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.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
PMID: 40128777

Abstract

BACKGROUND: To investigate the feasibility of detecting local recurrent nasopharyngeal carcinoma (rNPC) using unenhanced magnetic resonance images (MRI) and optimize a layered management strategy for follow-up with a deep learning model.

Authors

  • Yishu Deng
    Artificial Intelligence Laboratory, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Yingying Huang
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China; Department of Radiology, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou 510060, Guangdong, China.
  • Haijun Wu
    Department of Radiation Oncology, First People's Hospital of Foshan, No. 81 Lingnan North Road, Foshan 528000, Guangdong, China.
  • Dongxia He
    State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
  • Wenze Qiu
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, 510060, P. R. China.
  • Bingzhong Jing
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, 510060, P. R. China.
  • Xing Lv
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, 510060, P. R. China. lvxing@sysucc.org.cn.
  • Weixiong Xia
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, 510060, P. R. China.
  • Bin Li
    Department of Magnetic Resonance Imaging (MRI), Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Ying Sun
    CFAR and I2R, Agency for Science, Technology and Research, Singapore.
  • Chaofeng Li
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, 510060, P. R. China.
  • Chuanmiao Xie
    Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China.
  • Liangru Ke
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, 510060, P. R. China.

Keywords

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