Research progress of MRI-based radiomics in hepatocellular carcinoma
BackgroundPrimary liver cancer (PLC), notably hepatocellular carcinoma (HCC), stands as a formidable global health challenge, ranking as the sixth most prevalent malignant tumor and the third leading cause of cancer-related deaths. HCC presents a daunting clinical landscape characterized by nonspeci...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1420599/full |
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| author | Xiao-Yun Xie Rong Chen |
| author_facet | Xiao-Yun Xie Rong Chen |
| author_sort | Xiao-Yun Xie |
| collection | DOAJ |
| description | BackgroundPrimary liver cancer (PLC), notably hepatocellular carcinoma (HCC), stands as a formidable global health challenge, ranking as the sixth most prevalent malignant tumor and the third leading cause of cancer-related deaths. HCC presents a daunting clinical landscape characterized by nonspecific early symptoms and late-stage detection, contributing to its poor prognosis. Moreover, the limited efficacy of existing treatments and high recurrence rates post-surgery compound the challenges in managing this disease. While histopathologic examination remains the cornerstone for HCC diagnosis, its utility in guiding preoperative decisions is constrained. Radiomics, an emerging field, harnesses high-throughput imaging data, encompassing shape, texture, and intensity features, alongside clinical parameters, to elucidate disease characteristics through advanced computational techniques such as machine learning and statistical modeling. MRI radiomics specifically holds significant importance in the diagnosis and treatment of hepatocellular carcinoma (HCC).ObjectiveThis study aims to evaluate the methodology of radiomics and delineate the clinical advancements facilitated by MRI-based radiomics in the realm of hepatocellular carcinoma diagnosis and treatment.MethodsA systematic review of the literature was conducted, encompassing peer-reviewed articles published between July 2018 and Jan 2025, sourced from PubMed and Google Scholar. Key search terms included Hepatocellular carcinoma, HCC, Liver cancer, Magnetic resonance imaging, MRI, radiomics, deep learning, machine learning, and artificial intelligence.ResultsA comprehensive analysis of 93 articles underscores the efficacy of MRI radiomics, a noninvasive imaging analysis modality, across various facets of HCC management. These encompass tumor differentiation, subtype classification, histopathological grading, prediction of microvascular invasion (MVI), assessment of treatment response, early recurrence prognostication, and metastasis prediction.ConclusionMRI radiomics emerges as a promising adjunctive tool for early HCC detection and personalized preoperative decision-making, with the overarching goal of optimizing patient outcomes. Nevertheless, the current lack of interpretability within the field underscores the imperative for continued research and validation efforts. |
| format | Article |
| id | doaj-art-c85a81cfb47e4cbca7e4bbfe1bb488f1 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-c85a81cfb47e4cbca7e4bbfe1bb488f12025-02-06T05:21:53ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-02-011510.3389/fonc.2025.14205991420599Research progress of MRI-based radiomics in hepatocellular carcinomaXiao-Yun Xie0Rong Chen1Department of Radiation Oncology, Medical School of Southeast University, Nanjing, ChinaDepartment of Radiation Oncology, Zhongda Hospital, Nanjing, ChinaBackgroundPrimary liver cancer (PLC), notably hepatocellular carcinoma (HCC), stands as a formidable global health challenge, ranking as the sixth most prevalent malignant tumor and the third leading cause of cancer-related deaths. HCC presents a daunting clinical landscape characterized by nonspecific early symptoms and late-stage detection, contributing to its poor prognosis. Moreover, the limited efficacy of existing treatments and high recurrence rates post-surgery compound the challenges in managing this disease. While histopathologic examination remains the cornerstone for HCC diagnosis, its utility in guiding preoperative decisions is constrained. Radiomics, an emerging field, harnesses high-throughput imaging data, encompassing shape, texture, and intensity features, alongside clinical parameters, to elucidate disease characteristics through advanced computational techniques such as machine learning and statistical modeling. MRI radiomics specifically holds significant importance in the diagnosis and treatment of hepatocellular carcinoma (HCC).ObjectiveThis study aims to evaluate the methodology of radiomics and delineate the clinical advancements facilitated by MRI-based radiomics in the realm of hepatocellular carcinoma diagnosis and treatment.MethodsA systematic review of the literature was conducted, encompassing peer-reviewed articles published between July 2018 and Jan 2025, sourced from PubMed and Google Scholar. Key search terms included Hepatocellular carcinoma, HCC, Liver cancer, Magnetic resonance imaging, MRI, radiomics, deep learning, machine learning, and artificial intelligence.ResultsA comprehensive analysis of 93 articles underscores the efficacy of MRI radiomics, a noninvasive imaging analysis modality, across various facets of HCC management. These encompass tumor differentiation, subtype classification, histopathological grading, prediction of microvascular invasion (MVI), assessment of treatment response, early recurrence prognostication, and metastasis prediction.ConclusionMRI radiomics emerges as a promising adjunctive tool for early HCC detection and personalized preoperative decision-making, with the overarching goal of optimizing patient outcomes. Nevertheless, the current lack of interpretability within the field underscores the imperative for continued research and validation efforts.https://www.frontiersin.org/articles/10.3389/fonc.2025.1420599/fullMRIradiomicshepatocellular carcinomamachine learningtreatmentdiagnosis |
| spellingShingle | Xiao-Yun Xie Rong Chen Research progress of MRI-based radiomics in hepatocellular carcinoma Frontiers in Oncology MRI radiomics hepatocellular carcinoma machine learning treatment diagnosis |
| title | Research progress of MRI-based radiomics in hepatocellular carcinoma |
| title_full | Research progress of MRI-based radiomics in hepatocellular carcinoma |
| title_fullStr | Research progress of MRI-based radiomics in hepatocellular carcinoma |
| title_full_unstemmed | Research progress of MRI-based radiomics in hepatocellular carcinoma |
| title_short | Research progress of MRI-based radiomics in hepatocellular carcinoma |
| title_sort | research progress of mri based radiomics in hepatocellular carcinoma |
| topic | MRI radiomics hepatocellular carcinoma machine learning treatment diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1420599/full |
| work_keys_str_mv | AT xiaoyunxie researchprogressofmribasedradiomicsinhepatocellularcarcinoma AT rongchen researchprogressofmribasedradiomicsinhepatocellularcarcinoma |