2021
DOI: 10.1038/s41598-021-91634-0
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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 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 L… Show more

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Cited by 38 publications
(21 citation statements)
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“…Of these 27 prominent texture contributors, 7 were GLCM features and 9 were the GLRLM features. These GLCM and GLRLM features represented gray value changes between pixels and could reflect the complexity and heterogeneity of lesions ( 23 ), demonstrating a great ability to differentiate between benign, intermediate, and malignant tumors because the malignant tumors had more inhomogeneous internal structures than the benign and intermediate lesions ( 24 ). Moreover, cancer cells have rapid and overcrowded growth patterns as well as an insufficient supply of blood and oxygen, which causes cell hypoxia and the formation of various types of neovasculature.…”
Section: Discussionmentioning
confidence: 99%
“…Of these 27 prominent texture contributors, 7 were GLCM features and 9 were the GLRLM features. These GLCM and GLRLM features represented gray value changes between pixels and could reflect the complexity and heterogeneity of lesions ( 23 ), demonstrating a great ability to differentiate between benign, intermediate, and malignant tumors because the malignant tumors had more inhomogeneous internal structures than the benign and intermediate lesions ( 24 ). Moreover, cancer cells have rapid and overcrowded growth patterns as well as an insufficient supply of blood and oxygen, which causes cell hypoxia and the formation of various types of neovasculature.…”
Section: Discussionmentioning
confidence: 99%
“…Among the studies on this topic, Alksas et al suggested the possibility of Computer Assisted Diagnosis (CAD) for LI-RADS using more detailed analysis (i.e., functional markers, texture analysis, and morphological markers). Their CAD system distinguished between benign, intermediate, and malignant hepatocellular carcinoma and also allowed for the classification of subtypes, which was previously considered difficult (12). In this study, we obtained higher-contrast, multitemporal TICs in the prototype sequence, which enabled us to perform detailed analysis.…”
Section: Discussionmentioning
confidence: 99%
“…All these models could in the future increase the potential diagnostic sensitivity and accuracy of computational methods such as GLCM in pathology and related fields. 33 , 34 …”
Section: Discussionmentioning
confidence: 99%