Automatic Diagnosis of Hepatocellular Carcinoma and Metastases Based on Computed Tomography Images.
Liver cancer, a leading cause of cancer mortality, is often diagnosed by analyzing the grayscale variations in liver tissue across different computed tomography (CT) images. However, the intensity similarity can be strong, making it difficult for radiologists to visually identify hepatocellular carc...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 873 - 887 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184081720&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081720 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081720 184081720 184081720 10.1007/s10278-024-01192-w 184081720 ppf: 873 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Diagnosis of Hepatocellular Carcinoma and Metastases Based on Computed Tomography Images. aug: au: Zossou, Vincent-Béni Sèna Rodrigue Gnangnon, Freddy Houéhanou Biaou, Olivier de Vathaire, Florent Allodji, Rodrigue S. Ezin, Eugène C. affil: https://ror.org/01ed4t417 Université Paris-Saclay, UVSQ, Univ. Paris-Sud, CESP, Équipe Radiation Epidemiology, 94805, Villejuif, France sug: subj: Carcinoma, Hepatocellular Diagnosis Neoplasm Metastasis Diagnosis Tomography, X-Ray Computed Methods Automation Human Funding Source Sensitivity and Specificity Neoplasm Invasiveness Convolutional Neural Networks Utilization Deep Learning Image Processing, Computer Assisted Descriptive Statistics McNemar's Test T-Tests ab: Liver cancer, a leading cause of cancer mortality, is often diagnosed by analyzing the grayscale variations in liver tissue across different computed tomography (CT) images. However, the intensity similarity can be strong, making it difficult for radiologists to visually identify hepatocellular carcinoma (HCC) and metastases. It is crucial for the management and prevention strategies to accurately differentiate between these two liver cancers. This study proposes an automated system using a convolutional neural network (CNN) to enhance diagnostic accuracy to detect HCC, metastasis, and healthy liver tissue. This system incorporates automatic segmentation and classification. The liver lesions segmentation model is implemented using residual attention U-Net. A 9-layer CNN classifier implements the lesions classification model. Its input is the combination of the results of the segmentation model with original images. The dataset included 300 patients, with 223 used to develop the segmentation model and 77 to test it. These 77 patients also served as inputs for the classification model, consisting of 20 HCC cases, 27 with metastasis, and 30 healthy. The system achieved a mean Dice score of 87.65 % in segmentation and a mean accuracy of 93.97 % in classification, both in the test phase. The proposed method is a preliminary study with great potential in helping radiologists diagnose liver cancers. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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