项工作中，使用基于三维、可变形模型分割的T1加权图像分析算法来提取感兴趣体积（VOI），测试了癌症靶向金纳米颗粒 - 寡核苷酸缀合物区分恶性细胞（MDA - MB - 231）和健康细胞（MCF - 10A）的能力。通过体外研究，使用造影剂GNP - Gd(III)-DO3A - SH - AS1411和非靶向富含胞嘧啶的寡核苷酸（CRO）类似物以及对照（CTR）对应物（GNP - Gd(III)-DO3A - SH - CRO/CTR）对金纳米颗粒/算法组合进行了测试。值得注意的是，癌细胞与非恶性细胞有明显区分，尤其是在纳摩尔浓度的造影剂情况下。在微摩尔浓度的造影剂下，T1加权图像分析算法提供的结果与行业标准的Varian软件界面（VNMRJ）对T1图谱的分析结果相似，其中VNMRJ产生的MRI对比度增强效果好19.5%。然而，在纳摩尔浓度的造影剂下，我们的算法提供了更敏感和一致的结果，在该浓度下我们的算法产生的MRI对比度增强效果好约500%。
