School of Medical Technology, Beijing Institute of Technology, Beijing, China.
Department of Neurosurgery, First Medical Center of PLA General Hospital, Beijing, China.
J Biophotonics. 2023 Nov;16(11):e202300195. doi: 10.1002/jbio.202300195. Epub 2023 Aug 29.
Safely maximizing brain cancer removal without injuring adjacent healthy tissue is crucial for optimal treatment outcomes. However, it is challenging to distinguish cancer from noncancer intraoperatively. This study aimed to explore the feasibility of diffuse reflectance spectroscopy (DRS) as a label-free and real-time detection technology for discrimination between brain cancer and noncancer tissues. Fifty-five fresh cancer and noncancer specimens from 19 brain surgeries were measured with DRS, and the results were compared with co-registered clinical standard histopathology. Tissue optical properties were quantitatively obtained from the diffuse reflectance spectra and compared among different types of brain tissues. A machine learning-based classifier was trained to differentiate cancerous versus noncancerous tissues. Our method could achieve a sensitivity of 93% and specificity of 95% for discriminating high-grade glioma from normal white matter. Our results showed that DRS has the potential to be used for label-free, real-time in vivo cancer detection during brain surgery.
安全地最大限度地切除脑癌而不损伤相邻的健康组织对于获得最佳治疗效果至关重要。然而，在术中区分癌症和非癌症组织具有挑战性。本研究旨在探索漫反射光谱（DRS）作为一种无标记和实时检测技术，用于区分脑癌和非癌组织的可行性。对 19 例脑外科手术中的 55 个新鲜癌和非癌标本进行了 DRS 测量，并将结果与实时注册的临床标准组织病理学进行了比较。从漫反射光谱中定量获得组织光学特性，并比较不同类型的脑组织之间的差异。基于机器学习的分类器被训练来区分癌组织和非癌组织。我们的方法可以实现 93%的灵敏度和 95%的特异性，用于区分高级别胶质瘤和正常白质。我们的结果表明，DRS 有可能在脑外科手术中用于无标记、实时的体内癌症检测。
