Differential diagnosis of cutaneous carcinomas by infrared spectral micro-imaging combined with pattern recognition
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...
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| Veröffentlicht in: | Analyst (London) Jg. 134; H. 6; S. 1208 |
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01.06.2009
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| Abstract | 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. |
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| AbstractList | 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.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. 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. |
| Author | Durlach, Anne Manfait, Michel Bernard, Philippe Piot, Olivier Ly, Elodie |
| Author_xml | – sequence: 1 givenname: Elodie surname: Ly fullname: Ly, Elodie organization: Unité MéDIAN CNRS UMR 6237 MEDyC, Université Reims-Champagne Ardenne, Faculté de Pharmacie, 51 rue Cognacq-Jay, 51096 REIMS CEDEX, France – sequence: 2 givenname: Olivier surname: Piot fullname: Piot, Olivier – sequence: 3 givenname: Anne surname: Durlach fullname: Durlach, Anne – sequence: 4 givenname: Philippe surname: Bernard fullname: Bernard, Philippe – sequence: 5 givenname: Michel surname: Manfait fullname: Manfait, Michel |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19475150$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Algorithms Diagnosis, Differential Discriminant Analysis Eccrine Glands - pathology Hair Follicle - pathology Humans Image Interpretation, Computer-Assisted Pattern Recognition, Automated Principal Component Analysis Reproducibility of Results Sebaceous Glands - pathology Skin Neoplasms - diagnosis Skin Neoplasms - pathology Spectroscopy, Fourier Transform Infrared |
| Title | Differential diagnosis of cutaneous carcinomas by infrared spectral micro-imaging combined with pattern recognition |
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