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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Stroke is the second leading cause of death and a major cause of disability around the world, and the development of atherosclerotic plaques in the carotid arteries is generally considered the leading cause of severe cerebrovascular events. In recent years, new reports have reinforced the role of an accurate histopathological analysis of carotid plaques to perform the stratification of affected patients and proceed to the correct prevention of complications. This work proposes applying an unsupervised learning approach to analyze complex whole-slide images (WSIs) of atherosclerotic carotid plaques to allow a simple and fast examination of their most relevant features. All the code developed for the present analysis is freely available. The proposed method offers qualitative and quantitative tools to assist pathologists in examining the complexity of whole-slide images of carotid atherosclerotic plaques more effectively. Nevertheless, future studies using supervised methods should provide evidence of the correspondence between the clusters estimated using the proposed textural-based approach and the regions manually annotated by expert pathologists.

Details

Title
An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images
Author
Fraschini, Matteo 1   VIAFID ORCID Logo  ; Castagnola, Massimo 2   VIAFID ORCID Logo  ; Barberini, Luigi 3 ; Sanfilippo, Roberto 4 ; Coghe, Ferdinando 5 ; Didaci, Luca 1   VIAFID ORCID Logo  ; Cau, Riccardo 6   VIAFID ORCID Logo  ; Frongia, Claudio 1   VIAFID ORCID Logo  ; Scartozzi, Mario 7 ; Saba, Luca 6 ; Faa, Gavino 8 

 Dipartimento di Ingegneria Elettrica ed Elettronica, Università degli Studi di Cagliari, 09123 Cagliari, Italy; [email protected] (L.D.); [email protected] (C.F.) 
 Laboratorio di Proteomica, Centro Europeo di Ricerca sul Cervello, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy; [email protected] 
 Dipartimento di Scienze Mediche e Sanità Pubblica, Università degli Studi di Cagliari, 09123 Cagliari, Italy; [email protected] (L.B.); [email protected] (G.F.) 
 Dipartimento di Scienze Chirurgiche, Università degli Studi di Cagliari, 09123 Cagliari, Italy; [email protected] 
 UOC Laboratorio Analisi, AOU of Cagliari, 09123 Cagliari, Italy; [email protected] 
 Department of Radiology, Azienda Ospedaliero Universitaria, University of Cagliari, 40138 Cagliari, Italy; [email protected] (R.C.); [email protected] (L.S.) 
 Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; [email protected] 
 Dipartimento di Scienze Mediche e Sanità Pubblica, Università degli Studi di Cagliari, 09123 Cagliari, Italy; [email protected] (L.B.); [email protected] (G.F.); Department of Biology, College of Science and Technology, Temple University, Philadelphia, PA 19122, USA 
First page
5383
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3098220976
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.