Literature DB >> 16462732

Quantitative nuclear texture features analysis confirms WHO classification 2004 for lung carcinomas.

Katharina Schmid1, Nina Angerstein, Silvana Geleff, Andreas Gschwendtner.   

Abstract

The purpose of this study was to discriminate the main subsets of lung carcinomas of the WHO classification of 2004 by nuclear chromatin texture feature analysis. Our collective comprised 56 typical and 19 atypical carcinoids, 37 small-cell carcinomas, 15 large-cell neuroendocrine carcinomas, 42 adenocarcinomas, and 26 squamous cell carcinomas. After Feulgen staining, cell nuclei were automatically measured using a high-resolution image analyser (CytoSavant Oncometrics, Vancouver, BC, Canada). Texture features describing the granularity and the compactness of the nuclear chromatin were extracted for calculation of classification rules, which allowed the discrimination of different tumor groups. By applying the classification rule that described the granularity of the nuclear chromatin (defined by four different parameters) small-cell and non-small-cell lung carcinoma could correctly be discriminated in 93%. No significant discrimination was possible between the different subtypes of large-cell carcinomas, including large-cell neuroendocrine carcinoma. When using compactness of chromatin (defined by four texture parameters) as a means of discrimination, carcinoids and non-small-cell lung carcinomas were correctly distinguished in 92%. No significant discrimination between neuroendocrine tumors was achieved though. Our findings are in accordance with the new WHO classification of 2004: neuroendocrine tumors of the lung are now classified according only to their mitotic counts and presence of necrosis but not by their morphology; their discrimination by the means of nuclear image analysis is not sufficient and therefore not appropriate any longer.

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Year:  2006        PMID: 16462732     DOI: 10.1038/modpathol.3800541

Source DB:  PubMed          Journal:  Mod Pathol        ISSN: 0893-3952            Impact factor:   7.842


  8 in total

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Authors:  Oncel Tuzel; Lin Yang; Peter Meer; David J Foran
Journal:  Pattern Anal Appl       Date:  2007-10-01       Impact factor: 2.580

3.  Histology image analysis for carcinoma detection and grading.

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4.  Prediction of non-muscle invasive bladder cancer recurrence using machine learning of quantitative nuclear features.

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Journal:  Mod Pathol       Date:  2021-10-29       Impact factor: 7.842

5.  Constitutive over expression of IL-1β, IL-6, NF-κB, and Stat3 is a potential cause of lung tumorgenesis in urethane (ethyl carbamate) induced Balb/c mice.

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Journal:  J Carcinog       Date:  2012-07-24

6.  Automated detection of regions of interest for tissue microarray experiments: an image texture analysis.

Authors:  Bilge Karaçali; Aydin Tözeren
Journal:  BMC Med Imaging       Date:  2007-03-09       Impact factor: 1.930

7.  Automated recognition of cell phenotypes in histology images based on membrane- and nuclei-targeting biomarkers.

Authors:  Bilge Karaçali; Alexandra P Vamvakidou; Aydin Tözeren
Journal:  BMC Med Imaging       Date:  2007-09-06       Impact factor: 1.930

8.  Nuclear DNA methylation and chromatin condensation phenotypes are distinct between normally proliferating/aging, rapidly growing/immortal, and senescent cells.

Authors:  Jin Ho Oh; Arkadiusz Gertych; Jian Tajbakhsh
Journal:  Oncotarget       Date:  2013-03
  8 in total

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