无线电技术
接收机工作特性
逻辑回归
医学
特征选择
人工智能
机器学习
膀胱癌
阶段(地层学)
特征(语言学)
放射科
计算机科学
癌症
内科学
古生物学
语言学
哲学
生物
作者
Situ Xiong,Zhehong Fu,Zhikang Deng,Sheng Li,Xiangpeng Zhan,Fu‐Chun Zheng,Hailang Yang,Xiaoqiang Liu,Songhui Xu,Hao Liu,Bing Fan,Wentao Dong,Yanping Song,Bin Fu
摘要
Predicting the accurate preoperative staging of bladder cancer (BLCA), which markedly affects treatment decisions and patient outcomes, using traditional clinical parameters is challenging. Nevertheless, emerging studies in radiomics, especially machine learning-based computed tomography (CT) image-based radiomics, hold promise in improving stage prediction accuracy in various tumors. However, the comparative performance and clinical utility of models for BLCA are under investigation.
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