一个以传感器为导向的多式医疗数据采集和建模框架,用于瘤分类和治疗响应分析
Linfeng Xie1,2, Shanhe Xiao2,3, Bihong Ming2
1Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一个新的框架,用于使用多式数据进行非侵入性瘤分级和治疗响应预测. 该方法整合了深度学习,用于准确的分级和响应亚型发现,帮助个性化癌症护理.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 精确瘤学需要使用多式联络数据对瘤分级和治疗反应进行联合建模.
- 目前的方法通常依赖于侵入性分级,并且在多式联中缺乏结构约束.
- 评分和响应的独立建模限制了临床效用.
研究的目的:
- 开发一个以分级为指导的多式联络协作建模框架,用于整合的非侵入性瘤分级和治疗响应预测.
- 为了实现精确瘤学的可解释机制分析.
- 通过解决现有方法的局限性来改善临床决策.
主要方法:
- 利用深度学习模型 (3D ResNet-18,MLP,CNN-Transformer) 进行多式联机功能融合和响应建模.
- 纳入瘤分类作为一个弱监督的先前在一个统一的框架内.
- 采用了以等级为指导的特征融合机制,以强调歧视性信息.
主要成果:
- 获得了84.6%的准确性和0.81卡帕的瘤分级预测,与病理分级一致.
- 达到0.85AUC,0.81精度和0.79回忆治疗响应预测,优于现有模型.
- 确定了具有显著分层差异的稳定处理敏感和耐药亚型.
结论:
- 拟议的框架为非侵入性分级和治疗反应预测提供了一个综合解决方案.
- 在瘤学中证明了临床风险评估和个性化治疗决策的潜力.
- 验证了级别引导的多式联络融合对增强癌症护理的价值.
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