ArticleQuantitative imaging in medicine and surgery2023
Added-value of 3D amide proton transfer MRI in assessing prognostic factors of cervical cancer: a comparative study with multiple model diffusion-weighted imaging.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Time-dependent diffusion MRI for assessing tumor microstructure and prognostic risk factors in cervical cancer.Abdominal radiology (New York) · 2026Article
- Integrated PET-IVIM-DKI MRI for predicting lymphovascular invasion in NSCLC.Insights into imaging · 2025Article
- Article
- Added value of amide proton transfer-weighted magnetic resonance imaging to Prostate Imaging Reporting and Data System version 2.1 in differentiating clinically significant prostate cancer.Quantitative imaging in medicine and surgery · 2024Article
- Article
- Prediction of Kirsten rat sarcoma (Quantitative imaging in medicine and surgery · 2024Article
- Using amide proton transfer-weighted magnetic resonance imaging (MRI) in peritumor tissue to predict parametrial infiltration of cervical cancer: a case-control study.Quantitative imaging in medicine and surgery · 2024Article
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Authors and funding
8 authors at 3 institutions in 1 country.
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Abstract
Background: Amide proton transfer (APT) imaging has been gradually applied to cervical cancer, yet the relationships between APT and multiple model diffusion-weighted imaging (DWI) have yet to be investigated. This study attempted to evaluate the added value of 3-dimensional (3D) APT imaging to multiple model DWI for assessing prognostic factors of cervical cancer. Methods: This prospective diagnostic study was conducted in The First Affiliated Hospital of Zhengzhou University. A total of 88 consecutive patients with cervical cancer underwent APT imaging and DWI with 11 b-values (0-2,000 s/mm Results: APT SI Conclusions: Addition of APT to DWI may improve the ability to noninvasively predict poor prognostic factors of cervical cancer.
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