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在瘤微环境中检测细胞类型和密度可以改善乳腺癌的预后风险评估

Pu Liu¹, Xueli Zhang², Wenwen Wang³

¹Chongqing Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China; State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.

Biomolecules & biomedicine

|August 16, 2024

概括

瘤微环境中的乳腺癌细胞密度显著影响患者的预后. 在注释的整张幻灯片图像上训练的新人工智能模型准确地预测了生存率,帮助个性化治疗策略.

科学领域:

在瘤学瘤学.
计算病理学计算病理学
生物信息学是一种生物信息学.

背景情况:

乳腺癌瘤微环境中的细胞密度的预后价值仍然不完全理解.
大规模,注释全幻灯片成像 (WSI) 数据集的稀缺性限制了人工智能驱动的乳腺癌细胞密度分析.

研究的目的:

利用细胞密度建立一个强大的乳腺癌预后模型.
开发人工智能工具,用于WSI中准确的细胞类型识别,以提高预后评估.

主要方法:

拉索-考克斯回归被用来构建基于人口研究中的细胞密度的预后模型.
一个大数据集,超过7万个贴片,被病理学专家手动注释.
使用ResNet152进行转移学习,用于训练人工智能模型在WSI中进行细胞类型分类.

主要成果:

细胞密度得分成为乳腺癌患者的独立预后因素 (P < 0.05),在各层观察到显著的预后差异 (P = 0.0018).
人工智能模型在图像补丁中的各种细胞类型中实现了超过99%的分类准确性.
该模型在验证队列中显示了总生存期 (OS) 的强大预测性能,其1,3,5年AUC值分别为0.893,0.823和0.861.

结论:

细胞密度是乳腺癌的关键预后指标.
开发的AI模型和注释数据集为个性化医学和预后提供了宝贵的资源.
这项工作增强了AI在乳腺癌管理的数字病理学方面的潜力.

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The Tumor Microenvironment

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