乳腺癌淋巴结转移的预测由一个Nomogram模型整合病理学,放射学和免疫评分
Tian Xu1,2, Jingyao Feng1,2, Kun Zhang1,2
1Department of Radiotherapy, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, Jiangsu, China.
Chemical biology & drug design
|January 21, 2026
概括
这项研究开发了一种非侵入性乳腺癌名录,集成深度学习-病理学,放射学和免疫评分,以预测淋巴结转移 (LNM). 组合模型显示出有希望的准确性,可能减少侵入性活检的需要.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 计算病理学计算病理学
背景情况:
- 淋巴结转移 (LNM) 是乳腺癌的关键预后因素.
- 准确预测LNM对于指导治疗决策和减少过度治疗至关重要.
研究的目的:
- 开发和验证一种用于预测乳腺癌淋巴结转移的非侵入性诺米克图.
- 整合基于深度学习的病理学,放射学和免疫评分,以提高预测性能.
主要方法:
- 从TCGA-BRCA组织病理学幻灯片中提取了病态特征,使用ResNet50和拉索回归.
- 从TCIAMRI图像中使用pyradiomics提取了放射性特征.
- 使用ESTIMATE算法计算免疫分数.
- 一个组合名图被构建并使用10倍交叉验证进行验证.
主要成果:
- 病理学模型的AUC值为0.65,放射学模型的AUC值为0.61,组合名录的AUC值为0.69.
- 综合名图显示,对预测LNM的灵敏度 (0.66) 和特异性 (0.71) 得到了改善.
- 放射学得分成为模型中最强的独立预测因素.
结论:
- 开发的整合病理学,放射学和免疫评分的非侵入性名录是预测乳腺癌中LNM的可靠工具.
- 这种方法可能有助于减少不必要的侵入性诊断程序的数量,例如哨兵淋巴结活检.
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