Your Location:
Home >
Browse articles >
PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer
RESEARCH | Updated:2026-06-10
    • PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer

    • PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer

    • Military Medical Research   2026年13卷第5期 页码:747-765
    • DOI:10.1186/s40779-025-00679-z    

      中图分类号:
    • 收稿:2025-01-26

      录用:2025-11-25

      网络首发:2026-01-06

      纸质出版:2026-05

    Scan QR Code

  • Yu Wang, Yong-Bo Xiang, Xiao-Wei Chen, 等. PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer[J]. Military Medical Research, 2026,13(5):747-765. DOI: 10.1186/s40779-025-00679-z.

    Yu Wang, Yong-Bo Xiang, Xiao-Wei Chen, et al. PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer[J]. Military Medical Research, 2026, 13(5): 747-765. DOI: 10.1186/s40779-025-00679-z.

  •  
  •  

0

浏览量

34

Downloads

0

CSCD

文章被引用时,请邮件提醒。
Submit
工具集
下载
参考文献导出
分享
收藏
添加至我的专辑

相关文章

Enhancing the clinical relevance of haemorrhage prediction models in trauma
Artificial intelligence and machine learning for hemorrhagic trauma care
Cell-free DNA in sepsis: from molecular insights to clinical management
The "cytokine storm" in infection and sepsis:win the battle but lose the war
A methodological guideline for consciousness assessment via neural electrophysiological activity

相关作者

Sankalp Tandle
Jared M. Wohlgemut
Max E. R. Marsden
Erhan Pisirir
Evangelia Kyrimi
Rebecca S. Stoner
William Marsh
Zane B. Perkins

相关机构

Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London
The Royal London Hospital, Barts Health NHS Trust
Department of Electronic Engineering and Computer Science, Queen Mary University of London
Academic Department of Military Surgery and Trauma, Research and Clinical Innovation, The Royal Centre for Defence Medicine
Royal Canadian Medical Services
0