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Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural Network
Background and Aims. Hepatocellular carcinoma (HCC) remains a leading cause of death by cancer worldwide. Computerized diagnosis systems relying on novel imaging markers gained significant importance in recent years. Our aim was to integrate a novel morphometric measurement—the fractal dimension (FD...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4084678/ https://www.ncbi.nlm.nih.gov/pubmed/25025042 http://dx.doi.org/10.1155/2014/239706 |
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author | Gheonea, Dan Ionuț Streba, Costin Teodor Vere, Cristin Constantin Șerbănescu, Mircea Pirici, Daniel Comănescu, Maria Streba, Letiția Adela Maria Ciurea, Marius Eugen Mogoantă, Stelian Rogoveanu, Ion |
author_facet | Gheonea, Dan Ionuț Streba, Costin Teodor Vere, Cristin Constantin Șerbănescu, Mircea Pirici, Daniel Comănescu, Maria Streba, Letiția Adela Maria Ciurea, Marius Eugen Mogoantă, Stelian Rogoveanu, Ion |
author_sort | Gheonea, Dan Ionuț |
collection | PubMed |
description | Background and Aims. Hepatocellular carcinoma (HCC) remains a leading cause of death by cancer worldwide. Computerized diagnosis systems relying on novel imaging markers gained significant importance in recent years. Our aim was to integrate a novel morphometric measurement—the fractal dimension (FD)—into an artificial neural network (ANN) designed to diagnose HCC. Material and Methods. The study included 21 HCC and 28 liver metastases (LM) patients scheduled for surgery. We performed hematoxylin staining for cell nuclei and CD31/34 immunostaining for vascular elements. We captured digital images and used an in-house application to segment elements of interest; FDs were calculated and fed to an ANN which classified them as malignant or benign, further identifying HCC and LM cases. Results. User intervention corrected segmentation errors and fractal dimensions were calculated. ANNs correctly classified 947/1050 HCC images (90.2%), 1021/1050 normal tissue images (97.23%), 1215/1400 LM (86.78%), and 1372/1400 normal tissues (98%). We obtained excellent interobserver agreement between human operators and the system. Conclusion. We successfully implemented FD as a morphometric marker in a decision system, an ensemble of ANNs designed to differentiate histological images of normal parenchyma from malignancy and classify HCCs and LMs. |
format | Online Article Text |
id | pubmed-4084678 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-40846782014-07-14 Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural Network Gheonea, Dan Ionuț Streba, Costin Teodor Vere, Cristin Constantin Șerbănescu, Mircea Pirici, Daniel Comănescu, Maria Streba, Letiția Adela Maria Ciurea, Marius Eugen Mogoantă, Stelian Rogoveanu, Ion Biomed Res Int Research Article Background and Aims. Hepatocellular carcinoma (HCC) remains a leading cause of death by cancer worldwide. Computerized diagnosis systems relying on novel imaging markers gained significant importance in recent years. Our aim was to integrate a novel morphometric measurement—the fractal dimension (FD)—into an artificial neural network (ANN) designed to diagnose HCC. Material and Methods. The study included 21 HCC and 28 liver metastases (LM) patients scheduled for surgery. We performed hematoxylin staining for cell nuclei and CD31/34 immunostaining for vascular elements. We captured digital images and used an in-house application to segment elements of interest; FDs were calculated and fed to an ANN which classified them as malignant or benign, further identifying HCC and LM cases. Results. User intervention corrected segmentation errors and fractal dimensions were calculated. ANNs correctly classified 947/1050 HCC images (90.2%), 1021/1050 normal tissue images (97.23%), 1215/1400 LM (86.78%), and 1372/1400 normal tissues (98%). We obtained excellent interobserver agreement between human operators and the system. Conclusion. We successfully implemented FD as a morphometric marker in a decision system, an ensemble of ANNs designed to differentiate histological images of normal parenchyma from malignancy and classify HCCs and LMs. Hindawi Publishing Corporation 2014 2014-06-16 /pmc/articles/PMC4084678/ /pubmed/25025042 http://dx.doi.org/10.1155/2014/239706 Text en Copyright © 2014 Dan Ionu Gheonea et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Gheonea, Dan Ionuț Streba, Costin Teodor Vere, Cristin Constantin Șerbănescu, Mircea Pirici, Daniel Comănescu, Maria Streba, Letiția Adela Maria Ciurea, Marius Eugen Mogoantă, Stelian Rogoveanu, Ion Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural Network |
title | Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural Network |
title_full | Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural Network |
title_fullStr | Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural Network |
title_full_unstemmed | Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural Network |
title_short | Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural Network |
title_sort | diagnosis system for hepatocellular carcinoma based on fractal dimension of morphometric elements integrated in an artificial neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4084678/ https://www.ncbi.nlm.nih.gov/pubmed/25025042 http://dx.doi.org/10.1155/2014/239706 |
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