{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:26:54Z","timestamp":1787009214568,"version":"build-2736575974"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T00:00:00Z","timestamp":1681776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100016047","name":"Science Fund of the Republic of Serbia","doi-asserted-by":"publisher","award":["7739645"],"award-info":[{"award-number":["7739645"]}],"id":[{"id":"10.13039\/501100016047","id-type":"DOI","asserted-by":"publisher"}]},{"name":"SensoFracTW"},{"name":"Ministry of Education and Science of the Republic of Serbia","award":["200110"],"award-info":[{"award-number":["200110"]}]},{"name":"Khalifa University of Science and Technology","award":["FSU-2020-25"],"award-info":[{"award-number":["FSU-2020-25"]}]},{"name":"Khalifa University of Science and Technology","award":["RC2-2018-022"],"award-info":[{"award-number":["RC2-2018-022"]}]},{"name":"College of Medicine and Health Sciences"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) analyses are two contemporary computational methods that can identify discrete changes in cell and tissue textural features. Previous research has indicated that these methods may be applicable in the pathology for identification and classification of various types of cancers. In this study, we present findings that squamous epithelial cells in laryngeal carcinoma, which appear morphologically intact during conventional pathohistological evaluation, have distinct nuclear GLCM and DWT features. The average values of nuclear GLCM indicators of these cells, such as angular second moment, inverse difference moment, and textural contrast, substantially differ when compared to those in noncancerous tissue. In this work, we also propose machine learning models based on random forests and support vector machine that can be successfully trained to separate the cells using GLCM and DWT quantifiers as input data. We show that, based on a limited cell sample, these models have relatively good classification accuracy and discriminatory power, which makes them suitable candidates for future development of AI-based sensors potentially applicable in laryngeal carcinoma diagnostic protocols.<\/jats:p>","DOI":"10.1093\/micmic\/ozad042","type":"journal-article","created":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T15:12:23Z","timestamp":1681830743000},"page":"1220-1227","source":"Crossref","is-referenced-by-count":16,"title":["Gray-Level Co-occurrence Matrix Analysis of Nuclear Textural Patterns in Laryngeal Squamous Cell Carcinoma: Focus on Artificial Intelligence Methods"],"prefix":"10.1093","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1565-8724","authenticated-orcid":false,"given":"Svetlana","family":"Valjarevic","sequence":"first","affiliation":[{"name":"University of Belgrade, Faculty of Medicine, Clinical Hospital Center \u201cZemun\u201d , Vukova 9, RS-11080 Belgrade , Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milan B","family":"Jovanovic","sequence":"additional","affiliation":[{"name":"University of Belgrade, Faculty of Medicine, Clinical Hospital Center \u201cZemun\u201d , Vukova 9, RS-11080 Belgrade , Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nenad","family":"Miladinovic","sequence":"additional","affiliation":[{"name":"University of Belgrade, Faculty of Medicine, Clinical Hospital Center \u201cZemun\u201d , Vukova 9, RS-11080 Belgrade , Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jelena","family":"Cumic","sequence":"additional","affiliation":[{"name":"University of Belgrade, Faculty of Medicine, University Clinical Centre of Serbia , Dr. Koste Todorovi\u0107a 8, RS-11129, Belgrade , Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Dugalic","sequence":"additional","affiliation":[{"name":"University of Belgrade, Faculty of Medicine, University Clinical Centre of Serbia , Dr. Koste Todorovi\u0107a 8, RS-11129, Belgrade , Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter R","family":"Corridon","sequence":"additional","affiliation":[{"name":"Department of Immunology and Physiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology , Shakhbout Bin Sultan St - Hadbat Al Za'faranah - Zone 1 - Abu Dhabi, UAE"},{"name":"Biomedical Engineering, Healthcare Engineering Innovation Center, Khalifa University of Science and Technology , Shakhbout Bin Sultan St - Hadbat Al Za'faranah - Zone 1 - Abu Dhabi, UAE"},{"name":"Center for Biotechnology, Khalifa University of Science and Technology , Shakhbout Bin Sultan St - Hadbat Al Za'faranah - Zone 1 - Abu Dhabi, UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6589-8964","authenticated-orcid":false,"given":"Igor","family":"Pantic","sequence":"additional","affiliation":[{"name":"University of Belgrade, Faculty of Medicine, Department of Medical Physiology , Vi\u0161egradska 26\/2, RS-11129 Belgrade , Serbia"},{"name":"Department of Pharmacology, College of Medicine and Health Sciences , Khalifa University of Science and Technology, Shakhbout Bin Sultan St - Hadbat Al Za'faranah - Zone 1 - Abu Dhabi, UAE"},{"name":"University of Haifa , 199 Abba Hushi Blvd, Mount Carmel, Haifa IL-3498838 , Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,4,18]]},"reference":[{"key":"2023060912165237300_ozad042-B1","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.3390\/electronics11182865","article-title":"Diagnosis of histopathological images to distinguish types of malignant lymphomas using hybrid techniques based on fusion features","volume":"11","author":"Al-Mekhlafi","year":"2022","journal-title":"Electronics"},{"issue":"4","key":"2023060912165237300_ozad042-B2","first-page":"659","article-title":"Automatic detection of the meningioma tumor firmness in MRI images","volume":"28","author":"AlKubeyyer","year":"2020","journal-title":"J Xray Sci Technol"},{"key":"2023060912165237300_ozad042-B3","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/2733965","article-title":"Ensemble learning framework with GLCM texture extraction for early detection of lung cancer on CT images","volume":"2022","author":"Althubiti","year":"2022","journal-title":"Comput Math Methods Med"},{"key":"2023060912165237300_ozad042-B4","volume-title":"Head and Neck Pathology","author":"Barnes","year":"2010"},{"issue":"6","key":"2023060912165237300_ozad042-B5","first-page":"340","article-title":"Histone acetylation and chromatin pattern in cancer: A review","volume":"30","author":"Blanca","year":"2008","journal-title":"Anal Quant Cytol Histol"},{"key":"2023060912165237300_ozad042-B6","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2023060912165237300_ozad042-B7","doi-asserted-by":"crossref","DOI":"10.1016\/j.anndiagpath.2021.151787","article-title":"Biomarkers of laryngeal squamous cell carcinoma: A review","volume":"54","author":"Cavaliere","year":"2021","journal-title":"Ann Diagn Pathol"},{"issue":"5","key":"2023060912165237300_ozad042-B8","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1016\/j.clinimag.2015.04.003","article-title":"Differentiation of true-progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide by GLCM texture analysis of conventional MRI","volume":"39","author":"Chen","year":"2015","journal-title":"Clin Imaging"},{"issue":"7414","key":"2023060912165237300_ozad042-B9","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/nature11247","article-title":"An integrated encyclopedia of DNA elements in the human genome","volume":"489","author":"Consortium","year":"2012","journal-title":"Nature"},{"issue":"10","key":"2023060912165237300_ozad042-B10","doi-asserted-by":"crossref","first-page":"2445","DOI":"10.3390\/biomedicines10102445","article-title":"Digital technologies: Advancing individualized treatments through gene and cell therapies, pharmacogenetics, and disease detection and diagnostics","volume":"10","author":"Corridon","year":"2022","journal-title":"Biomedicines"},{"issue":"1","key":"2023060912165237300_ozad042-B11","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1017\/S1431927621013878","article-title":"Gray-level co-occurrence matrix analysis for the detection of discrete, ethanol-induced, structural changes in cell nuclei: An artificial intelligence approach","volume":"28","author":"Davidovic","year":"2022","journal-title":"Microsc Microanal"},{"key":"2023060912165237300_ozad042-B12","doi-asserted-by":"crossref","DOI":"10.1016\/j.cbi.2021.109533","article-title":"Application of artificial intelligence for detection of chemico-biological interactions associated with oxidative stress and DNA damage","volume":"345","author":"Davidovic","year":"2021","journal-title":"Chem Biol Interact"},{"issue":"5","key":"2023060912165237300_ozad042-B13","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa369","article-title":"Towards deep phenotyping pregnancy: A systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes","volume":"22","author":"Davidson","year":"2021","journal-title":"Brief Bioinform"},{"issue":"4","key":"2023060912165237300_ozad042-B14","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1080\/00207454.2018.1536052","article-title":"Study of Haralick's And GLCM texture analysis on 3D medical images","volume":"129","author":"Dhruv","year":"2019","journal-title":"Int J Neurosci"},{"issue":"8","key":"2023060912165237300_ozad042-B15","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1007\/s00249-021-01574-w","article-title":"Morphological, fractal, and textural features for the blood cell classification: The case of acute myeloid leukemia","volume":"50","author":"Dincic","year":"2021","journal-title":"Eur Biophys J"},{"issue":"4","key":"2023060912165237300_ozad042-B16","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1017\/S1431927620001683","article-title":"The fractal and GLCM textural parameters of chromatin may be potential biomarkers of papillary thyroid carcinoma in Hashimoto's thyroiditis specimens","volume":"26","author":"Dincic","year":"2020","journal-title":"Microsc Microanal"},{"issue":"2","key":"2023060912165237300_ozad042-B17","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10522-017-9683-y","article-title":"A review of supervised machine learning applied to ageing research","volume":"18","author":"Fabris","year":"2017","journal-title":"Biogerontology"},{"issue":"4","key":"2023060912165237300_ozad042-B18","first-page":"333","article-title":"Methylation, gene expression and the chromatin connection in cancer (review)","volume":"4","author":"Gray","year":"1999","journal-title":"Int J Mol Med"},{"issue":"5","key":"2023060912165237300_ozad042-B19","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1111\/cyt.12787","article-title":"Fractal dimension and chromatin textural analysis to differentiate follicular carcinoma and adenoma on fine needle aspiration cytology","volume":"31","author":"Gupta","year":"2020","journal-title":"Cytopathology"},{"issue":"6","key":"2023060912165237300_ozad042-B20","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"SMC 3","author":"Haralick","year":"1973","journal-title":"IEEE Trans Syst Man Cybernet"},{"key":"2023060912165237300_ozad042-B21","doi-asserted-by":"crossref","first-page":"e536","DOI":"10.7717\/peerj-cs.536","article-title":"Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms","volume":"7","author":"Iqbal","year":"2021","journal-title":"PeerJ Comput Sci"},{"issue":"8","key":"2023060912165237300_ozad042-B22","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1002\/dc.24466","article-title":"Discriminant analysis and interpretation of nuclear chromatin distribution and coarseness using gray-level co-occurrence matrix features for lobular endocervical glandular hyperplasia","volume":"48","author":"Kanai","year":"2020","journal-title":"Diagn Cytopathol"},{"issue":"6","key":"2023060912165237300_ozad042-B23","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1007\/s12257-020-0049-y","article-title":"Artificial intelligence in drug discovery: A comprehensive review of data-driven and machine learning approaches","volume":"25","author":"Kim","year":"2020","journal-title":"Biotechnol Bioprocess Eng"},{"key":"2023060912165237300_ozad042-B24","first-page":"163","article-title":"Discrete wavelet transform\u2013derived features for digital image texture analysis","author":"Kocio\u0142ek"},{"issue":"7","key":"2023060912165237300_ozad042-B25","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1002\/cyto.a.24260","article-title":"A paradigm shift in nuclear chromatin interpretation: From qualitative intuitive recognition to quantitative texture analysis of breast cancer cell nuclei","volume":"99","author":"Lee","year":"2020","journal-title":"Cytometry A"},{"issue":"2","key":"2023060912165237300_ozad042-B26","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1080\/02664763.2018.1473348","article-title":"Spatial Bayesian modeling of GLCM with application to malignant lesion characterization","volume":"46","author":"Li","year":"2018","journal-title":"J Appl Stat"},{"issue":"3","key":"2023060912165237300_ozad042-B27","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1111\/coa.13719","article-title":"Paediatric laryngeal squamous cell carcinoma: Systematic review and pooled analysis","volume":"46","author":"Mur","year":"2021","journal-title":"Clin Otolaryngol"},{"issue":"4","key":"2023060912165237300_ozad042-B28","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1017\/S1431927619014594","article-title":"Application of gray level co-occurrence matrix algorithm for detection of discrete structural changes in cell nuclei after exposure to iron oxide nanoparticles and 6-hydroxydopamine","volume":"25","author":"Nikolovski","year":"2019","journal-title":"Microsc Microanal"},{"issue":"1","key":"2023060912165237300_ozad042-B29","first-page":"17","article-title":"Histone acetylation and chromatin pattern in cancer. A review","volume":"29","author":"Orr","year":"2007","journal-title":"Anal Quant Cytol Histol"},{"key":"2023060912165237300_ozad042-B30","doi-asserted-by":"crossref","DOI":"10.1016\/j.cbi.2021.109380","article-title":"Oxidopamine and oxidative stress: Recent advances in experimental physiology and pharmacology","volume":"336","author":"Pantic","year":"2021","journal-title":"Chem Biol Interact"},{"key":"2023060912165237300_ozad042-B31","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.jtbi.2015.01.035","article-title":"Discriminatory ability of fractal and grey level co-occurrence matrix methods in structural analysis of hippocampus layers","volume":"370","author":"Pantic","year":"2015","journal-title":"J Theor Biol"},{"issue":"11\u201312","key":"2023060912165237300_ozad042-B32","doi-asserted-by":"crossref","first-page":"718","DOI":"10.2298\/SARH181030074P","article-title":"Chromatin textural parameters of blood neutrophils are associated with stress levels in patients with recurrent depressive disorder","volume":"147","author":"Pantic","year":"2019","journal-title":"Srp Arh Celok Lek"},{"key":"2023060912165237300_ozad042-B33","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.jtbi.2016.02.038","article-title":"Fractal analysis and gray level co-occurrence matrix method for evaluation of reperfusion injury in kidney medulla","volume":"397","author":"Pantic","year":"2016","journal-title":"J Theor Biol"},{"key":"2023060912165237300_ozad042-B34","doi-asserted-by":"crossref","DOI":"10.1016\/j.cbi.2022.110269","article-title":"Artificial neural networks in contemporary toxicology research","volume":"369","author":"Pantic","year":"2023","journal-title":"Chem Biol Interact"},{"key":"2023060912165237300_ozad042-B35","doi-asserted-by":"crossref","DOI":"10.1016\/j.cbi.2022.109888","article-title":"Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art","volume":"358","author":"Pantic","year":"2022","journal-title":"Chem Biol Interact"},{"key":"2023060912165237300_ozad042-B36","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2022.797283","article-title":"Analysis of vascular architecture and parenchymal damage generated by reduced blood perfusion in decellularized porcine kidneys using a gray level co-occurrence matrix","volume":"9","author":"Pantic","year":"2022","journal-title":"Front Cardiovasc Med"},{"key":"2023060912165237300_ozad042-B37","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1017\/S1431927621000532","article-title":"Effects of iron oxide nanoparticles on structural organization of hepatocyte chromatin: Gray level co-occurrence matrix analysis","volume":"27","author":"Paunovic","year":"2021","journal-title":"Microsc Microanal"},{"key":"2023060912165237300_ozad042-B38","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"issue":"12","key":"2023060912165237300_ozad042-B39","doi-asserted-by":"crossref","first-page":"2214","DOI":"10.3174\/ajnr.A4455","article-title":"MRI texture analysis reveals bulbar abnormalities in Friedreich ataxia","volume":"36","author":"Santos","year":"2015","journal-title":"AJNR Am J Neuroradiol"},{"key":"2023060912165237300_ozad042-B40","doi-asserted-by":"crossref","DOI":"10.1016\/j.jphotochemrev.2022.100529","article-title":"Design, photophysical properties, and applications of fluorene-based fluorophores in two-photon fluorescence bioimaging: A review","volume":"52","author":"Shaya","year":"2022","journal-title":"J Photochem Photobiol C: Photochem Rev"},{"key":"2023060912165237300_ozad042-B41","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.nima.2012.09.006","article-title":"A software tool for automatic classification and segmentation of 2D\/3D medical images","volume":"702","author":"Strzelecki","year":"2013","journal-title":"Nucl Instrum Methods Phys Res A"},{"key":"2023060912165237300_ozad042-B42","first-page":"245","article-title":"Mazda\u2014A software for texture analysis","author":"Szczypinski","year":"2007"},{"issue":"1","key":"2023060912165237300_ozad042-B43","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.cmpb.2008.08.005","article-title":"MaZda-A software package for image texture analysis","volume":"94","author":"Szczypinski","year":"2009","journal-title":"Comput Methods Programs Biomed"},{"issue":"6","key":"2023060912165237300_ozad042-B44","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1109\/TMI.2019.2963177","article-title":"3D-GLCM CNN: A 3-dimensional gray-level co-occurrence matrix-based CNN model for polyp classification via CT colonography","volume":"39","author":"Tan","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"2023060912165237300_ozad042-B45","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1017\/S1431927617012806","article-title":"The effects of betaine on the nuclear fractal dimension, chromatin texture, and proliferative activity in hepatocytes in mouse model of nonalcoholic fatty liver disease","volume":"24","author":"Veskovic","year":"2018","journal-title":"Microsc Microanal"},{"key":"2023060912165237300_ozad042-B46","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.compbiomed.2015.03.033","article-title":"Computer-aided diagnosis of myocardial infarction using ultrasound images with DWT, GLCM and HOS methods: A comparative study","volume":"62","author":"Vidya","year":"2015","journal-title":"Comput Biol Med"},{"key":"2023060912165237300_ozad042-B47","article-title":"Decellularized blood vessel development: Current state-of-the-art and future directions","volume":"10","author":"Wang","year":"2022","journal-title":"Front Bioeng Biotechnol"},{"key":"2023060912165237300_ozad042-B48","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpi.2023.100196","article-title":"Predicting IHC staining classes of NF1 using features in the hematoxylin channel","volume":"14","author":"Zhang","year":"2023","journal-title":"J Pathol Inform"}],"container-title":["Microscopy and Microanalysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/mam\/article-pdf\/29\/3\/1220\/50553756\/ozad042.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/mam\/article-pdf\/29\/3\/1220\/50553756\/ozad042.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T08:18:29Z","timestamp":1686298709000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/mam\/article\/29\/3\/1220\/7127992"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,18]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,4,18]]},"published-print":{"date-parts":[[2023,6,9]]}},"URL":"https:\/\/doi.org\/10.1093\/micmic\/ozad042","relation":{},"ISSN":["1431-9276","1435-8115"],"issn-type":[{"value":"1431-9276","type":"print"},{"value":"1435-8115","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,6]]},"published":{"date-parts":[[2023,4,18]]}}}