Buschman H P, Motz J T, Deinum G, Römer T J, Fitzmaurice M, Kramer J R, van der Laarse A, Bruschke A V, Feld M S
Leiden University Medical Center, Leiden, Netherlands.
Cardiovasc Pathol. 2001 Mar-Apr;10(2):59-68. doi: 10.1016/s1054-8807(01)00063-1.
Recent studies have shown that chemical composition and morphology, rather than anatomy (degree of stenosis), determine atherosclerotic plaque instability and predict disease progression. Current clinical diagnostic techniques provide accurate assessment of plaque anatomy, but have limited capability to assess plaque morphology in vivo. Here we describe a technique for a morphology-based diagnosis of atherosclerosis in the coronary arteries using Raman spectroscopy that can potentially be performed in vivo using optical fiber technology.
Raman tissue spectra were collected from normal and atherosclerotic coronary artery samples in different stages of disease progression (n=165) from explanted transplant recipient hearts (n=16). Raman spectra from the elastic laminae (EL), collagen fibers (CF), smooth muscle cells (SMC), adventitial adipocytes (AA) or fat cells, foam cells (FC), necrotic core (NC), cholesterol crystals (CC), beta-carotene containing crystals (beta-C), and calcium mineralizations (CM) were used as basis spectra in a linear least squares-minimization (LSM) model to calculate the contribution of these morphologic structures to the coronary artery tissue spectra.
We developed a diagnostic algorithm that used the fit-contributions of the various morphologic structures to classify 97 coronary artery samples in an initial calibration data set as either nonatherosclerotic, calcified plaque, or noncalcified atheromatous plaque. The algorithm was subsequently tested prospectively in a second validation data set, and correctly classified 64 (94%) of 68 coronary artery samples.
Raman spectroscopy provides information about the morphologic composition of intact human coronary artery without the need for excision and microscopic examination. In the future, it may be possible to use this technique to analyze the morphologic composition of atherosclerotic coronary artery lesions and assess plaque instability and disease progression in vivo.
最近的研究表明，决定动脉粥样硬化斑块不稳定性并预测疾病进展的是化学成分和形态，而非解剖结构（狭窄程度）。当前的临床诊断技术能够准确评估斑块的解剖结构，但在体内评估斑块形态的能力有限。在此，我们描述了一种利用拉曼光谱对冠状动脉粥样硬化进行基于形态学诊断的技术，该技术有可能通过光纤技术在体内实施。
从16例移植受者心脏的离体样本中，收集处于疾病进展不同阶段的正常和动脉粥样硬化冠状动脉样本（n = 165）的拉曼组织光谱。弹性膜（EL）、胶原纤维（CF）、平滑肌细胞（SMC）、外膜脂肪细胞（AA）或脂肪细胞、泡沫细胞（FC）、坏死核心（NC）、胆固醇结晶（CC）、含β-胡萝卜素结晶（β-C）以及钙盐沉积（CM）的拉曼光谱被用作线性最小二乘法（LSM）模型中的基础光谱，以计算这些形态结构对冠状动脉组织光谱的贡献。
我们开发了一种诊断算法，该算法利用各种形态结构的拟合贡献，将初始校准数据集中的97个冠状动脉样本分类为非动脉粥样硬化、钙化斑块或非钙化动脉粥样斑块。随后，该算法在第二个验证数据集中进行前瞻性测试，正确分类了68个冠状动脉样本中的64个（94%）。
拉曼光谱无需切除和显微镜检查就能提供完整人体冠状动脉形态组成的信息。未来，有可能利用该技术分析动脉粥样硬化冠状动脉病变的形态组成，并在体内评估斑块不稳定性和疾病进展。
