使用深度学习-病理和临床因素预测异常性膜性脏病的免疫治疗反应
Xuejiao Wei1, Mengtuan Long1, Zhongyu Fan1
1Department of Nephrology, The First Hospital of Jilin University, Changchun, China.
Frontiers in endocrinology
|March 25, 2024
概括
一种新的深度学习模型使用病理特征准确地预测异常膜性病 (IMN) 患者的免疫治疗反应. 该模型的性能优于仅基于临床因素的预测,有助于治疗决策.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 计算病理学计算病理学
- 人工智能在医学中的应用
背景情况:
- 异形膜性病 (IMN) 患者对免疫治疗的反应不同.
- 个体患者的异质性需要个性化治疗策略.
研究的目的:
- 开发和验证一种深度学习模型,用于预测IMN患者的免疫治疗反应.
- 为了比较病理特征与临床因素的预测性能.
主要方法:
- 291名IMN患者的数据集被分为训练 (n=219) 和验证 (n=72) 队列.
- 通过使用补丁级卷积神经网络训练,分析了全幻灯片组织病理图像.
- 开发了机器学习模型,以评估病理和临床特征的预测值.
主要成果:
- 综合病理特征的模型实现了0.85 (培训) 和0.77 (验证) 的AUC.
- 结合临床特征的模型显示预测效率较低,AUC为0.75 (训练) 和0.62 (验证).
- 糖尿病和吸烟被确定为影响免疫治疗反应的独立风险因素.
结论:
- 将病理特征集成到深度学习模型中,可以更好地预测IMN患者的免疫治疗反应.
- 与仅依赖临床数据的模型相比,这种模型提供了更准确的治疗疗效评估.
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