本研究的目的是评估基于超声 (US) 的深度学习 (DL) 列线图预测乳腺癌患者新辅助化疗 (NAC) 后腋窝淋巴结 (ALN) 状态的能力，及其协助放射科医生诊断腋窝淋巴结 (ALN) 状态的能力。两个医疗中心回顾性招募了 535 名接受 NAC 的淋巴结阳性乳腺癌患者。中心 1 包括训练队列中的 288 名患者和内部验证队列中的 123 名患者，而中心 2 则纳入了外部验证队列中的 124 名患者。在 NAC 前后的 US 图像上训练五个 DL 模型（ResNet 34、ResNet 50、VGG19、GoogLeNet 和 DenseNet 121），并选择最佳模型。使用 DL 预测概率和临床病理学特征构建了基于美国的 DL 列线图。此外，还比较了放射科医生在有或没有列线图的帮助下的表现。ResNet 50 在所有 DL 模型中表现最佳，在内部和外部验证队列中分别实现了 0.837 和 0.850 的曲线下面积 (AUC)。美国的 DL 列线图显示出对 NAC 后 ALN 状态的强大预测能力，内部和外部验证队列中的 AUC 分别为 0.890 和 0.870，优于临床模型和 DL 模型（p 均 < 0.05，除了 p = 外部验证队列中的 DL 模型为 0.19）。此外，列线图显着提高了放射科医生的诊断能力。美国的 DL 列线图有望预测 NAC 后的 ALN 状态，并可以帮助放射科医生获得更好的诊断表现。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。

The aim of this study was to evaluate the capability of an ultrasound (US)-based deep learning (DL) nomogram for predicting axillary lymph node (ALN) status after neoadjuvant chemotherapy (NAC) in breast cancer patients and its potential to assist radiologists in diagnosis.Two medical centers retrospectively recruited 535 node-positive breast cancer patients who had undergone NAC. Center 1 included 288 patients in the training cohort and 123 patients in the internal validation cohort, while center 2 enrolled 124 patients for the external validation cohort. Five DL models (ResNet 34, ResNet 50, VGG19, GoogLeNet, and DenseNet 121) were trained on pre- and post-NAC US images, and the best model was chosen. A US-based DL nomogram was constructed using DL predictive probabilities and clinicopathological characteristics. Furthermore, the performances of radiologists were compared with and without the assistance of the nomogram.ResNet 50 performed best among all DL models, achieving areas under the curve (AUCs) of 0.837 and 0.850 in the internal and external validation cohorts, respectively. The US-based DL nomogram demonstrated strong predictive ability for ALN status post-NAC, with AUCs of 0.890 and 0.870 in the internal and external validation cohorts, respectively, outperforming both the clinical model and the DL model (p all < 0.05, except p = 0.19 for DL model in external validation cohort). Moreover, the nomogram significantly improved radiologists' diagnostic ability.The US-based DL nomogram is promising for predicting ALN status post-NAC and could assist radiologists for better diagnostic performance.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.