新辅助化疗后，基于超声的深度学习放射学列表肿瘤和腋窝淋巴结状态预测
Ultrasound-Based Deep Learning Radiomics Nomogram for Tumor and Axillary Lymph Node Status Prediction After Neoadjuvant Chemotherapy
影响因子:3.90000
分区:医学2区 / 核医学2区
发表日期:2025 Jan
作者:
Yue-Xia Liu, Qing-Hua Liu, Quan-Hui Hu, Jia-Yao Shi, Gui-Lian Liu, Han Liu, Sheng-Chun Shu
摘要
这项研究旨在探讨新辅助化学疗法（NAC）在乳腺癌患者中预测肿瘤状态和腋窝淋巴结转移（ALNM）的深度学习放射学戒指（DLRN）的可行性。此外，我们采用COX回归模型进行生存分析来验证融合算法的有效性。在2014年10月至2022年7月之间，共经过NAC的243例患者。DLRN综合的临床特征以及从超声（USOUND）图像中提取的深度传递功能。通过构造ROC曲线评估了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。开发了一个生存模型来验证融合算法的有效性。在训练队列中，DLRN在肿瘤和LNM的接收器操作特性曲线值为0.984和0.985下产生面积，而在测试队列中分别为0.892和0.870。在训练和测试队列中，列诺图的一致性指数（C-指数）分别为0.761和0.731。 Kaplan-Meier的生存曲线表明，高危组的患者的总生存率明显低于低风险组的患者（p <0.05）。基于美国的DLRN模型可以作为预测NAC患者在NAC后肿瘤和LNM状态的临床指南。这种融合模型还可以预测患者的预后，这可以帮助临床医生做出更好的临床决策。
Abstract
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.