Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada.
Ultrashort Laser Nanomanufacturing Research Facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada.
ACS Nano. 2022 Nov 22;16(11):17948-17964. doi: 10.1021/acsnano.2c04187. Epub 2022 Sep 16.
Brain cancers, one of the most fatal malignancies, require accurate diagnosis for guided therapeutic intervention. However, conventional methods for brain cancer prognosis (imaging and tissue biopsy) face challenges due to the complex nature and inaccessible anatomy of the brain. Therefore, deep analysis of brain cancer is necessary to (i) detect the presence of a malignant tumor, (ii) identify primary or secondary origin, and (iii) find where the tumor is housed. In order to provide a diagnostic technique with such exhaustive information here, we attempted a liquid biopsy-based deep surveillance of brain cancer using a very minimal amount of blood serum (5 μL) in real time. We hypothesize that holistic analysis of serum can act as a reliable source for deep brain cancer surveillance. To identify minute amounts of tumor-derived material in circulation, we synthesized an ultrasensitive 3D nanosensor, adopted SERS as a diagnostic methodology, and undertook a DEEP neural network-based brain cancer surveillance. Detection of primary and secondary tumor achieved 100% accuracy. Prediction of intracranial tumor location achieved 96% accuracy. This modality of using patient sera for deep surveillance is a promising noninvasive liquid biopsy tool with the potential to complement current brain cancer diagnostic methodologies.
脑癌是最致命的恶性肿瘤之一，需要准确的诊断来指导治疗干预。然而，传统的脑癌预后方法（成像和组织活检）由于大脑的复杂性质和不可及的解剖结构而面临挑战。因此，需要对脑癌进行深入分析，以 (i) 检测恶性肿瘤的存在，(ii) 识别原发性或继发性起源，以及 (iii) 找到肿瘤所在的位置。为了提供具有如此详尽信息的诊断技术，我们尝试使用非常少量的血清（5 μL）实时进行基于液体活检的脑癌深度监测。我们假设对血清进行整体分析可以作为深度脑癌监测的可靠来源。为了在循环中检测到微量的肿瘤衍生物质，我们合成了一种超灵敏的 3D 纳米传感器，采用 SERS 作为诊断方法，并进行了基于深度神经网络的脑癌监测。原发性和继发性肿瘤的检测准确率达到 100%。颅内肿瘤位置的预测准确率达到 96%。这种使用患者血清进行深度监测的方法是一种很有前途的非侵入性液体活检工具，有可能补充现有的脑癌诊断方法。
