Brian F. Buxton Department of Cardiac and Thoracic Aortic Surgery, Austin Health, Heidelberg, Melbourne, Vic, Australia; Department of Surgery (Austin Health), Melbourne Medical School, The University of Melbourne, Vic, Australia; Spectromix Laboratory, Melbourne, Vic, Australia.
Department of Surgery (Austin Health), Melbourne Medical School, The University of Melbourne, Vic, Australia; Melbourne Medical School, The University of Melbourne, Vic, Australia.
Pathology. 2024 Apr;56(3):313-321. doi: 10.1016/j.pathol.2023.11.008. Epub 2024 Jan 18.
Histopathology is the gold standard for diagnosing fibrosis, but its routine use is constrained by the need for additional stains, time, personnel and resources. Vibrational spectroscopy is a novel technique that offers an alternative atraumatic approach, with short scan times, while providing metabolic and morphological data. This review evaluates vibrational spectroscopy for the assessment of fibrosis, with a focus on point-of-care capabilities. OVID Medline, Embase and Cochrane databases were systematically searched using PRISMA guidelines for search terms including vibrational spectroscopy, human tissue and fibrosis. Studies were stratified based on imaging modality and tissue type. Outcomes recorded included tissue type, machine learning technique, metrics for accuracy and author conclusions. Systematic review yielded 420 articles, of which 14 were relevant. Ten of these articles considered mid-infrared spectroscopy, three dealt with Raman spectroscopy and one with near-infrared spectroscopy. The metrics for detecting fibrosis were Pearson correlation coefficients ranging from 0.65-0.98; sensitivity from 76-100%; specificity from 90-99%; area under receiver operator curves from 0.83-0.98; and accuracy of 86-99%. Vibrational spectroscopy identified fibrosis in myeloproliferative neoplasms in bone, cirrhotic and hepatocellular carcinoma in liver, end-stage heart failure in cardiac tissue and following laser ablation for acne in skin. It also identified interstitial fibrosis as a predictor of early renal transplant rejection in renal tissue. Vibrational spectroscopic techniques can therefore accurately identify fibrosis in a range of human tissues. Emerging data show that it can be used to quantify, classify and provide data about the nature of fibrosis with a high degree of accuracy with potential scope for point-of-care use.
组织病理学是诊断纤维化的金标准，但由于需要额外的染色剂、时间、人员和资源，其常规应用受到限制。振动光谱学是一种新颖的技术，提供了一种非侵入性的替代方法，具有较短的扫描时间，同时提供代谢和形态数据。本综述评估了振动光谱学在纤维化评估中的应用，重点是即时护理能力。使用 PRISMA 指南，通过 OVID Medline、Embase 和 Cochrane 数据库系统地搜索了振动光谱学、人体组织和纤维化等搜索词。研究根据成像方式和组织类型进行分层。记录的结果包括组织类型、机器学习技术、准确性指标和作者结论。系统评价产生了 420 篇文章，其中 14 篇相关。其中 10 篇文章考虑了中红外光谱，3 篇涉及拉曼光谱，1 篇涉及近红外光谱。检测纤维化的指标包括皮尔逊相关系数为 0.65-0.98；灵敏度为 76-100%；特异性为 90-99%；接受者操作曲线下的面积为 0.83-0.98；准确性为 86-99%。振动光谱学在骨髓增生性肿瘤中的骨、肝硬化和肝细胞癌中的肝、终末期心力衰竭中的心脏组织以及痤疮激光消融后的皮肤中均能识别纤维化。它还可以识别间质纤维化，作为肾组织中早期肾移植排斥的预测指标。因此，振动光谱技术可以准确识别多种人体组织中的纤维化。新出现的数据表明，它可以用于定量、分类，并提供纤维化性质的高度准确数据，具有即时护理应用的潜在范围。
