Multimodal Ultrasound Radiomics in Liver Disease: Current Status and Future Directions
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| Názov: | Multimodal Ultrasound Radiomics in Liver Disease: Current Status and Future Directions |
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| Autori: | Zhong Xian, Xie Xiaoyan |
| Zdroj: | Advanced Ultrasound in Diagnosis and Therapy, Vol 9, Iss 4, Pp 388-408 (2025) |
| Informácie o vydavateľovi: | Editorial Office of Advanced Ultrasound in Diagnosis and Therapy, 2025. |
| Rok vydania: | 2025 |
| Zbierka: | LCC:Medical technology LCC:Medicine |
| Predmety: | handcrafted radiomics, deep learning, contrast-enhanced ultrasound, shear wave elastography, liver disease, Medical technology, R855-855.5, Medicine |
| Popis: | Multimodal ultrasound, including B-mode imaging, contrast-enhanced ultrasound (CEUS), and ultrasound-based elastography, has demonstrated significant value in evaluating both diffuse liver diseases such as fibrosis and steatosis, and focal liver lesions such as hepatocellular carcinoma (HCC). Radiomics, including both handcrafted radiomics and deep learning approaches, has emerged as a promising strategy to enhance ultrasound-based liver disease assessment. Recent studies have applied radiomics across multimodal ultrasound, achieving notable success in grading fatty liver disease, staging fibrosis, and improving diagnosis, risk stratification, and prognostic prediction in HCC. Multimodal ultrasound provides complementary information on liver morphology, perfusion, and stiffness, while fusion strategies further enhance diagnostic accuracy and robustness. Future efforts should focus on standardized, large-scale multicenter validation, methodological improvements in multimodal integration, and the incorporation of explainable artificial intelligence to support clinical translation. Ultimately, despite ongoing challenges related to data heterogeneity, reproducibility, interpretability, and clinical validation, multimodal ultrasound radiomics holds strong promise for noninvasive, individualized, and clinically meaningful liver disease management. |
| Druh dokumentu: | article |
| Popis súboru: | electronic resource |
| Jazyk: | English |
| ISSN: | 2576-2516 |
| Relation: | https://www.journaladvancedultrasound.com/fileup/2576-2516/PDF/1762476820383-1980119663.pdf; https://doaj.org/toc/2576-2516 |
| DOI: | 10.26599/AUDT.2025.250101 |
| Prístupová URL adresa: | https://doaj.org/article/a0ee7af055f84b669dd61e65ce509cec |
| Prístupové číslo: | edsdoj.0ee7af055f84b669dd61e65ce509cec |
| Databáza: | Directory of Open Access Journals |
Nájsť tento článok vo Web of Science | Abstrakt: | Multimodal ultrasound, including B-mode imaging, contrast-enhanced ultrasound (CEUS), and ultrasound-based elastography, has demonstrated significant value in evaluating both diffuse liver diseases such as fibrosis and steatosis, and focal liver lesions such as hepatocellular carcinoma (HCC). Radiomics, including both handcrafted radiomics and deep learning approaches, has emerged as a promising strategy to enhance ultrasound-based liver disease assessment. Recent studies have applied radiomics across multimodal ultrasound, achieving notable success in grading fatty liver disease, staging fibrosis, and improving diagnosis, risk stratification, and prognostic prediction in HCC. Multimodal ultrasound provides complementary information on liver morphology, perfusion, and stiffness, while fusion strategies further enhance diagnostic accuracy and robustness. Future efforts should focus on standardized, large-scale multicenter validation, methodological improvements in multimodal integration, and the incorporation of explainable artificial intelligence to support clinical translation. Ultimately, despite ongoing challenges related to data heterogeneity, reproducibility, interpretability, and clinical validation, multimodal ultrasound radiomics holds strong promise for noninvasive, individualized, and clinically meaningful liver disease management. |
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| ISSN: | 25762516 |
| DOI: | 10.26599/AUDT.2025.250101 |