本研究旨在探讨深度学习放射组学列线图（DLRN）用于预测乳腺癌患者新辅助化疗（NAC）后肿瘤状态和腋窝淋巴结转移（ALNM）的可行性。此外，我们采用Cox回归模型进行生存分析，以验证融合算法的有效性。回顾性纳入2014年10月至2022年7月期间接受NAC的243例患者。DLRN整合了临床特征以及放射组学和深度转移学习从超声（美国）图像中提取的特征。通过构建ROC曲线评估DLRN的诊断性能，并使用决策曲线分析（DCA）评估模型的临床实用性。开发了生存模型来验证融合算法的有效性。在训练队列中，DLRN 产生的肿瘤和 LNM 的受试者工作特征曲线下面积值分别为 0.984 和 0.985，而在测试中分别为 0.892 和 0.870队列。训练组和测试组的列线图一致性指数（C 指数）分别为 0.761 和 0.731。 Kaplan-Meier 生存曲线显示，高危组患者的总生存率显着低于低危组患者 (P < 0.05)。美国的 DLRN 模型有望作为预测状态的临床指导乳腺癌患者 NAC 后的肿瘤和 LNM。这种融合模型还可以预测患者的预后，从而帮助临床医生做出更好的临床决策。版权所有 © 2024 大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

This study aims to explore the feasibility of the deep learning radiomics nomogram (DLRN) for predicting tumor status and axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) in patients with breast cancer. Additionally, we employ a Cox regression model for survival analysis to validate the effectiveness of the fusion algorithm.A total of 243 patients who underwent NAC were retrospectively included between October 2014 and July 2022. The DLRN integrated clinical characteristics as well as radiomics and deep transfer learning features extracted from ultrasound (US) images. The diagnostic performance of DLRN was evaluated by constructing ROC curves, and the clinical usefulness of models was assessed using decision curve analysis (DCA). A survival model was developed to validate the effectiveness of the fusion algorithm.In the training cohort, the DLRN yielded area under the receiver operating characteristic curve values of 0.984 and 0.985 for the tumor and LNM, while 0.892 and 0.870, respectively, in the test cohort. The consistency indices (C-index) of the nomogram were 0.761 and 0.731, respectively, in the training and test cohorts. The Kaplan-Meier survival curves showed that patients in the high-risk group had significantly poorer overall survival than patients in the low-risk group (P < 0.05).The US-based DLRN model could hold promise as clinical guidance for predicting the status of tumors and LNM after NAC in patients with breast cancer. This fusion model can also predict the prognosis of patients, which could help clinicians make better clinical decisions.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.