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识别瘤学临床试验候选人使用人工智能预测治疗变化:试点实施研究

Kenneth L Kehl¹, Tali Mazor¹, Pavel Trukhanov¹

¹Dana-Farber Cancer Institute, Boston, MA.

JCO precision oncology

|March 21, 2024

概括

通过分析放射学报告,人工智能 (AI) 工具帮助识别患者进行精确瘤学试验. 这种人工智能方法显著减少了手动审查时间,有助于实时招募患者参加临床研究.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
临床试验管理临床试验管理

背景情况:

精准瘤学旨在将癌症患者与基于基因组资料的向治疗相匹配.
精确瘤学临床试验的累积挑战源于难以及时识别符合条件的患者.
人工智能 (AI) 为简化患者识别和试验匹配提供了潜在的解决方案.

研究的目的:

试点部署人工智能工具,以识别可能符合准确瘤学临床试验条件的患者.
在一个学术癌症中心评估AI在标记患者新系统治疗的效率.

主要方法:

神经网络处理放射学报告的未来应用,以确定可能开始新系统治疗的患者.
整合人工智能模型输出与MatchMiner工具,基于瘤基因组学进行患者试验匹配.
提供给瘤护士导航员 (ONN) 的每周报告,以方便与治疗瘤学家就潜在的试验资格进行沟通.

主要成果:

人工智能模型标记了525名患者的3,168名患者试验匹配 (占60,199人的5%),这减少了手动审查工作的95%.
在审查后,瘤护士导航员联系了74名患者的治疗瘤学家.
十名患者 (14%) 接受了试验咨询,五名 (7%) 参与了早期阶段的试验.

结论:

由人工智能驱动的方法有助于实时识别潜在的临床试验候选人.
需要进一步的研究来克服现有的障碍,并改善临床试验准确瘤学积累.
人工智能工具在优化患者招募用于精确癌症医学研究方面表现有前途.

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