Chowdary M V P, Kumar K Kalyan, Kurien Jacob, Mathew Stanley, Krishna C Murali
Center for Laser Spectroscopy, Manipal Academy of Higher Education, Manipal-576 104, Karnataka, India.
Biopolymers. 2006 Dec 5;83(5):556-69. doi: 10.1002/bip.20586.
Breast cancers are the leading cancers among females. Diagnosis by fine needle aspiration cytology (FNAC) is the gold standard. The widely practiced screening method, mammography, suffers from high false positive results and repeated exposure to harmful ionizing radiation. As with all other cancers survival rates are shown to heavily depend on stage of the cancers (Stage 0, 95%; Stage IV, 75%). Hence development of more reliable screening and diagnosis methodology is of considerable interest in breast cancer management. Raman spectra of normal, benign, and malignant breast tissue show significant differences. Spectral differences between normal and diseased breast tissues are more pronounced than between the two pathological conditions, malignant and benign tissues. Based on spectral profiles, the presence of lipids (1078, 1267, 1301, 1440, 1654, 1746 cm(-1)) is indicated in normal tissue and proteins (stronger amide I, red shifted DeltaCH2, broad and strong amide III, 1002, 1033, 1530, 1556 cm(-1)) are found in benign and malignant tissues. The major differences between benign and malignant tissue spectra are malignant tissues seem to have an excess of lipids (1082, 1301, 1440 cm(-1)) and presence of excess proteins (amide I, amide III, red shifted DeltaCH2, 1033, 1002 cm(-1)) is indicated in benign spectra. The multivariate statistical tool, principal components analysis (PCA) is employed for developing discrimination methods. A score of factor 1 provided a reasonable classification of all three tissue types. The analysis is further fine-tuned by employing Mahalanobis distance and spectral residuals as discriminating parameters. This approach is tested both retrospectively and prospectively. The limit test, which provides the most unambiguous discrimination, is also considered and this approach clearly discriminated all three tissue types. These results further support the efficacy of Raman spectroscopic methods in discriminating normal and diseased breast tissues.
乳腺癌是女性中最常见的癌症。细针穿刺细胞学检查（FNAC）是诊断的金标准。广泛应用的筛查方法——乳腺钼靶摄影，存在较高的假阳性结果，且会使患者反复暴露于有害的电离辐射中。与所有其他癌症一样，生存率很大程度上取决于癌症的分期（0期，95%；IV期，75%）。因此，开发更可靠的筛查和诊断方法在乳腺癌管理中具有重要意义。正常、良性和恶性乳腺组织的拉曼光谱显示出显著差异。正常乳腺组织与病变乳腺组织之间的光谱差异比恶性和良性这两种病理状态之间的差异更为明显。基于光谱特征，正常组织中显示存在脂质（1078、1267、1301、1440、1654、1746 cm⁻¹），而良性和恶性组织中发现有蛋白质（较强的酰胺I、红移的ΔCH₂、宽且强的酰胺III、1002、1033、1530、1556 cm⁻¹）。良性和恶性组织光谱的主要差异在于，恶性组织似乎有过量的脂质（1082、1301、1440 cm⁻¹），而良性光谱中显示存在过量的蛋白质（酰胺I、酰胺III、红移的ΔCH₂、1033、1002 cm⁻¹）。采用多元统计工具主成分分析（PCA）来开发鉴别方法。因子1的得分对所有三种组织类型进行了合理分类。通过采用马氏距离和光谱残差作为鉴别参数，对分析进行了进一步优化。该方法进行了回顾性和前瞻性测试。还考虑了能提供最明确鉴别的极限测试，这种方法能清晰地区分所有三种组织类型。这些结果进一步支持了拉曼光谱法在鉴别正常和病变乳腺组织方面的有效性。
