Non-enhanced abbreviated MRI as a periodic surveillance protocol for colorectal liver metastases compared with contrast-enhanced CT: a prospective observational study

NE-AMRI may be a promising periodic surveillance tool for CRC patients after surgery to increase diagnostic accuracy of liver metastases, developing personalized clinical management and improving prognosis, simultaneously avoiding side effects associated with ionizing radiation and contrast agents.

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Publication Details

Journal
International Journal of Surgery
Published
2025-01-28
DOI
https://doi.org/10.1097/js9.0000000000002252
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
2.72

Funders

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article

Non-enhanced abbreviated MRI as a periodic surveillance protocol for colorectal liver metastases compared with contrast-enhanced CT: a prospective observational study

Shengxiang Rao, Jingjing Liu, Liheng Liu, Gengyun Miao et al.
2 citations
International Journal of Surgery
Radiomics and Machine Learning in Medical Imaging
2.72
article

Non-enhanced abbreviated MRI as a periodic surveillance protocol for colorectal liver metastases compared with contrast-enhanced CT: a prospective observational study

Shengxiang Rao, Jingjing Liu, Liheng Liu, Gengyun Miao, Lechi Ye, Mengsu Zeng
article en
2 citations

Abstract

NE-AMRI may be a promising periodic surveillance tool for CRC patients after surgery to increase diagnostic accuracy of liver metastases, developing personalized clinical management and improving prognosis, simultaneously avoiding side effects associated with ionizing radiation and contrast agents.

International Journal of SurgeryVol. 111(3)
Sun Yat-sen University (CN), Fudan University (CN), Zhongshan Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Shanghai Institute of Hematology (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Fudan University
Good health and well-being
Openalex Percentile: Top 13%
Radiomics and Machine Learning in Medical Imaging
2.72
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