Krishnan Nambudiri Manu Krishnan, Sujadevi V G, Poornachandran Prabaharan, Murali Krishna C, Kanno Takahiro, Noothalapati Hemanth
Centre for Internet Studies and Artificial Intelligence, Amrita Vishwa Vidyapeetham, Amritapuri 690525, Kerala, India.
Chilakapati Laboratory, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Kharghar, Navi Mumbai 410210, Maharashtra, India.
Cancers (Basel). 2024 Nov 22;16(23):3917. doi: 10.3390/cancers16233917.
Frozen section biopsy, introduced in the early 1900s, still remains the gold standard methodology for rapid histologic evaluations. Although a valuable tool, it is labor-, time-, and cost-intensive. Other challenges include visual and diagnostic variability, which may complicate interpretation and potentially compromise the quality of clinical decisions. Raman spectroscopy, with its high specificity and non-invasive nature, can be an effective tool for dependable and quick histopathology. The most promising modality in this context is stimulated Raman histology (SRH), a label-free, non-linear optical process which generates conventional H&E-like images in short time frames. SRH overcomes limitations of conventional Raman scattering by leveraging the qualities of stimulated Raman scattering (SRS), wherein the energy gets transferred from a high-power pump beam to a probe beam, resulting in high-energy, high-intensity scattering. SRH's high resolution and non-requirement of preprocessing steps make it particularly suitable when it comes to intrasurgical histology. Combining SRH with artificial intelligence (AI) can lead to greater precision and less reliance on manual interpretation, potentially easing the burden of the overburdened global histopathology workforce. We review the recent applications and advances in SRH and how it is tapping into AI to evolve as a revolutionary tool for rapid histologic analysis.
冰冻切片活检于20世纪初引入，至今仍是快速组织学评估的金标准方法。尽管它是一种有价值的工具，但它耗费人力、时间和成本。其他挑战包括视觉和诊断的变异性，这可能会使解读复杂化，并可能影响临床决策的质量。拉曼光谱具有高特异性和非侵入性，可成为可靠且快速的组织病理学的有效工具。在这种情况下，最有前景的模式是受激拉曼组织学（SRH），这是一种无标记的非线性光学过程，可在短时间内生成传统的苏木精和伊红（H&E）样图像。SRH通过利用受激拉曼散射（SRS）的特性克服了传统拉曼散射的局限性，在SRS中，能量从高功率泵浦光束转移到探测光束，从而产生高能量、高强度的散射。SRH的高分辨率和无需预处理步骤使其在手术中的组织学方面特别适用。将SRH与人工智能（AI）相结合可提高精度并减少对人工解读的依赖，有可能减轻全球负担过重的组织病理学工作人员的负担。我们回顾了SRH的最新应用和进展，以及它如何利用人工智能发展成为快速组织学分析的革命性工具。
