基于超声的深度学习放射组学列线图，用于新辅助化疗后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
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影响因子:3.9
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
DOI:
10.1016/j.acra.2024.07.036
摘要
本研究旨在探索深度学习放射组学列线图（DLRN）在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）中的可行性。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。研究中共纳入243例接受NAC的患者，回顾性分析其数据（2014年10月至2022年7月）。DLRN集成了临床特征以及从超声（US）图像中提取的放射组学和深度迁移学习特征。通过构建受试者工作特征（ROC）曲线评估模型的诊断性能，并利用决策曲线分析（DCA）评估模型的临床实用性。还建立了生存模型以验证融合算法的有效性。在训练队列中，DLRN在肿瘤和淋巴结转移（LNM）预测中，AUC值分别为0.984和0.985，而在测试队列中分别为0.892和0.870。列线图的一致性指数（C-index）在训练和测试队列中分别为0.761和0.731。Kaplan-Meier生存曲线显示，高风险组患者的总生存期显著低于低风险组（P<0.05）。基于超声的DLRN模型有望作为临床指南，用于预测乳腺癌患者在NAC后肿瘤和淋巴结转移状态，并可用于预后评估，帮助临床医生做出更佳的临床决策。
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.