Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural Network
| Autor: | Dan Ionuț Gheonea, Cristin Constantin Vere, Mircea Șerbănescu, Letiția Adela Maria Streba, Marius Eugen Ciurea, Ion Rogoveanu, Daniel Pirici, Maria Comănescu, Costin Teodor Streba, Stelian Ştefăniţă Mogoantă |
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| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Male
medicine.medical_specialty Pathology Carcinoma Hepatocellular Article Subject H&E stain lcsh:Medicine Malignancy Fractal dimension General Biochemistry Genetics and Molecular Biology Fractal Carcinoma Humans Medicine Segmentation Neoplasm Metastasis Aged General Immunology and Microbiology Artificial neural network business.industry Liver Neoplasms lcsh:R General Medicine Middle Aged medicine.disease Fractals Hepatocellular carcinoma Female Neural Networks Computer Radiology business Research Article |
| Zdroj: | BioMed Research International, Vol 2014 (2014) BioMed Research International |
| ISSN: | 2314-6141 2314-6133 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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