Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel.
Nanomedicine. 2013 Jan;9(1):15-21. doi: 10.1016/j.nano.2012.07.009. Epub 2012 Sep 8.
In this case study, we demonstrate the feasibility of nanomaterial-based sensors for identifying the breath-print of early-stage lung cancer (LC) and for short-term follow-up after LC-resection. Breath samples were collected from a small patient cohort prior to and after lung resection. Gas-chromatography/mass-spectrometry showed that five volatile organic compounds were significantly reduced after LC surgery. A nanomaterial-based sensor-array distinguished between pre-surgery and post-surgery LC states, as well as between pre-surgery LC and benign states. In contrast, the same sensor-array could neither distinguish between pre-surgery and post-surgery benign states, nor between LC and benign states after surgery. This indicates that the observed pattern is associated with the presence of malignant lung tumors. The proof-of-concept presented here has initiated a large-scale clinical study for post-surgery follow-up of LC patients.
Monitoring for tumor recurrence remains very challenging due to post-surgical and radiation therapy induced changes in target organs, which often renders standard radiological identification of recurrent malignancies inaccurate. In this paper a novel nanotechnology-based sensor array is used for identification of volatile organic compounds in exhaled air that enable identification of benign vs. malignant states.
在本案例研究中，我们展示了基于纳米材料的传感器在识别早期肺癌（LC）的呼吸特征以及 LC 切除术后短期随访方面的可行性。在肺切除术前和术后，我们从小患者队列中采集了呼吸样本。气相色谱/质谱显示，五种挥发性有机化合物在 LC 手术后显著减少。基于纳米材料的传感器阵列可以区分手术前和手术后的 LC 状态，以及手术前的 LC 和良性状态。相比之下，相同的传感器阵列既不能区分手术前和手术后的良性状态，也不能区分手术后 LC 和良性状态。这表明观察到的模式与恶性肺肿瘤的存在有关。这里提出的概念验证已经启动了一项针对 LC 患者术后随访的大规模临床研究。
由于手术后和放射治疗引起的靶器官变化，监测肿瘤复发仍然极具挑战性，这常常导致标准的放射学识别复发性恶性肿瘤不准确。在本文中，一种新的基于纳米技术的传感器阵列用于识别呼气中挥发性有机化合物，从而能够识别良性与恶性状态。
