准确评估乳腺癌腋窝淋巴结（ALN）对新辅助化疗（NAC）的反应具有重要价值。本研究旨在开发一种人工智能系统，利用多区域动态对比增强 MRI (DCE-MRI) 和临床病理学特征来预测乳腺癌 NAC 后腋窝病理完全缓解 (pCR)。本研究包括来自六个医疗中心的回顾性和前瞻性数据集2018年5月至2023年12月在中国建立了基于深度学习的全自动集成系统（FAIS-DL），以顺序执行肿瘤和ALN分割以及腋窝pCR预测。使用受试者工作特征曲线下面积 (AUC)、准确性、敏感性和特异性来评估 FAIS-DL 的预测性能。对 45 名患者进行了 RNA 测序分析，以探索 FAIS-DL 的生物学基础。对 1145 名患者（平均年龄，50 岁±10 [SD]）进行了评估。在这些患者中，506 名患者在训练和验证集中（腋窝 pCR 率为 40.3%），127 名患者在内部测试集中（腋窝 pCR 率为 37.8%），414 名在汇总外部测试集中（腋窝 pCR 率为 48.8%） ），前瞻性测试集中有 98 例（腋窝 pCR 率为 43.9%）。在预测腋窝 pCR 方面，FAIS-DL 在内部测试集、汇总外部测试集和前瞻性测试集中分别实现了 0.95、0.93 和 0.94 的 AUC，也显着高于临床模型和深度学习模型基于单区域 DCE-MRI（所有 P < 0.05，德隆检验）。在汇总的外部和前瞻性测试集中，FAIS-DL 将不必要的腋窝淋巴结清扫率从 47.9% 降低到 6.8%，并将受益率从 52.2% 提高到 86.5%。 RNA测序分析显示，高FAIS-DL评分与免疫介导的基因和通路的上调相关。FAIS-DL在预测腋窝pCR方面表现出令人满意的性能，这可能指导乳腺癌患者个性化治疗方案的制定。本研究得到国家自然科学基金（82371933）、山东省自然科学基金（ZR2021MH120）、泰山学者青年专家计划（tsqn202211378）、中华医学教育协会重点项目的资助（2022KTM030）、中国博士后科学基金（314730）和北京市博士后研究基金（2023-zz-012）。版权所有© 2024 作者。由 Elsevier B.V. 出版。保留所有权利。

The accurate evaluation of axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer holds great value. This study aimed to develop an artificial intelligence system utilising multiregional dynamic contrast-enhanced MRI (DCE-MRI) and clinicopathological characteristics to predict axillary pathological complete response (pCR) after NAC in breast cancer.This study included retrospective and prospective datasets from six medical centres in China between May 2018 and December 2023. A fully automated integrated system based on deep learning (FAIS-DL) was built to perform tumour and ALN segmentation and axillary pCR prediction sequentially. The predictive performance of FAIS-DL was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RNA sequencing analysis were conducted on 45 patients to explore the biological basis of FAIS-DL.1145 patients (mean age, 50 years ±10 [SD]) were evaluated. Among these patients, 506 were in the training and validation sets (axillary pCR rate of 40.3%), 127 in the internal test set (axillary pCR rate of 37.8%), 414 in the pooled external test set (axillary pCR rate of 48.8%), and 98 in the prospective test set (axillary pCR rate of 43.9%). For predicting axillary pCR, FAIS-DL achieved AUCs of 0.95, 0.93, and 0.94 in the internal test set, pooled external test set, and prospective test set, respectively, which were also significantly higher than those of the clinical model and deep learning models based on single-regional DCE-MRI (all P < 0.05, DeLong test). In the pooled external and prospective test sets, the FAIS-DL decreased the unnecessary axillary lymph node dissection rate from 47.9% to 6.8%, and increased the benefit rate from 52.2% to 86.5%. RNA sequencing analysis revealed that high FAIS-DL scores were associated with the upregulation of immune-mediated genes and pathways.FAIS-DL has demonstrated satisfactory performance in predicting axillary pCR, which may guide the formulation of personalised treatment regimens for patients with breast cancer in clinical practice.This study was supported by the National Natural Science Foundation of China (82371933), National Natural Science Foundation of Shandong Province of China (ZR2021MH120), Mount Taishan Scholars and Young Experts Program (tsqn202211378), Key Projects of China Medicine Education Association (2022KTM030), China Postdoctoral Science Foundation (314730), and Beijing Postdoctoral Research Foundation (2023-zz-012).Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.