在临床试验中开发和部署基于组织病理学的深度学习算法,用于临床试验中患者预先查
Albert Juan Ramon1, Chaitanya Parmar2, Oscar M Carrasco-Zevallos3
1Janssen R&D, LLC, a Johnson & Johnson Company. Data Science and Digital Health, San Diego, CA, USA. ajuanram@its.jnj.com.
Nature communications
|June 1, 2024
概括
使用H&E染色图像的人工智能算法可以识别尿癌的遗传变化,潜在地降低分子测试成本并加速患者在临床试验中的招生.
科学领域:
- 在瘤学瘤学.
- 病理学 病理学 病理学
- 人工智能的人工智能
背景情况:
- 精确识别像纤维细胞生长因子受体这样的基因变异对于向癌症治疗至关重要.
- 目前的分子测试方法可能耗时且组织密集,可能会延迟患者护理和临床试验招生.
- 开发基于人工智能的生物标志物检测解决方案可以简化这些过程.
研究的目的:
- 开发和验证一种深度学习算法,用于检测H&E染色全片图像中的生物标志物,用于晚期泌尿管癌.
- 评估该算法的潜力,以减少对分子测试的需求,并加速为临床试验招募患者.
- 评估人工智能系统在临床环境中的实际应用性和成本节约潜力.
主要方法:
- 一个深度学习算法是使用3000多个H&E染色的整片图像开发的,这些图像来自患有晚期泌尿管癌的患者.
- 该算法被优化为高灵敏度,以最大限度地减少符合条件的患者的排除.
- 验证是在350名患者的独立数据集上进行的,该系统在89个全球临床场所部署.
主要成果:
- 算法实现了曲线下的面积 (AUC) 为0.75.
- 它的特异性为31.8%,灵敏度为88.7%.
- 预计分子测试减少28.7%,在多个地点的研究中成功部署.
结论:
- 开发的人工智能算法在准确识别来自H&E图像的潜在生物标志物状态方面表现有前途.
- 这种方法可以显著减少对分子测试的依赖,从而节省成本,使患者更快地获得向治疗.
- 成功部署表明了该系统在药物开发和临床实践中优化资源配置的潜力.
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