PCGMMF:基于增强的多式模式功能融合的乳腺癌预后复发和转移风险的预测方法
Wei Du1, Liang Gao2, Xianhua Xu3
1School of Information Engineering, Huzhou University, Huzhou, Zhejiang, China, 313000.
Journal of biomedical informatics
|September 11, 2025
概括
一种新的多式融合方法,PCGMMF,通过整合组织病理学,临床,基因和甲基化数据来改善乳腺癌预后. 这种方法提高了复发和转移风险的预测准确性.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 医疗成像医学成像
背景情况:
- 乳腺癌是一种异质性疾病,死亡率高,目前的预后方法难以预测复发和转移.
- 现有的方法往往无法捕捉复杂的生物学关系和瘤异质性,导致不理想的预后性能.
- 需要先进的方法来准确预测乳腺癌复发和转移风险.
研究的目的:
- 开发和评估一种新型的多式融合方法 (PCGMMF),以改善乳腺癌预后分析.
- 整合各种数据类型,包括基因病理图像,临床数据,基因表达和DNA甲基化,以提高预测.
- 通过使用先进的深度学习技术,解决乳腺癌异质性和多模式特征相互作用的问题.
主要方法:
- 通过转移学习利用预训练的Vision-LSTM模型进行组织病理图像特征提取.
- 实施了对基因组数据的全面特征选择策略 (SVM,Mantle测试,相关性分析).
- 开发了一种基于双向注意力和自我注意力的增强多式模式特征融合模块 (BSAMF),以处理数据异质性和相互依赖性.
主要成果:
- 对于乳腺癌复发和转移风险,PCGMMF实现了0.903的预测准确度和0.924的AUC.
- 拟议的方法在预后分析中优于现有的最先进的方法.
- 解释性分析确定了重要的组织病理区域,提供了潜在的临床见解.
结论:
- 通过有效的多式联络数据集成,PCGMMF为乳腺癌预后分析提供了强大而创新的解决方案.
- 该方法显示了改进个性化精密治疗策略的巨大潜力.
- 研究结果为临床实践和乳腺癌管理决策提供了宝贵的参考资料.
相关概念视频
01:24Kaplan-Meier Approach
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
01:20Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
01:21Cancer Survival Analysis
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...