医学
危险分层
生物标志物
回顾性队列研究
肿瘤科
内科学
深度学习
人工智能
队列
病态的
分层(种子)
队列研究
机器学习
个性化医疗
精密医学
生物标志物发现
预测模型
风险评估
比例危险模型
作者
Xiang Peng,Hao Tan,Bangxin Xiao,Yiwen Tan,Xiaofeng Yue,Youde Cao,Bing Liang,Wenlong Zhao,X. Liu,Quanhao He,Weiyang He,Mingzhao Xiao
标识
DOI:10.1097/js9.0000000000003581
摘要
This prior knowledge-guided deep learning system significantly improves OS prediction and risk stratification in UTUC by integrating multiscale pathological features. The AI-driven, interpretable tool offers an objective approach for prognostic assessment and biomarker discovery, with strong potential to refine personalized UTUC management and enhance prognostic accuracy.
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