在多种癌症类型的整体幻灯片图像中自动瘤细分的概括
Ole-Johan Skrede1, Manohar Pradhan2, Maria Xepapadakis Isaksen2
1Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway. olejohas@ifi.uio.no.
NPJ precision oncology
|February 3, 2026
概括
一个新的深度学习模型在各种癌症类型的组织病理学图像中实现了准确的一般瘤细分. 这个单一模型的性能与专业模型相比,简化了数字病理学工作流程.
科学领域:
- 计算病理学计算病理学
- 医学中的人工智能.
- 数字病理学图像分析图像分析
背景情况:
- 深度学习 (DL) 显示出在病理学中自动化复杂任务的前景,例如瘤细分.
- 目前的DL模型通常需要为每个癌症类型提供特定的培训,这限制了它们的广泛适用性.
- 组织病理图像分析对于癌症诊断和治疗计划至关重要.
研究的目的:
- 开发和验证用于各种癌症类型的瘤细分的通用深度学习模型.
- 评估单一,通用模型与癌症类型特定模型的性能.
- 评估模型在不同患者群体,样本制备和幻灯片扫描仪中的稳定性.
主要方法:
- 开发一种通用瘤细分模型,使用来自4000多名患者 (结肠直肠,子宫内膜,肺,前列腺癌) 的2万多张整片图像.
- 对包括6种癌症类型的3000多名患者在内的外部队列进行验证.
- 从癌症基因组图谱 (TCGA) 中对1500多名额外患者进行探索性分析.
主要成果:
- 一般瘤细分模型在所有验证和TCGA队列中实现了超过80%的平均Dice系数.
- 在全块切除标本和各种验证数据集中,性能保持一致.
- 在将一般模型与单个癌症特定模型进行比较时,没有观察到显著的性能下降.
结论:
- 一个单一的,通用的深度学习模型可以有效地在多种癌症类型中执行瘤细分.
- 开发的模型表现出稳健性和通用性,在各种患者队列和技术条件中表现良好.
- 这种方法为数字病理学中的自动瘤细分提供了一个简化且潜在的更有效的解决方案.
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