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

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 innovati...

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Vydáno v:Laboratory investigation Ročník 91; číslo 5; s. 799 - 811
Hlavní autoři: Sebiskveradze, David, Vrabie, Valeriu, Gobinet, Cyril, Durlach, Anne, Bernard, Philippe, Ly, Elodie, Manfait, Michel, Jeannesson, Pierre, Piot, Olivier
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Inc 01.05.2011
Nature Publishing Group US
Nature Publishing Group
Elsevier (anciennement Nature Publishing Group)
Témata:
631/114/2404
631/67/1813
631/67/2329
Algorithms
Automation
Biological and medical sciences
Biotechnology
Cluster Analysis
clustering methods
Dermatology
FT–IR spectroscopy
Fundamental and applied biological sciences. Psychology
fuzziness index
fuzzy C-means
Fuzzy Logic
Humans
Investigative techniques, diagnostic techniques (general aspects)
Laboratory Medicine
Medical sciences
Medicine
Medicine & Public Health
number of clusters
Pathology
research-article
skin cancers
Skin Neoplasms - pathology
Spectroscopy, Fourier Transform Infrared
Tumors of the skin and soft tissue. Premalignant lesions
clustering methods
FT–IR spectroscopy
number of clusters
fuzziness index
skin cancers
fuzzy C-means
Biotechnology
Skin disease
Methodology
Index
Decision tree
Skin cancer
Heterogeneity
Tissue
Number
Clinical biology
Human
Automation
FT-IR spectroscopy
Cluster
Method
Malignant tumor
Algorithm
Section
Infrared spectrometry
Skin carcinoma
Skin
Cancer
ISSN:0023-6837, 1530-0307, 1530-0307
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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:0023-6837
1530-0307
1530-0307
DOI:10.1038/labinvest.2011.13