一个临床可翻译的多模式深度学习模型,用于从组织病理学图像中检测HRD
Mohan Uttarwar1,2,3, Jayant Khandare1, P M Shivamurthy2
1School of Consciousness, Dr. Vishwanath Karad MIT World Peace University, Kothrud, Pune 411038, India.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
一个新的AI模型,TRINITY,可以使用标准病理图像预测同源复合缺陷 (HRD) 状态. 这为目前的基因检测提供了一个更快,更便宜的替代方案,用于指导癌症患者的PARP抑制剂治疗.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 病理学 病理学 病理学
背景情况:
- 聚 (ADP-ribose) 聚合酶 (PARP) 抑制剂治疗越来越负担得起,对乳腺癌和卵巢癌至关重要.
- 同源重组缺陷 (HRD) 是PARP抑制剂反应的关键生物标志物,但其识别具有挑战性.
- 目前用于HRD测试的下一代测序 (NGS) 取决于组织,失效率高,回报时间长.
研究的目的:
- 开发一种非侵入性方法来预测HRD状态.
- 克服目前基于组织的HRD测试方法的局限性.
- 为引导PARP抑制剂疗法创建一个快速,经济有效和节省组织的替代方案.
主要方法:
- 开发一种名为 TRINITY 的多式联络人工智能模型.
- 三位一体集成成像,基于图像的转录组和临床分子数据.
- 来自H&E染色样本的全幻灯片图像 (WSI) 被分析以预测HRD状态.
主要成果:
- 在TCGA乳腺和卵巢癌样本中,TRINITY取得了高性能指标 (例如,AUC-ROC为0.91和0.72).
- 该模型在外部盲人研究中显示出有希望的结果 (AUC-ROC为0.89).
- 在不同的队列中,TRINITY显示了预测HRD状态的潜力.
结论:
- 三位一体显示出作为一个快速,成本效益和节省组织的替代方案,用于HRD的传统NGS测试的潜力.
- 人工智能模型可能有助于识别可以从PARP抑制剂治疗中受益的患者.
- 需要进一步验证,以确认TRINITY在各种癌症类型中的通用性.
相关概念视频
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01:44Termination of Translation
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