A novel computer-aided diagnostic system for accurate detection and grading of liver tumors

Abstract Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect...

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Autores principales:	Ahmed Alksas, Mohamed Shehata, Gehad A. Saleh, Ahmed Shaffie, Ahmed Soliman, Mohammed Ghazal, Adel Khelifi, Hadil Abu Khalifeh, Ahmed Abdel Razek, Guruprasad A. Giridharan, Ayman El-Baz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/274eb2a8344a4ff3a796fca31c71bdc9
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spelling	oai:doaj.org-article:274eb2a8344a4ff3a796fca31c71bdc92021-12-02T17:12:18ZA novel computer-aided diagnostic system for accurate detection and grading of liver tumors10.1038/s41598-021-91634-02045-2322https://doaj.org/article/274eb2a8344a4ff3a796fca31c71bdc92021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91634-0https://doaj.org/toc/2045-2322Abstract Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect and grade liver tumors non-invasively. A total of 95 patients with liver tumors (M = 65, F = 30, age range = 34–82 years) were enrolled in the study after consents were obtained. 38 patients had benign tumors (LR1 = 19 and LR2 = 19), 19 patients had intermediate tumors (LR3), and 38 patients had hepatocellular carcinoma (HCC) malignant tumors (LR4 = 19 and LR5 = 19). A multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) was collected to extract the imaging markers. A comprehensive CAD system was developed, which includes the following main steps: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of textural markers using a novel rotation invariant gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) models, and iii) calculation of the functional markers by estimating the wash-in/wash-out slopes, which enable quantification of the enhancement characteristics across different CE-MR phases. These markers were subsequently processed using a two-stages random forest-based classifier to classify the liver tumor as benign, intermediate, or malignant and determine the corresponding grade (LR1, LR2, LR3, LR4, or LR5). The overall CAD system using all the identified imaging markers achieved a sensitivity of 91.8%±0.9%, specificity of 91.2%±1.9%, and F $$_{1}$$ 1 score of 0.91±0.01, using the leave-one-subject-out (LOSO) cross-validation approach. Importantly, the CAD system achieved overall accuracies of $$88\%\pm 5\%$$ 88 % ± 5 % , 85%±2%, 78%±3%, 83%±4%, and 79%±3% in grading liver tumors into LR1, LR2, LR3, LR4, and LR5, respectively. In addition to LOSO, the developed CAD system was tested using randomly stratified 10-fold and 5-fold cross-validation approaches. Alternative classification algorithms, including support vector machine, naive Bayes classifier, k-nearest neighbors, and linear discriminant analysis all produced inferior results compared to the proposed two stage random forest classification model. These experiments demonstrate the feasibility of the proposed CAD system as a novel tool to objectively assess liver tumors based on the new comprehensive imaging markers. The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.Ahmed AlksasMohamed ShehataGehad A. SalehAhmed ShaffieAhmed SolimanMohammed GhazalAdel KhelifiHadil Abu KhalifehAhmed Abdel RazekGuruprasad A. GiridharanAyman El-BazNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ahmed Alksas Mohamed Shehata Gehad A. Saleh Ahmed Shaffie Ahmed Soliman Mohammed Ghazal Adel Khelifi Hadil Abu Khalifeh Ahmed Abdel Razek Guruprasad A. Giridharan Ayman El-Baz A novel computer-aided diagnostic system for accurate detection and grading of liver tumors
description	Abstract Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect and grade liver tumors non-invasively. A total of 95 patients with liver tumors (M = 65, F = 30, age range = 34–82 years) were enrolled in the study after consents were obtained. 38 patients had benign tumors (LR1 = 19 and LR2 = 19), 19 patients had intermediate tumors (LR3), and 38 patients had hepatocellular carcinoma (HCC) malignant tumors (LR4 = 19 and LR5 = 19). A multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) was collected to extract the imaging markers. A comprehensive CAD system was developed, which includes the following main steps: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of textural markers using a novel rotation invariant gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) models, and iii) calculation of the functional markers by estimating the wash-in/wash-out slopes, which enable quantification of the enhancement characteristics across different CE-MR phases. These markers were subsequently processed using a two-stages random forest-based classifier to classify the liver tumor as benign, intermediate, or malignant and determine the corresponding grade (LR1, LR2, LR3, LR4, or LR5). The overall CAD system using all the identified imaging markers achieved a sensitivity of 91.8%±0.9%, specificity of 91.2%±1.9%, and F $$_{1}$$ 1 score of 0.91±0.01, using the leave-one-subject-out (LOSO) cross-validation approach. Importantly, the CAD system achieved overall accuracies of $$88\%\pm 5\%$$ 88 % ± 5 % , 85%±2%, 78%±3%, 83%±4%, and 79%±3% in grading liver tumors into LR1, LR2, LR3, LR4, and LR5, respectively. In addition to LOSO, the developed CAD system was tested using randomly stratified 10-fold and 5-fold cross-validation approaches. Alternative classification algorithms, including support vector machine, naive Bayes classifier, k-nearest neighbors, and linear discriminant analysis all produced inferior results compared to the proposed two stage random forest classification model. These experiments demonstrate the feasibility of the proposed CAD system as a novel tool to objectively assess liver tumors based on the new comprehensive imaging markers. The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.
format	article
author	Ahmed Alksas Mohamed Shehata Gehad A. Saleh Ahmed Shaffie Ahmed Soliman Mohammed Ghazal Adel Khelifi Hadil Abu Khalifeh Ahmed Abdel Razek Guruprasad A. Giridharan Ayman El-Baz
author_facet	Ahmed Alksas Mohamed Shehata Gehad A. Saleh Ahmed Shaffie Ahmed Soliman Mohammed Ghazal Adel Khelifi Hadil Abu Khalifeh Ahmed Abdel Razek Guruprasad A. Giridharan Ayman El-Baz
author_sort	Ahmed Alksas
title	A novel computer-aided diagnostic system for accurate detection and grading of liver tumors
title_short	A novel computer-aided diagnostic system for accurate detection and grading of liver tumors
title_full	A novel computer-aided diagnostic system for accurate detection and grading of liver tumors
title_fullStr	A novel computer-aided diagnostic system for accurate detection and grading of liver tumors
title_full_unstemmed	A novel computer-aided diagnostic system for accurate detection and grading of liver tumors
title_sort	novel computer-aided diagnostic system for accurate detection and grading of liver tumors
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/274eb2a8344a4ff3a796fca31c71bdc9
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A novel computer-aided diagnostic system for accurate detection and grading of liver tumors

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