用深度学习进行多模式数据整合,预测用深度学习预测割抵抗性前列腺癌的进展风险:一项多中心的回顾性研究
Chuan Zhou1,2, Yun-Feng Zhang3, Sheng Guo3
1The First Clinical Medical College of Lanzhou University, Lanzhou, China.
Frontiers in oncology
|March 29, 2024
概括
这项研究开发了一种集成成像和病理学数据的AI模型,以预测前列腺癌进展到抵抗割的前列腺癌. 综合模型显著提高了预测准确度,有助于患者管理.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 晚期前列腺癌 (PCa) 经常发展为抵抗割的PCa (CRPC),这是一个预后不佳的疾病.
- 多参数磁共振成像 (mpMRI) 和组织病理学提供了有价值的预后信息.
- 人工智能 (AI) 可以整合多式联运数据以提高预后能力.
研究的目的:
- 构建一个基于人工智能的模型来预测CRPC进展.
- 整合来自mpMRI和组织病理学的多式数据,以改善预测.
- 开发一个指导患者预后和管理策略的工具.
主要方法:
- 对399名PCa患者的数据进行了回顾性分析.
- 从T2WI,DWI和ADCMRI序列中划分感兴趣的区域 (ROI).
- 深度学习模型训练使用病态的H&E幻灯片和放射性特征.
- 使用ROC,校准和决策曲线分析评估的联合组合模型名图的构建.
主要成果:
- 结合的AI模型实现了0.86.8的曲线下的面积 (AUC).
- 深度学习模型 (ResNet-50) 在放射学 (AUC 0.768) 和病理学 (AUC 0.752) 方面表现强.
- 组合模型显示了良好的校准和临床净益处,可以更好地预测PCa到CRPC的进展.
结论:
- 整合多模式数据,包括mpMRI和组织病理学,显著提高前列腺癌进展到CRPC的预测.
- 开发的AI模型为指导患者预后和管理决策提供了有价值的工具.
- 由人工智能驱动的多式联通数据集成是改善晚期前列腺癌治疗结果的有希望的方法.
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