The Fractal and GLCM Textural Parameters of Chromatin May Be Potential Biomarkers of Papillary Thyroid Carcinoma in Hashimoto's Thyroiditis Specimens

Dinčić, Marko; Todorović, Jovan; Ostojic, Jelena Nesovic; Kovačević, Sanjin; Dunđerović, Duško; Lopičić, Srđan; Spasić, Svetolik; Radojević-Škodrić, Sanja; Stanisavljević, Dejana; Ilić, Aleksandra

doi:10.1017/s1431927620001683

Cited by 13 publications

(12 citation statements)

References 60 publications

(111 reference statements)

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“…alterations (Martinovic et al, 2018;Vesković et al, 2018), and development or progression of neoplasms (Adam et al, 2006;Bedin et al, 2010;Metze, 2010;Ferro et al, 2011;Metze et al, 2012Metze et al, , 2019Dinčić et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Fractality is a general organization principle of the cell nucleus and is present at different levels of the nuclear architecture and functions (Metze, 2010, 2013; Metze et al, 2009, 2019). The fractal features of chromatin change with cell differentiation (Chalut et al, 2011), apoptosis (Pantic et al, 2012), treatment with nanoparticles (Pantic et al, 2013 a ), aging (Pantic et al, 2013 b , 2013 c , 2016, 2017), metabolic alterations (Martinovic et al, 2018; Vesković et al, 2018), and development or progression of neoplasms (Adam et al, 2006; Bedin et al, 2010; Metze, 2010; Ferro et al, 2011; Metze et al, 2012, 2019; Dinčić et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

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The Fractal Dimension Suggests Two Chromatin Configurations in Small Cell Neuroendocrine Lung Cancer and Is an Independent Unfavorable Prognostic Factor for Overall Survival

et al. 2022

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Experimental studies have shown that in small cell neuroendocrine lung carcinomas (SCLC) global opening of the chromatin structure is associated with a higher transcription activity and increase of tumor aggressiveness and metastasis. The study of the fractal characteristics (FD) of nuclear chromatin has been widely used to describe the cell nuclear texture and its changes correspond to changes in nuclear metabolic and transcription activity. Hence, we investigated whether the nuclear fractal dimension could be a prognostic factor in SCLC. Hematoxylin-eosin stained brush cytology slides from 49 patients with SCLC were retrieved from our files. The chromatin (FD) was calculated in digitalized and interactively segmented nuclei using a differential box-counting method. The 3,575 nuclei studied showed a bimodal distribution (peaks at FD1 = 2.115 and FD2 = 2.180). The 75 percentile of the FD was an independent unfavorable prognostic factor for overall survival when tested together with ECOG (Eastern Cooperative Oncology Group) performance status, tumor extension, and therapy in a multivariate Cox regression. Our study corroborates the concept of two main chromatin configurations in small cell neuroendocrine carcinomas and that globally more open chromatin indicates a higher risk of metastasis and therefore a shorter survival of the patient.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The Fractal Dimension Suggests Two Chromatin Configurations in Small Cell Neuroendocrine Lung Cancer and Is an Independent Unfavorable Prognostic Factor for Overall Survival

et al. 2022

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“…This parameter measures the dispersion of the gray-level intensity distribution considering the GLCM mean. For additional information on the GLCM protocol, the reader is referred to the previously published data (Ou et al, 2014; Mapayi et al, 2015; Santos et al, 2015; Tan et al, 2016; Dinčić et al, 2020; Tan et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach

et al. 2021

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Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.

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“…Textural indicators are computed based on higher mathematical operations and the second order statistical analysis is often required (16)(17)(18)(19). Gray level co-occurrence matrix approach is often used for this although today there are many different alternatives.…”

Section: Fractal and Textural Indicatorsmentioning

confidence: 99%

“…horizontal, vertical, diagonal). Probably most important textural features that are subsequently calculated are angular second moment, inverse difference moment, entropy, and contrast (16,17). Angular second moment is often a representation of textural uniformity, whereas inverse difference moment greatly depends on the local homogeneity of resolution units.…”

Section: Fractal and Textural Indicatorsmentioning

confidence: 99%

Application of fractal and textural analysis in medical physiology, pathophysiology and pathology

Pantić¹,

Paunovic²,

Radojević-Škodrić³

2022

Medicinska istraživanja

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Fractal and textural analyses represent a rapidly developing class of computational and mathematical methods with potential wide applications in medicine and biology. In recent years, they have been successfully used for the evaluation of subtle alterations in cell and tissue morphology associated with various physiological and pathological processes. It has been shown that cells in early stages of apoptosis exhibit changes in chromatin fractal and textural features. Cellular senescence is also sometimes associated with changes in textural patterns in some cell populations. So far, artificial intelligence approaches based on co-occurrence matrix textural data were successfully implemented in predicting cell damage in in vitro conditions, with artificial neural networks achieving the best performance. In the future, several methodological issues and challenges related to the use of fractal and textural methods will have to be resolved before their introduction into contemporary clinical practice. This concise review focuses on the recent research on the application of fractal and textural methods in experimental physiology and related fields.

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The Fractal and GLCM Textural Parameters of Chromatin May Be Potential Biomarkers of Papillary Thyroid Carcinoma in Hashimoto's Thyroiditis Specimens

Abstract: Abstract

Cited by 13 publications

References 60 publications

The Fractal Dimension Suggests Two Chromatin Configurations in Small Cell Neuroendocrine Lung Cancer and Is an Independent Unfavorable Prognostic Factor for Overall Survival

The Fractal Dimension Suggests Two Chromatin Configurations in Small Cell Neuroendocrine Lung Cancer and Is an Independent Unfavorable Prognostic Factor for Overall Survival

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach

Application of fractal and textural analysis in medical physiology, pathophysiology and pathology

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