长度预测前列腺癌患者的积极监测结果,使用多重实例学习
Filip Winzell1, Ida Arvidsson1, Kalle Åström1
1Lund University, Centre for Mathematical Sciences, Lund, Sweden.
Journal of medical imaging (Bellingham, Wash.)
|October 16, 2025
概括
人工智能可以在使用全幻灯片图像的积极监视下识别高风险的前列腺癌患者,从而有可能避免过度治疗. 需要进一步的工作来改进用于临床用途的模型概括.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 数字病理学数字病理学
背景情况:
- 积极监测是前列腺癌患者的急性治疗的替代方案,前列腺癌患者的疾病程度较低.
- 监测疾病进展需要定期访问和评估患者.
- 识别高风险患者对于防止过度治疗至关重要.
研究的目的:
- 开发基于人工智能 (AI) 的模型,用于在积极监测队列中识别高风险的前列腺癌患者.
- 用全幻灯片图像来预测前列腺癌患者在主动监测中的纵向结果.
主要方法:
- 开发了一种使用UNI-2基础模型和基于注意力的方法的多实例学习框架.
- 在全幻灯片图像上训练有素的模型,带有患者级别标签,不包括明确的格里森等级.
- 使用Cox比例危险模型和外部数据集评估模型性能.
主要成果:
- 在接收机操作员特征曲线下的平均面积达到0.958.
- 适应预测概率的考克斯模型产生了0.824的C指数和2.32的危险比率.
- 在对外部数据集进行测试时,观察到模型性能显著下降.
结论:
- 在预测模型中避免格里森分数可以对纵向前列腺癌预测结果有益.
- 良性前列腺组织可能包含有价值的预后信息.
- 临床应用需要进一步的研究,以提高模型的概括性.
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