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用病理基础模型对乳腺癌复发风险进行可解释的预测

Jakub R Kaczmarzyk^1,2,3, Sarah C Van Alsten^4,5, Alyssa J Cozzo⁴

¹Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA. jakub.kaczmarzyk@stonybrookmedicine.edu.

NPJ digital medicine

|January 16, 2026

概括

组织病理学基础模型可以预测乳腺癌复发风险,为转录基因分析提供可扩展的替代方案. 这些可解释的人工智能模型显示了精确瘤学的前景.

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
发现生物标志物的发现.

背景情况:

基于PAM50的ROR-P得分等转录组测试对于分层化ER阳性,HER2阴性乳腺癌复发风险至关重要.
这些转录基因分析并非普遍可用,因此需要使用替代方法进行风险分层.

研究的目的:

介绍MAKO,用于评估病理学基础模型的框架,用于从H&E染色全幻灯片图像中预测ROR-P得分.
对12个病理学基础模型和两个非病理学基线进行基准测试,以预测复发风险的能力.

主要方法:

利用基于注意力的多个实例学习来预测从整个幻灯片图像中ROR-P得分.
在卡罗来纳州乳腺癌研究 (CBCS) 队列上训练并验证了基础模型,并对TCGA BRCA数据进行了测试.
采用HIPPO解释性方法来识别关键瘤区域和候选生物标志物.

主要成果:

几种基础模型在分类,回归和生存预测任务中表现优于基线模型.
CONCH获得了最高的ROC AUC,而H-optimus-0和Virchow2显示出与连续ROR-P得分的最佳相关性.
基于组织学模型有效地根据复发风险对CBCS参与者进行了分层,可与转录组ROR-P得分进行比较.

结论:

可解释,基于组织学的人工智能模型作为乳腺癌复发风险分层的可扩展替代方案具有重大前景.
基础模型可以准确预测复发风险,可能提高精确瘤学应用.
瘤区域对于高风险预测至关重要,已识别的生物标志物需要进一步调查.

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