Radiomics and Deep Learning as Important Techniques of Artificial Intelligence — Diagnosing Perspectives in Cytokeratin 19 Positive Hepatocellular Carcinoma
Fei Wang,1 Chunyue Yan,2 Xinlan Huang,3 Jiqiang He,1 Ming Yang,1 Deqiang Xian4 1Department of Radiology, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of China; 2Department of Emergency Medicine, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of China; 3Department of Medic...
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Dove Medical Press
2025-06-01
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| author | Wang F Yan C Huang X He J Yang M Xian D |
| author_facet | Wang F Yan C Huang X He J Yang M Xian D |
| author_sort | Wang F |
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| description | Fei Wang,1 Chunyue Yan,2 Xinlan Huang,3 Jiqiang He,1 Ming Yang,1 Deqiang Xian4 1Department of Radiology, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of China; 2Department of Emergency Medicine, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of China; 3Department of Medical Imaging, Southwest Medical University, Luzhou, 646000, People’s Republic of China; 4Department of Administrative Office, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of ChinaCorrespondence: Fei Wang, Department of Radiology, Luzhou People’s Hospital, Lu zhou, 646000, People’s Republic of China, Tel +86-0830-6681280, Email 1871904255@qq.com Deqiang Xian, Department of Administrative Office, Luzhou People’s Hospital, Lu zhou, 646000, People’s Republic of China, Email 349999828@qq.comBackground: Currently, there are inconsistencies among different studies on preoperative prediction of Cytokeratin 19 (CK19) expression in HCC using traditional imaging, radiomics, and deep learning. We aimed to systematically analyze and compare the performance of non-invasive methods for predicting CK19-positive HCC, thereby providing insights for the stratified management of HCC patients.Methods: A comprehensive literature search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane Library from inception to February 2025. Two investigators independently screened and extracted data based on inclusion and exclusion criteria. Eligible studies were included, and key findings were summarized in tables to provide a clear overview.Results: Ultimately, 22 studies involving 3395 HCC patients were included. 72.7% (16/22) focused on traditional imaging, 36.4% (8/22) on radiomics, 9.1% (2/22) on deep learning, and 54.5% (12/22) on combined models. The magnetic resonance imaging was the most commonly used imaging modality (19/22), and over half of the studies (12/22) were published between 2022 and 2025. Moreover, 27.3% (6/22) were multicenter studies, 36.4% (8/22) included a validation set, and only 13.6% (3/22) were prospective. The area under the curve (AUC) range of using clinical and traditional imaging was 0.560 to 0.917. The AUC ranges of radiomics were 0.648 to 0.951, and the AUC ranges of deep learning were 0.718 to 0.820. Notably, the AUC ranges of combined models of clinical, imaging, radiomics and deep learning were 0.614 to 0.995. Nevertheless, the multicenter external data were limited, with only 13.6% (3/22) incorporating validation.Conclusion: The combined model integrating traditional imaging, radiomics and deep learning achieves excellent potential and performance for predicting CK19 in HCC. Based on current limitations, future research should focus on building an easy-to-use dynamic online tool, combining multicenter-multimodal imaging and advanced deep learning approaches to enhance the accuracy and robustness of model predictions.Keywords: hepatocellular carcinoma, cytokeratin 19, radiomics, deep learning, artificial intelligence, systematic review |
| format | Article |
| id | doaj-art-4fcb24dd6f0f43f0b8ea8d7f6c2ab79f |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-06-01 |
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| series | Journal of Hepatocellular Carcinoma |
| spelling | doaj-art-4fcb24dd6f0f43f0b8ea8d7f6c2ab79f2025-08-20T03:32:37ZengDove Medical PressJournal of Hepatocellular Carcinoma2253-59692025-06-01Volume 12Issue 111291140103602Radiomics and Deep Learning as Important Techniques of Artificial Intelligence — Diagnosing Perspectives in Cytokeratin 19 Positive Hepatocellular CarcinomaWang F0Yan C1Huang X2He J3Yang M4Xian D5Department of RadiologyDepartment of Emergency MedicineDepartment of Medical ImagingDepartment of RadiologyDepartment of RadiologyDepartment of Administrative OfficeFei Wang,1 Chunyue Yan,2 Xinlan Huang,3 Jiqiang He,1 Ming Yang,1 Deqiang Xian4 1Department of Radiology, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of China; 2Department of Emergency Medicine, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of China; 3Department of Medical Imaging, Southwest Medical University, Luzhou, 646000, People’s Republic of China; 4Department of Administrative Office, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of ChinaCorrespondence: Fei Wang, Department of Radiology, Luzhou People’s Hospital, Lu zhou, 646000, People’s Republic of China, Tel +86-0830-6681280, Email 1871904255@qq.com Deqiang Xian, Department of Administrative Office, Luzhou People’s Hospital, Lu zhou, 646000, People’s Republic of China, Email 349999828@qq.comBackground: Currently, there are inconsistencies among different studies on preoperative prediction of Cytokeratin 19 (CK19) expression in HCC using traditional imaging, radiomics, and deep learning. We aimed to systematically analyze and compare the performance of non-invasive methods for predicting CK19-positive HCC, thereby providing insights for the stratified management of HCC patients.Methods: A comprehensive literature search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane Library from inception to February 2025. Two investigators independently screened and extracted data based on inclusion and exclusion criteria. Eligible studies were included, and key findings were summarized in tables to provide a clear overview.Results: Ultimately, 22 studies involving 3395 HCC patients were included. 72.7% (16/22) focused on traditional imaging, 36.4% (8/22) on radiomics, 9.1% (2/22) on deep learning, and 54.5% (12/22) on combined models. The magnetic resonance imaging was the most commonly used imaging modality (19/22), and over half of the studies (12/22) were published between 2022 and 2025. Moreover, 27.3% (6/22) were multicenter studies, 36.4% (8/22) included a validation set, and only 13.6% (3/22) were prospective. The area under the curve (AUC) range of using clinical and traditional imaging was 0.560 to 0.917. The AUC ranges of radiomics were 0.648 to 0.951, and the AUC ranges of deep learning were 0.718 to 0.820. Notably, the AUC ranges of combined models of clinical, imaging, radiomics and deep learning were 0.614 to 0.995. Nevertheless, the multicenter external data were limited, with only 13.6% (3/22) incorporating validation.Conclusion: The combined model integrating traditional imaging, radiomics and deep learning achieves excellent potential and performance for predicting CK19 in HCC. Based on current limitations, future research should focus on building an easy-to-use dynamic online tool, combining multicenter-multimodal imaging and advanced deep learning approaches to enhance the accuracy and robustness of model predictions.Keywords: hepatocellular carcinoma, cytokeratin 19, radiomics, deep learning, artificial intelligence, systematic reviewhttps://www.dovepress.com/radiomics-and-deep-learning-as-important-techniques-of-artificial-inte-peer-reviewed-fulltext-article-JHCHepatocellular carcinomaCytokeratin 19RadiomicsDeep learningArtificial intelligenceSystematic review |
| spellingShingle | Wang F Yan C Huang X He J Yang M Xian D Radiomics and Deep Learning as Important Techniques of Artificial Intelligence — Diagnosing Perspectives in Cytokeratin 19 Positive Hepatocellular Carcinoma Journal of Hepatocellular Carcinoma Hepatocellular carcinoma Cytokeratin 19 Radiomics Deep learning Artificial intelligence Systematic review |
| title | Radiomics and Deep Learning as Important Techniques of Artificial Intelligence — Diagnosing Perspectives in Cytokeratin 19 Positive Hepatocellular Carcinoma |
| title_full | Radiomics and Deep Learning as Important Techniques of Artificial Intelligence — Diagnosing Perspectives in Cytokeratin 19 Positive Hepatocellular Carcinoma |
| title_fullStr | Radiomics and Deep Learning as Important Techniques of Artificial Intelligence — Diagnosing Perspectives in Cytokeratin 19 Positive Hepatocellular Carcinoma |
| title_full_unstemmed | Radiomics and Deep Learning as Important Techniques of Artificial Intelligence — Diagnosing Perspectives in Cytokeratin 19 Positive Hepatocellular Carcinoma |
| title_short | Radiomics and Deep Learning as Important Techniques of Artificial Intelligence — Diagnosing Perspectives in Cytokeratin 19 Positive Hepatocellular Carcinoma |
| title_sort | radiomics and deep learning as important techniques of artificial intelligence amp mdash diagnosing perspectives in cytokeratin 19 positive hepatocellular carcinoma |
| topic | Hepatocellular carcinoma Cytokeratin 19 Radiomics Deep learning Artificial intelligence Systematic review |
| url | https://www.dovepress.com/radiomics-and-deep-learning-as-important-techniques-of-artificial-inte-peer-reviewed-fulltext-article-JHC |
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