Huang Zhibin, Mo Sijie, Li Guoqiu, Tian Hongtian, Wu Huaiyu, Chen Jing, Wang Mengyun, Tang Shuzhen, Xu Jinfeng, Dong Fajin
Department of Ultrasound, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, 518020, China.
The Second Clinical Medical College, Jinan University, Shenzhen, 518020, China.
Breast Cancer Res. 2025 Jul 1;27(1):123. doi: 10.1186/s13058-025-02073-y.
To develop and validate a predictive model for axillary lymph node metastasis (ALNM) in breast cancer (BC) by integrating clinicopathological factors, ultrasound features, and photoacoustic imaging-derived SO measurements, aiming to improve diagnostic accuracy and provide comprehensive clinical insights.
A total of 317 BC patients were included, with the cohort split into a training set (70%) and a testing set (30%). Univariate and multivariate logistic regression identified key predictive factors, leading to the creation of three models: ModA (clinicopathological factors only), ModB (clinicopathological and ultrasound features), and ModC (clinicopathological, ultrasound, and SO measurements from photoacoustic imaging). De-Long test and ROC curve were used to evaluate and compare the diagnostic performance of the models.
Multivariate analysis showed that maximum diameter, Ki67 expression, AUS report and SO levels were identified as significant risk factors for ALNM. ModA achieved an AUC of 0.776 (95% CI: 0.691-0.862), ModB improved to 0.824 (95% CI: 0.738-0.909), and ModC demonstrated the highest performance with an AUC of 0.882 (95% CI: 0.815-0.950) in the testing set. The results highlight that the comprehensive model (ModC), integrating clinical, ultrasound, and photoacoustic imaging data, provides superior predictive accuracy for ALNM.
Integrating SO measurements with traditional clinical and ultrasound data can substantially enhance the prediction of ALNM in BC patients. This combined model offers a comprehensive and reliable decision support tool for the preoperative risk assessment of axillary lymph nodes in BC.
通过整合临床病理因素、超声特征和光声成像衍生的SO测量值，开发并验证一种用于预测乳腺癌（BC）腋窝淋巴结转移（ALNM）的模型，旨在提高诊断准确性并提供全面的临床见解。
共纳入317例BC患者，将队列分为训练集（70%）和测试集（30%）。单因素和多因素逻辑回归确定关键预测因素，从而创建三个模型：模型A（仅临床病理因素）、模型B（临床病理和超声特征）和模型C（临床病理、超声和光声成像的SO测量值）。使用De-Long检验和ROC曲线评估并比较模型的诊断性能。
多因素分析表明，最大直径、Ki67表达、AUS报告和SO水平被确定为ALNM的显著危险因素。在测试集中，模型A的AUC为0.776（95%CI：0.691-0.862），模型B提高到0.824（95%CI：0.738-0.909），模型C表现最佳，AUC为0.882（95%CI：0.815-0.950）。结果表明，整合临床、超声和光声成像数据的综合模型（模型C）对ALNM具有更高的预测准确性。
将SO测量值与传统临床和超声数据相结合，可显著提高BC患者ALNM的预测能力。这种联合模型为BC腋窝淋巴结的术前风险评估提供了一种全面且可靠的决策支持工具。
