开发和验证基于转移学习的多omics胸腺瘤风险分类模型
Wei Liu1, Wei Wang2, Hanyi Zhang3
1School of Health Management, China Medical University, Shenyang, China. wliu@cmu.edu.cn.
Journal of digital imaging
|June 2, 2023
概括
一个结合临床,放射学和深度特征的新型预测模型准确地分层了胸腺瘤风险. 这种融合模型使用转移学习,提供一种非侵入性的方法来指导胸腺瘤患者的手术策略.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 胸腺瘤风险分层对于确定适当的治疗策略至关重要.
- 需要准确的非侵入性方法来预测胸腺瘤风险,以优化患者管理.
- 目前的方法可能无法充分利用综合临床和成像数据的潜力.
研究的目的:
- 开发和评估一个整合临床,放射学和深度特征的预测模型,用于分层胸腺瘤风险.
- 使用转移学习评估融合模型与单个模型 (临床,放射学,深度特征) 的性能.
- 确定开发的模型在非侵入性地区分高风险和低风险胸腺瘤中的有效性.
主要方法:
- 追溯分析了150名胸腺瘤患者的队列,数据分为训练 (80%) 和测试 (20%) 组.
- 从CT图像中提取了临床,放射性 (2590个特征) 和深度特征 (192个特征) (非增强,动脉,静脉阶段).
- 使用ANOVA,皮尔森相关性,PCA和LASSO进行特征选择,随后进行SVM分类和转移学习用于模型开发.
主要成果:
- 融合模型实现了高性能,AUC为0.99 (训练) 和0.95 (测试),精度为0.93 (训练) 和0.83 (测试).
- 与单个模型相比,融合模型在风险分层方面表现优越.
- 临床,放射学和深度模型表现出不同程度的成功,但被综合方法所超越.
结论:
- 融合模型整合了临床,放射学和深度特征,对非侵入性分层胸腺瘤风险有效.
- 这种方法显示出有很大的潜力,有助于确定胸腺瘤患者的最佳手术策略.
- 转移学习提高了模型准确预测胸腺瘤风险的能力,在临床瘤学中提供了有价值的工具.
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