一种多数据融合深度学习模型,用于预测上道泌尿瘤的预后
Hongdi Sun1, Siping Chen2, Yongxing Bao3
1Department of Hematology, The Third Clinical Institute Affiliated to Wenzhou Medical University (Wenzhou People's Hospital), Wenzhou, Zhejiang, China.
Frontiers in oncology
|August 22, 2025
概括
一个结合CT成像和临床数据的新型深度学习模型准确地预测了上道泌尿道癌 (UTUC) 患者的结果. 这种先进的工具有助于个性化风险分层和治疗这种罕见的恶性尿路癌症.
科学领域:
- 泌尿外科
- 医学成像
- 人工智能
背景情况:
- 上部泌尿道癌 (UTUC) 是一种罕见的,侵袭性的尿道癌.
- 在手术后UTUC的复发率很高.
- 准确的预后对于患者管理至关重要.
研究的目的:
- 开发和验证用于预测UTUC患者结果的多模式深度学习模型.
- 将模型的性能与单模模型和传统临床参数进行比较.
- 通过特征重要性分析确定预后的关键预测因素.
主要方法:
- 对133名UTUC患者进行彻底切除术的回顾性分析.
- 开发综合CT成像和临床数据的多模式图像临床组合分类器 (MICC).
- 与基于CT (ImageNet) 和基于临床数据 (ClinicalNet) 的模型进行比较,以及病理T阶段.
- 使用SHapley添加剂解释 (SHAP) 进行特征重要性评估.
主要成果:
- 与单模模型相比,MICC模型显示出更高的预后准确性 (AUC 0. 918训练,0. 895测试).
- 获得了高分类指标:准确度为0.854,灵敏度为0.889,特异性为0.836.
- 通过SHAP分析,CT成像特征被确定为最重要的预测因素.
结论:
- 通过整合多相CT成像和临床数据,MICC模型可以准确预测UTUC患者的预后.
- 这种方法可以帮助临床医生进行个性化风险分层和治疗计划.
- 通过增强的预后预测,预计会改善患者的治疗结果.
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