基于基础模型的多式变压器框架用于HER2分层乳腺癌的生存分析
Qiang Li1, Shansong Wang1, Mojtaba Safari1
1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America.
Physics in medicine and biology
|September 25, 2025
概括
一个新的多式变压器框架,SurvMBC,通过整合组织病理学,分子和临床数据,改善了对HER2阳性乳腺癌的生存预测. 这种方法提高了预后准确性,并支持个性化治疗规划.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- HER2阳性乳腺癌呈现出显著的异质性,使准确的生存预测复杂化.
- 整合各种数据类型对于全面了解瘤生物学和预后至关重要.
研究的目的:
- 开发和验证多式变压器框架 (SurvMBC) 以改善HER2阳性乳腺癌的生存预测.
- 为了提高预后准确性,利用组织病理学,分子学和临床数据.
主要方法:
- 提出了SurvMBC,这是一个基础模型增强的架构,融合了整个幻灯片图像,临床叙述和分子特征.
- 使用病理学语言和图像预训练 (PLIP),BioBERT用于临床文本,Gen2Vec用于分子数据.
- 采用了带有注意力机制的跨模式变压器,用于整合多模式表示和预测生存.
主要成果:
- 在1095名HER2阳性乳腺癌患者的队列中,SurvMBC取得了0.857的一致性指数 (C指数).
- 该模型表现出强的性能,具有低的综合布赖尔分数和显著的风险分层能力 (日志排名p<0.01).
- 模型输出显示了与瘤阶段,等级和激素受体状态的显著关联 (p<0.05).
结论:
- 多模式数据融合有效地解决了瘤异质性,显著提高了HER2阳性乳腺癌的预后准确性.
- 基于注意力的整合促进了情境意识的学习,使个性化风险分层成为可能.
- 在HER2阳性乳腺癌患者中,SurvMBC支持风险适应性治疗规划.
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