期刊
LABORATORY INVESTIGATION
卷 91, 期 5, 页码 799-811出版社
ELSEVIER SCIENCE INC
DOI: 10.1038/labinvest.2011.13
关键词
clustering methods; FT-IR spectroscopy; fuzziness index; fuzzy C-means; number of clusters; skin cancers
资金
- Institut National du Cancer (INCa), Canceropole Grand Est.
- Ligue contre le Cancer
- Comite de l'Aisne
- INSERM PNR Imagerie
- CNRS
- INCa
- Region Champagne-Ardenne
This study aims to develop a new FT-IR spectral imaging of tumoral tissue permitting a better characterization of tumor heterogeneity and tumor/surrounding tissue interface. Infrared (IR) data were acquired on 13 biopsies of paraffin-embedded human skin carcinomas. Our approach relies on an innovative fuzzy C-means (FCM)-based clustering algorithm, allowing the automatic and simultaneous estimation of the optimal FCM parameters (number of clusters K and fuzziness index m). FCM seems more suitable than classical 'hard' clusterings, as it permits the assignment of each IR spectrum to every cluster with a specific membership value. This characteristic allows differentiating the nuances in the assignment of pixels, particularly those corresponding to tumoral tissue and those located at the tumor/peritumoral tissue interface. FCM images permit to highlight a marked heterogeneity within the tumor and characterize the interconnection between tissular structures. For the infiltrative tumors, a progressive gradient in the membership values of the pixels of the invasive front was also revealed. Laboratory Investigation (2011) 91, 799-811; doi:10.1038/labinvest.2011.13; published online 28 February 2011
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