对多中心胃癌的可解释深度学习,从CT图像中进行T阶段测试
Guoliang Zheng1, Huan Wang2, Xiaomiao Chai3
1Department of Gastric Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, Shenyang, Liaoning, China.
NPJ digital medicine
|December 20, 2025
概括
一个新的深度学习框架,GTRNet,使用CT扫描精确地分阶段胃癌T1-T4瘤,超越放射科医生,并帮助做出手术前治疗决策.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 胃癌的手术前T分期对治疗计划至关重要.
- 对于胃癌分期的常规对比增强CT解释缺乏一致的可靠性,并且是主观的.
研究的目的:
- 开发和验证一个自动化,可解释的深度学习框架 (GTRNet),用于使用常规CT图像进行胃癌的术前T分期.
- 为了比较GTRNet与放射科医生在分类瘤阶段的性能.
主要方法:
- 进行了一项回顾性多中心研究,涉及1792名患者.
- 一个端到端的深度学习模型GTRNet在CT图像上进行了训练,没有手动细分或注释.
- 该模型的性能在内部和两个独立的外部队列中进行了评估.
主要成果:
- 在内部和外部验证队列中,GTRNet表现出高的区别 (AUC 0.86-0.95) 和准确性 (81-85%).
- 深度学习模型在T分期准确性方面超过了人类放射科医生.
- 可解释AI (Grad-CAM) 突出关注胃壁和血清,提高了可解释性.
结论:
- 开发的GTRNet框架为基于CT的胃癌T分期提供了一个自动化和可解释的解决方案.
- 这一管道有可能标准化手术前分期并改善新辅助疗法的选择.
- 结合GTRNet的rad-score和临床因素的nomogram显示了比传统方法更好的临床效用.
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