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基于深度学习的可解释预测扩散大B细胞淋巴瘤的复发

Hussein Naji^1,2, Paul Hahn^1,2, Juan I Pisula^1,2

¹Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.

|May 20, 2025

概括

深度学习模型可以使用组织学图像预测扩散大B细胞淋巴瘤 (DLBCL) 复发. 大型,不规则的瘤细胞核是复发的关键指标,有助于早期检测更好的治疗策略.

科学领域:

计算病理学计算病理学
人工智能在瘤学中的应用
癌症成像分析分析癌症成像分析

背景情况:

扩散性大B细胞淋巴瘤 (DLBCL) 是侵略性的,多达50%的患者在化疗后出现复发.
早期预测复发对于启动替代治疗策略至关重要.
目前用于癌症复发预测的深度学习模型缺乏可解释性.

研究的目的:

开发一种可解释的深度学习管道,用于从组织学图像中预测DLBCL复发.
识别与DLBCL复发相关的基于图像的关键特征.
提高对DLBCL异质性和治疗结果的生物学基础的理解.

主要方法:

开发了一个深度学习管道,使用来自公共DLBCL队列的组织学图像.
使用基于注意力的分类来识别相关的图像区域.
在高度相关的区域分割核,并分析形态特征,以预测复发.

主要成果:

获得了0.88的f1得分,在区分复发患者和没有复发患者方面表现出高准确度.
确定了大而形状不规则的瘤细胞核作为复发的最有预测性特征.
突出了注意力机制在组织学图像中的诊断相关区域的有用性.

结论:

组织学图像对于预测DLBCL治疗结果具有重要价值.
这项研究增强了对驱动像DLBCL.这样的侵袭性癌症的复杂生物过程的理解.
可解释的深度学习模型可以提供有关癌症复发的见解,这可能导致新的治疗方法.

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