开发和验证计算机组织学人工智能驱动的预测生物标志物,用于在晚期胰腺癌中选择化疗
Andrew E Hendifar1, Viswesh Krishna2, Vrishab Krishna2
1Cedars Sinai Medical Center, Los Angeles, CA.
概括
一个新的组织形态生物学生物标志物可以预测哪种化疗对晚期胰腺癌患者有益. 这个工具指导了基于gemcitabine (G-chemo) 和基于fluoropyrimidine (F-chemo) 的疗法之间的选择,以获得更好的结果.
科学领域:
- 在瘤学瘤学.
- 计算病理学计算病理学
- 生物标志物开发 生物标志物开发
背景情况:
- 晚期胰腺管腺癌 (PDAC) 缺乏用于第一线化疗选择的预测生物标志物.
- 目前的治疗策略往往涉及基于基 (G-chemo) 或基于基 (F-chemo) 的疗法,疗效可变.
研究的目的:
- 使用人工智能开发和验证基于组织形态学的生物标志物 (GvF),以预测先进PDAC中一线G化疗与F化疗的治疗效益.
- 确定哪些患者最能从特定的化疗方案中获益.
主要方法:
- 利用计算组织学人工智能 (CHAI) 平台从H&E染色活检中提取定量组织形态特征.
- 在发展队列中,根据与下一次治疗或死亡 (TNTD) 相关的特征开发了GvF生物标记得分.
- 在一个独立的队列中验证了生物标志物的TNTD和整体存活率 (OS) 的预测性能.
主要成果:
- 经过验证的GvF生物标志物成功预测了不同治疗效益.
- 在验证队列中,接受F-chemo的F-pref患者与G-chemo相比显示出明显更好的TNTD和OS.
- 与F-chemo相比,接受G-chemo的G-pref患者显示TNTD明显更好,尽管不是OS,但与F-chemo相比.
结论:
- 基于组织形态学的GvF生物标志物有效地预测了先进PDAC中第一线化疗的差异性治疗益处.
- 这种人工智能驱动的生物标志物可以指导最佳治疗选择,可能改善患者的治疗结果.
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