Automation of an algorithm based on fuzzy clustering for analyzing tumoral heterogeneity in human skin carcinoma tissue sections

• DOI: 10.1038/labinvest.2011.13
• Publication Date: 2011-03-01T22:04:42Z
• Authors: David Sebiskveradze; Valeriu Vrabie; Cyril Gobinet; Anne Durlach; Philippe Bernard; Elodie Ly; Michel Manfait; Pierre Jeannesson; Olivier Piot
• Keywords: Tumor Heterogeneity; Interface (matter)

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.