Application of dynamic enhanced scanning with GD-EOB-DTPA MRI based on deep learning algorithm for lesion diagnosis in liver cancer patients
BackgroundImprovements in the clinical diagnostic use of magnetic resonance imaging (MRI) for the identification of liver disorders have been made possible by gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA). Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) technology i...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1423549/full |
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| author | Bo Liu Jinhua Yang Yifei Wu Xi Chen Xueru Wu |
| author_facet | Bo Liu Jinhua Yang Yifei Wu Xi Chen Xueru Wu |
| author_sort | Bo Liu |
| collection | DOAJ |
| description | BackgroundImprovements in the clinical diagnostic use of magnetic resonance imaging (MRI) for the identification of liver disorders have been made possible by gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA). Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) technology is in high demand.ObjectivesThe purpose of the study is to segment the liver using an enhanced multi-gradient deep convolution neural network (EMGDCNN) and to identify and categorize a localized liver lesion using a Gd-EOB-DTPA-enhanced MRI.MethodsWe provided the classifier images of the liver in five states (unenhanced, arterial, portal venous, equilibrium, and hepatobiliary) and labeled them with localized liver diseases (hepatocellular carcinoma, metastasis, hemangiomas, cysts, and scarring). The Shanghai Public Health Clinical Center ethics committee recruited 132 participants between August 2021 and February 2022. Fisher’s exact test analyses liver lesion Gd-EOB-DTPA-enhanced MRI data.ResultsOur method could identify and classify liver lesions at the same time. On average, 25 false positives and 0.6 real positives were found in the test instances. The percentage of correct answers was 0.790. AUC, sensitivity, and specificity evaluate the procedure. Our technique outperforms others in extensive testing.ConclusionEMGDCNN may identify and categorize a localized hepatic lesion in Gd-EOB-DTPA-enhanced MRI. We found that one network can detect and classify. Radiologists need higher detection capability. |
| format | Article |
| id | doaj-art-31dece6847864a5f80961015cffa21be |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-31dece6847864a5f80961015cffa21be2025-01-06T06:59:34ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011410.3389/fonc.2024.14235491423549Application of dynamic enhanced scanning with GD-EOB-DTPA MRI based on deep learning algorithm for lesion diagnosis in liver cancer patientsBo LiuJinhua YangYifei WuXi ChenXueru WuBackgroundImprovements in the clinical diagnostic use of magnetic resonance imaging (MRI) for the identification of liver disorders have been made possible by gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA). Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) technology is in high demand.ObjectivesThe purpose of the study is to segment the liver using an enhanced multi-gradient deep convolution neural network (EMGDCNN) and to identify and categorize a localized liver lesion using a Gd-EOB-DTPA-enhanced MRI.MethodsWe provided the classifier images of the liver in five states (unenhanced, arterial, portal venous, equilibrium, and hepatobiliary) and labeled them with localized liver diseases (hepatocellular carcinoma, metastasis, hemangiomas, cysts, and scarring). The Shanghai Public Health Clinical Center ethics committee recruited 132 participants between August 2021 and February 2022. Fisher’s exact test analyses liver lesion Gd-EOB-DTPA-enhanced MRI data.ResultsOur method could identify and classify liver lesions at the same time. On average, 25 false positives and 0.6 real positives were found in the test instances. The percentage of correct answers was 0.790. AUC, sensitivity, and specificity evaluate the procedure. Our technique outperforms others in extensive testing.ConclusionEMGDCNN may identify and categorize a localized hepatic lesion in Gd-EOB-DTPA-enhanced MRI. We found that one network can detect and classify. Radiologists need higher detection capability.https://www.frontiersin.org/articles/10.3389/fonc.2024.1423549/fullliver lesiondetectionand classificationGd-EOB-DTPAdeep learningmagnetic resonance |
| spellingShingle | Bo Liu Jinhua Yang Yifei Wu Xi Chen Xueru Wu Application of dynamic enhanced scanning with GD-EOB-DTPA MRI based on deep learning algorithm for lesion diagnosis in liver cancer patients Frontiers in Oncology liver lesion detection and classification Gd-EOB-DTPA deep learning magnetic resonance |
| title | Application of dynamic enhanced scanning with GD-EOB-DTPA MRI based on deep learning algorithm for lesion diagnosis in liver cancer patients |
| title_full | Application of dynamic enhanced scanning with GD-EOB-DTPA MRI based on deep learning algorithm for lesion diagnosis in liver cancer patients |
| title_fullStr | Application of dynamic enhanced scanning with GD-EOB-DTPA MRI based on deep learning algorithm for lesion diagnosis in liver cancer patients |
| title_full_unstemmed | Application of dynamic enhanced scanning with GD-EOB-DTPA MRI based on deep learning algorithm for lesion diagnosis in liver cancer patients |
| title_short | Application of dynamic enhanced scanning with GD-EOB-DTPA MRI based on deep learning algorithm for lesion diagnosis in liver cancer patients |
| title_sort | application of dynamic enhanced scanning with gd eob dtpa mri based on deep learning algorithm for lesion diagnosis in liver cancer patients |
| topic | liver lesion detection and classification Gd-EOB-DTPA deep learning magnetic resonance |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1423549/full |
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