基于CT的深度学习放射学分析用于胃癌的手术前劳伦分类,并探索瘤微环境
Ming Cheng1,2, Yimin Guo3,4, Huiping Zhao5
1Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China.
European journal of radiology open
|July 3, 2025
概括
深度学习放射学分析 (DLRA) 在术前有效地区分了劳伦在胃癌 (GC) 的分类. 这种方法还揭示了基于Lauren亚型的GC瘤微环境中的显著异质性.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 生物信息学是一种生物信息学.
背景情况:
- 胃癌 (GC) 的分类对于治疗决策至关重要.
- 在手术前区分劳伦亚型可以指导个性化治疗.
研究的目的:
- 评估基于CT的深度学习放射学分析 (DLRA) 的实用性,用于在GC中进行劳伦分类的手术前分化.
- 为了探索与不同劳伦亚型相关的瘤微环境特征.
主要方法:
- 一组578名GC患者被分为培训,内部验证和外部验证组.
- 从CT扫描 (动脉和静脉阶段) 中提取了放射特征.
- 开发并验证了整合特征和临床数据的放射学名图;进行了转录学分析.
主要成果:
- 开发的名图表显示了所有队列中劳伦分类的良好歧视 (C指数从0.756到0.815不等).
- 诺米图表表现优于临床模型,显示出优异的校准和临床适用性.
- 转录学揭示了Lauren亚型中不同的基因丰富路径 (细胞收缩,迁移) 和免疫细胞透模式.
结论:
- 基于CT的DLRA是在GC中进行手术前劳伦分类的宝贵工具.
- 该研究强调了与劳伦分类相关的GC瘤微环境中的显著异质性,影响细胞功能和免疫透.
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