自主监督的人工智能在诊断时预测了原发性皮肤状细胞癌的不良结果
Nicolas Coudray1,2, Michelle C Juarez3, Maressa C Criscito3
1Applied Bioinformatics Laboratories, New York University School of Medicine, New York, NY, USA.
NPJ digital medicine
|February 15, 2025
概括
一个新的深度学习模型使用初始活检图像预测皮肤状细胞癌 (cSCC) 患者的不良结果. 这种工具有助于早期风险分层,改善临床决策,以改善患者的生存率.
科学领域:
- 在瘤学瘤学.
- 数字病理学数字病理学
- 人工智能的人工智能
背景情况:
- 初级皮肤状细胞癌 (cSCC) 在美国导致显著的死亡率.
- 在初始诊断时准确的风险分层对于有效的临床管理至关重要.
- 整个幻灯片图像 (WSI) 的组织病理特征具有预后价值.
研究的目的:
- 开发和验证一种深度学习模型,用于预测cSCC患者的不良结果.
- 为了使用WSI的组织病理特征进行预后评估.
- 改善异质患者群体的风险分层.
主要方法:
- 使用来自163名cSCC患者的WSI开发了一种自我监督的深度学习模型.
- 该模型经过训练,以根据组织病理特征预测无疾病生存率.
- 对来自多个机构的563名cSCC患者的独立队列进行了验证.
主要成果:
- 该模型在发展队列中实现了0.73的一致性指数,在Mayo验证队列中达到0.84的无病生存率.
- 模型可解释性确定差差分化和深入入侵是不良预后的关键指标.
- 该模型有效地在BWH T2a和AJCC T2患者亚组中分层风险,已知结果可变性.
结论:
- 基于WSI的深度学习模型可以准确地预测cSCC的糟糕结果.
- 这种人工智能工具提高了预后准确性,并帮助cSCC患者的临床决策.
- 该模型在异质组中分层风险的能力提供了显著的临床实用性.
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