深度学习识别了与烟雾暴露状况相关的膀癌的组织病理变化
Okyaz Eminaga1, Hubert Lau2,3, Eugene Shkolyar4
1AI Vobis, Palo Alto, California, United States of America.
PloS one
|July 31, 2024
概括
深度学习模型可以从膀癌组织学图像中预测吸烟状况. 这项技术可以提供对患者烟雾暴露史的见解.
科学领域:
- 在瘤学瘤学.
- 病理学 病理学 病理学
- 人工智能的人工智能
背景情况:
- 吸烟是已知的膀癌 (BC) 的风险因素之一.
- 结核细胞组织学与吸烟状况之间的关系尚不清楚.
研究的目的:
- 调查膀癌组织学图像是否可以预测吸烟暴露状态.
- 开发和验证这种预测的深度学习模型.
主要方法:
- 在66个BC病例的全幻灯片组织学图像上训练了一个深度学习模型.
- 该模型在94个BC病例上进行了外部验证,使用AUC评估预测准确度.
- 对BC等级,性别,年龄和诊断时间进行控制的多变量分析.
主要成果:
- 该模型在外部验证中获得了0.67的AUC (95%CI:0.58-0.76),表明非随机分类.
- 该模型独立地预测了吸烟暴露状态,赔率比为1.710 (95% CI: 1.148-2.54).
- 鉴定出了预测吸烟状态的组织学模式.
结论:
- 深度学习可以识别膀癌中与吸烟暴露相关的组织病理特征.
- 基于组织学的吸烟状况预测可能提供有价值的临床信息.
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