CT深度学习放射学和基因组学用于预测上皮卵巢癌的分期
Yinping Leng1,2, Wenjie Liu1,2, Wanling Qi3
1Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
International journal of surgery (London, England)
|January 20, 2026
概括
一个新的组合模型准确地预测了使用计算机断层扫描 (CT) 放射学,深度学习 (DL) 功能和转录学来预测表皮卵巢癌 (EOC) 阶段. 这种方法有助于为EOC患者制定个性化治疗策略.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 生物信息学是一种生物信息学.
背景情况:
- 准确的手术前分期对表皮卵巢癌 (EOC) 患者的预后和治疗至关重要.
- 目前的分期方法需要改进,以获得更好的患者结果.
研究的目的:
- 开发和验证一种用于预测EOC分期的新型模型.
- 该模型整合了基于计算机断层扫描 (CT) 的放射学,深度学习 (DL) 功能和转录学数据.
- 这项研究旨在将这些特征与瘤微环境联系起来,以提高分期预测.
主要方法:
- 共有201名EOC患者被分为培训,内部验证和外部验证组.
- 从CT图像中提取了1130个放射性和512个DL特征.
- 构建了五种后勤回归模型:临床-语义 (CS),放射学,DL,CS-放射学,以及一个组合模型.
- 使用AUC,校准曲线和决策曲线分析 (DCA) 评估模型性能.
- 使用RNA测序数据研究了与放射基因组学相关的免疫透模式.
主要成果:
- 组合模型表现出极好的预测性能,AUC为0.910 (训练),0.913 (内部验证) 和0.882 (外部测试集).
- DCA表明组合模型具有良好的临床适用性.
- 校准曲线显示了预测值和观察值之间的良好一致性.
- 与早期相比,高级阶段的EOC显示出更高的免疫透率.
结论:
- 开发的组合模型准确地预测了EOC分期.
- 这为开发针对EOC患者个性化治疗策略提供了可靠的基础.
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