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Updated: Sep 10, 2025

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多模式方法预测在晚期黑色素瘤中停止免疫检查点抑制剂后复发
Ka-Won Noh1, Yuri Tolkach2, Doris Helbig3
1University Hospital Cologne, Cologne, Germany.
概括
研究人员确定了关键生物标志物,包括TGFBR1表达和数字病理系统,以预测晚期黑色素瘤免疫检查点抑制剂 (ICI) 停用后的复发. 这有助于在符合条件的患者中安全地停止治疗.
科学领域:
- 癌症学
- 免疫疗法
- 数字病理学
背景情况:
- 免疫检查点抑制剂 (ICI) 在晚期黑色素瘤中提供持久反应.
- 缺乏预测算法阻碍了ICI治疗的安全中止.
- 确定适合停止治疗的患者对于治疗晚期黑色素瘤至关重要.
研究的目的:
- 在晚期黑色素瘤中停止ICI后开发复发的预测模型.
- 在ICI停止时确定风险分层的生物标志物.
- 建立一个安全停止ICI治疗的算法.
主要方法:
- 结合临床数据,人工智能分析H&E染色全片图像和基因表达特征的多模式方法.
- 单变量和多变量考克斯回归分析以确定复发的预测因素.
- 开发多变量适应回归线 (MARS) 模型进行预测准确性评估.
主要成果:
- 最好的整体反应,六个基因mRNA表达,瘤细胞密度 (TCD) 和淋巴细胞与血细胞的比率 (LYM/ PC) 预测了复发.
- TGFBR1表达和基于数字病理学的预后系统独立地与复发相关.
- 在停止ICI治疗后,MARS模型实现了84. 6%的复发预测准确度.
结论:
- 已识别的预后标记在常规临床实践中是可以解释和实施的.
- 这些标志物为考虑停止ICI的患者提供了风险分层.
- 这些发现支持在晚期黑色素瘤治疗中个性化治疗策略.
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