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基于DNA甲基化的瘤类型的预测来自中枢神经系统瘤的组织病理学,具有深度学习
Danh-Tai Hoang1, Eldad D Shulman2, Rust Turakulov3
1Biological Data Science Institute, College of Science, Australian National University, Canberra, Australian Capital Territory, Australia.
Nature medicine
|May 17, 2024
概括
一个新的深度学习模型,DEPLOY,准确地从组织病理学图像中对中枢神经系统 (CNS) 瘤进行分类. 这种工具有助于病理学家快速诊断中枢神经系统瘤,改善患者治疗策略.
科学领域:
- 计算病理学计算病理学
- 人工智能在瘤学中的应用
- 神经瘤诊断诊断神经瘤诊断
背景情况:
- 精确的中枢神经系统 (CNS) 瘤分类对于有效治疗至关重要.
- 基因甲基化分析提高了诊断准确度,但耗时且无法广泛使用.
- 组织病理学仍然是中枢神经系统瘤诊断的基石.
研究的目的:
- 开发一个深度学习模型 (DEPLOY) 用于准确的中枢神经系统瘤分类,使用组织病理学.
- 为了克服耗时的DNA甲基化分析的局限性.
- 为中枢神经系统瘤诊断提供快速和易于使用的工具.
主要方法:
- 开发了DEPLOY,这是一个集成直接图像分类,DNA甲基化β值预测和人口统计数据的深度学习模型.
- 在1796个内部患者病例上训练了一个十级模型.
- 在3个独立的外部数据集上对该模型进行了验证,其中包括2,156名患者.
主要成果:
- DEPLOY可以准确地从组织病理学图像中预测DNA甲基化β值.
- 该模型在对外部数据集进行中枢神经系统瘤分类时实现了95%的整体准确性和91%的平衡准确性.
- 高可信度预测显示出显著的诊断性能.
结论:
- DEPLOY显示出作为一个快速,人工智能驱动的工具,以协助病理学家在中枢神经系统瘤诊断中的潜力.
- 该模型可以显著减少诊断时间框架.
- 这种方法可以提高诊断精度,并简化中枢神经系统瘤的患者管理.
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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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