Kouri Maria Anthi, Spyratou Ellas, Karnachoriti Maria, Kalatzis Dimitris, Danias Nikolaos, Arkadopoulos Nikolaos, Seimenis Ioannis, Raptis Yannis S, Kontos Athanassios G, Efstathopoulos Efstathios P
Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece.
2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece.
Cancers (Basel). 2022 Feb 23;14(5):1144. doi: 10.3390/cancers14051144.
Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic "molecular finger-print" of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.
准确的原位诊断和对恶性肿瘤的最佳手术切除是降低癌症相关发病率和死亡率的关键因素。在外科肿瘤学中,准确区分健康组织和癌组织对于患者的术后护理至关重要。传统成像技术一直试图作为原位活检和手术指导的辅助工具。然而,就特异性、敏感性、复用能力、空间和时间分辨率而言,没有一种单一的成像方式被证明是足够的。此外,大多数技术无法提供有关分子组织组成的信息。在本综述中,我们强调拉曼光谱作为一种具有高检测灵敏度和空间分辨率的光谱技术的潜力,可在微观尺度上实时区分健康边缘和恶性边缘。拉曼光谱构成了组织的固有“分子指纹”,与炎症或癌组织状态相关的任何生化改变都反映在其拉曼光谱指纹上。如今,结合现代仪器设备和机器学习方法的先进拉曼系统正在作为辅助工具进入临床领域,以实现外科肿瘤学的个性化和优化疗效。