Unité MéDIAN CNRS UMR 6237 MEDyC, Université Reims-Champagne Ardenne, Faculté de Pharmacie, 51 rue Cognacq-Jay, 51096 REIMS CEDEX, France.
Analyst. 2009 Jun;134(6):1208-14. doi: 10.1039/b820998g. Epub 2009 Feb 24.
Non-melanoma skin cancer includes basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and Bowen's disease. The differential diagnosis of these lesions is sometimes difficult and relies on the histopathological examination of surgical specimens. However, a precise differential diagnosis is crucial for an accurate therapy and thus better patient care. FTIR spectral micro-imaging was applied directly on formalin-fixed paraffin-embedded samples of non-melanoma skin cancers. Chemometric and multivariate statistical analyses were developed to generate an automated IR-based histology without any chemical dewaxing. Different prediction models were developed using linear discriminant analysis combined with data reduction by Principal Component Analysis (PCA) or by wavenumber selection using statistical tests or genetic algorithms. Pseudo-colour maps were reconstructed and compared to conventional histology procedures. High correlation was obtained between the prediction maps and the histology which proves the great potential of FTIR spectroscopy for the differential diagnosis of skin carcinomas.
非黑色素瘤皮肤癌包括基底细胞癌（BCC）、鳞状细胞癌（SCC）和 Bowen 病。这些病变的鉴别诊断有时较为困难，依赖于手术标本的组织病理学检查。然而，准确的鉴别诊断对于精确的治疗和更好的患者护理至关重要。傅里叶变换红外光谱（FTIR）光谱微观成象技术直接应用于非黑色素瘤皮肤癌的福尔马林固定石蜡包埋样本。通过化学计量学和多元统计分析，生成了一种无需任何化学脱蜡的自动化基于红外的组织学方法。使用线性判别分析（LDA），结合主成分分析（PCA）的数据缩减或使用统计检验或遗传算法的波数选择，开发了不同的预测模型。重建了伪彩色图谱，并与常规组织学程序进行了比较。预测图谱与组织学之间具有高度相关性，这证明了 FTIR 光谱在皮肤癌的鉴别诊断中的巨大潜力。
