新辅助治疗治疗的乳腺癌症预后预测的病理学模型的开发和验证：一项回顾性多中心研究
Development and Validation of a Pathomics Model for Prognosis Prediction in Neoadjuvant Therapy-Treated Breast Cancer: A Retrospective, Multicenter Study.
摘要(中文译文)
准确的结果预测对于接受新辅助治疗（NAT）的乳腺癌患者的个体化治疗至关重要。在本研究中，我们开发并验证了一种多模态人工智能模型——临床病理组学整合结果预测模型（CIOPM），通过整合临床病理数据与苏木精-伊红（H&E）染色的手术标本全切片图像（WSIs），预测接受NAT治疗的乳腺癌患者的预后。研究纳入了2008年1月至2020年5月期间来自四个多中心队列的835例患者的847张WSIs，用于模型开发与外部验证。CIOPM在两个验证队列（VCs）中对总生存期（OS）和无病生存期（DFS）均展现出稳健的预测性能：在VC 1中，OS的C指数分别为0.933（95% CI：0.878-0.977）和0.915（95% CI：0.850-0.960）；在VC 2中，DFS的C指数分别为0.947（95% CI：0.895-0.983）和0.937（95% CI：0.905-0.965）。CIOPM能够准确地将接受NAT治疗的乳腺癌患者分层为高风险组和低风险组，对OS和DFS预测的AUC均达到0.957。此外，该模型在消融实验、亚组分析及临床风险模型比较中均展现出优越的性能。该模型为可靠的风险分层和个体化治疗决策提供了支持。
Abstract
Accurate outcome prediction is essential for personalized therapy in patients with breast cancer receiving neoadjuvant therapy (NAT). In this study, we developed and validated a multimodal artificial intelligence model, the ClinicHistomics Integrated Outcome Prediction Model (CIOPM), to predict prognosis in NAT-treated breast cancer patients by integrating clinicopathological data and hematoxylin and eosin (H&E)-stained surgical specimen whole-slide images (WSIs). A total of 847 WSIs from 835 patients across four multicenter cohorts collected between January 2008 and May 2020 were enrolled in model development and external validation. The CIOPM demonstrated robust predictive performance for overall survival (OS) and disease-free survival (DFS) in the two validation cohorts (VCs), yielding C-indices of 0.933 (95% CI: 0.878-0.977) and 0.915 (95% CI: 0.850-0.960) for OS in VC 1, and 0.947 (95% CI: 0.895-0.983) and 0.937 (95% CI: 0.905-0.965) for DFS in VC 2, respectively. The CIOPM enables accurate stratification of patients with NAT-treated breast cancer into high- and low-risk groups, achieving an AUC of 0.957 for both OS and DFS prediction. Moreover, it demonstrated superior performance in ablation studies, subgroup analyses, and clinical risk model comparisons. This model provides reliable risk stratification and personalized treatment decisions.
如何引用
AMA
Yani Wei, Wei Lou, Zongbo Han, Fan Yang, Fengling Li, Haofeng Li, et al. Development and Validation of a Pathomics Model for Prognosis Prediction in Neoadjuvant Therapy-Treated Breast Cancer: A Retrospective, Multicenter Study.. MedComm. 2026; doi:10.1002/mco2.70826.
APA
Yani Wei, Wei Lou, Zongbo Han, Fan Yang, Fengling Li, Haofeng Li, et al (2026). Development and Validation of a Pathomics Model for Prognosis Prediction in Neoadjuvant Therapy-Treated Breast Cancer: A Retrospective, Multicenter Study.. MedComm. https://doi.org/10.1002/mco2.70826
GB/T 7714
Yani Wei, Wei Lou, Zongbo Han, Fan Yang, Fengling Li, Haofeng Li, et al. Development and Validation of a Pathomics Model for Prognosis Prediction in Neoadjuvant Therapy-Treated Breast Cancer: A Retrospective, Multicenter Study.[J]. MedComm, 2026 doi:10.1002/mco2.70826.
在 PubMed增强版 阅读全文
翻译全文 · AI 文献问答 · PDF 原文下载 · 引用导出