Establishment of a deep-learning-assisted recurrent nasopharyngeal carcinoma detecting simultaneous tactic (DARNDEST) with high cost-effectiveness based on magnetic resonance images: a multicenter study in an endemic area
Detecting local rNPC using unenhanced MRI with deep learning is feasible and DARNDEST-driven follow-up management is efficient and economic.
Authors
- Bingzhong Jing
- Xing Lv (ORCID: https://orcid.org/0000-0003-3125-6853)
- Wen‐Ze Qiu (ORCID: https://orcid.org/0000-0001-5994-3087)
- Chuanmiao Xie (ORCID: https://orcid.org/0000-0001-8533-623X)
- Liang‐Ru Ke (ORCID: https://orcid.org/0000-0001-7927-9726)
- Wei‐Xiong Xia (ORCID: https://orcid.org/0000-0002-2828-5963)
- Dengfeng He (ORCID: https://orcid.org/0000-0002-0662-2797)
- Ying Sun (ORCID: https://orcid.org/0000-0002-5888-2929)
- Yishu Deng (ORCID: https://orcid.org/0000-0002-7904-2208)
- Chaofeng Li (ORCID: https://orcid.org/0000-0002-0237-0350)
- Haijun Wu (ORCID: https://orcid.org/0000-0001-9662-7825)
- Bin Li (ORCID: https://orcid.org/0000-0001-5122-5515)
- Yingying Huang (ORCID: https://orcid.org/0000-0003-4837-731X)
Institutions
- Sun Yat-sen University (CN)
- First People's Hospital of Foshan (CN)
- Sun Yat-sen University Cancer Center (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2025-03-24
- DOI
- https://doi.org/10.1186/s40644-025-00853-5
- Citations
- 2
- Primary Topic
- Head and Neck Cancer Studies
- Type
- article
- Field-Weighted Citation Impact
- 3.70
Funders
- National Natural Science Foundation of China