用深度学习预测胃肠道流体瘤中的突变和结果:一个多中心,跨国研究
medRxiv : the preprint server for health sciences
|February 12, 2026
概括
应用于全幻灯片图像的深度学习模型准确地预测胃肠道 stromal 瘤 (GIST) 的突变和无复发生存率. 这些模型的性能与传统的风险评分相当,为GIST管理提供了一个新的工具.
科学领域:
- 在瘤学瘤学.
- 计算病理学计算病理学
- 基因组学就是基因组学.
背景情况:
- 胃肠层瘤 (GIST) 是最常见的胃肠介质瘤.
- GIST是由KIT和PDGFRA突变驱动的,影响预后和治疗.
- 已建立的GIST风险模型具有有限的可重现性;对全幻灯片图像 (WSI) 的深度学习 (DL) 显示出分子分类和预后的希望.
研究的目的:
- 开发和验证深度学习模型,用于预测使用WSIs的GIST中的分子变化,治疗灵敏度和无复发存活率 (RFS).
- 与已建立的临床病理风险得分相比,评估DL模型的性能.
- 为了评估DL的预后价值在不同的患者子组,包括那些接受辅助治疗.
主要方法:
- 分析了来自21个国际中心的8398例GIST病例.
- 在WSIs上训练DL模型,以预测KIT和PDGFRA突变,治疗敏感性 (阿瓦普利提尼布,伊马替尼布) 和RFS.
- 使用曲线下的面积 (AUC) 和危险比率 (HR) 验证DL模型性能.
主要成果:
- DL模型在预测突变方面实现了高AUC:KIT为0.87,PDGFRA为0.96.
- 精确预测了KIT外基子11delinss557-558 (AUC0.67) 和PDGFRA外基子18D842V (AUC0.93) 等特定突变.
- DL模型预测RFS的HR总体为8.44,在接受辅助治疗的患者中为4.74,与基于病理的得分相似.
结论:
- 在WSIs上的深度学习能够准确预测GIST中的分子变化,治疗灵敏度和RFS.
- DL模型的表现与国际队列中已建立的风险得分相似.
- DL模型提供了有价值的预后工具,特别是在辅助治疗的治疗持续时间决定的指导中.
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