Huang Xiangyang, Li Xin, Lyu Huiping, Wang Yiling, Zhang Lihui, Zhong Huohu, Lyu Guorong
Department of Ultrasound, Nan'an Hospital Affiliated to Shanghai University, Quanzhou, Fujian, China.
Department of Ultrasound, Nan'an Hospital Affiliated to Shanghai University, Quanzhou, Fujian, China; Department of Ultrasound, Second Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian, China.
Ultrasound Med Biol. 2025 Sep 10. doi: 10.1016/j.ultrasmedbio.2025.07.029.
This study aimed to construct a multifactorial predictive model by integrating ultrasound imaging parameters of primary breast tumors and axillary lymph nodes, along with clinical pathological indicators. The goal was to enhance the accuracy of predicting axillary lymph node metastasis and provide a basis for clinical decision-making.
A total of 268 breast cancer patients treated at Nan'an Hospital in Fujian Province from March 2022 to December 2024 were included in this study. They were randomly divided into training (187 cases) and validation (81 cases) cohorts in a 7:3 ratio. Ultrasound examinations were conducted to measure parameters such as maximum tumor diameter, lymph node cortex-to-medulla area ratio, the peak systolic velocity and lymph node resistive index of lymph node vascular, along with the collection of clinical pathological indicators, including pathological grade, tumor type, tumor location, menopausal status and age. Univariate and multivariate logistic regression analyses were performed to construct and validate a predictive model for breast cancer lymph node metastasis.
The results of the multivariate logistic regression analysis indicated that tumor maximum diameter (OR = 1.463, 95% CI: 1.250-1.712), lymph node cortex-to-medulla area ratio (OR = 11.878, 95% CI: 1.351-104.449), lymph node peak systolic velocity (OR = 1.165, 95% CI: 1.066-1.273) and lymph node resistive index (OR = 5.136, 95% CI: 2.721-9.693) were independent risk factors for breast cancer lymph node metastasis. The nomogram model constructed based on these factors demonstrated good predictive ability in both the training and validation cohorts, with area under the curves of 0.958 (95% CI: 0.930-0.986) and 0.958 (95% CI: 0.922-0.995), respectively. Internal validation using the Bootstrap method with 1000 repetitions showed consistent calibration curves and Hosmer-Lemeshow goodness-of-fit tests (p > 0.05), indicating good agreement between the predicted and observed probabilities of breast cancer lymph node metastasis. Decision curve analysis revealed that within a risk threshold range of 0.1-0.6, the model provided the greatest net benefit, indicating its strong clinical utility.
Tumor maximum diameter, lymph node cortex-to-medulla area ratio, lymph node peak systolic velocity and lymph node resistive index are independent risk factors for axillary lymph node metastasis in breast cancer. The predictive model built on these factors demonstrates high accuracy and clinical utility in predicting breast cancer lymph node metastasis, offering important guidance for individualized treatment of breast cancer patients.
本研究旨在通过整合原发性乳腺肿瘤和腋窝淋巴结的超声成像参数以及临床病理指标，构建多因素预测模型。目标是提高预测腋窝淋巴结转移的准确性，并为临床决策提供依据。
本研究纳入了2022年3月至2024年12月在福建省南安医院接受治疗的268例乳腺癌患者。他们以7:3的比例随机分为训练组（187例）和验证组（81例）。进行超声检查以测量最大肿瘤直径、淋巴结皮质与髓质面积比、淋巴结血管的收缩期峰值速度和淋巴结阻力指数等参数，并收集临床病理指标，包括病理分级、肿瘤类型、肿瘤位置、绝经状态和年龄。进行单因素和多因素逻辑回归分析以构建和验证乳腺癌淋巴结转移的预测模型。
多因素逻辑回归分析结果表明，肿瘤最大直径（OR = 1.463，95%CI：1.250 - 1.712）、淋巴结皮质与髓质面积比（OR = 11.878，95%CI：1.351 - 104.449）、淋巴结收缩期峰值速度（OR = 1.165，95%CI：1.066 - 1.273）和淋巴结阻力指数（OR = 5.136，95%CI：2.721 - 9.693）是乳腺癌淋巴结转移的独立危险因素。基于这些因素构建的列线图模型在训练组和验证组中均显示出良好的预测能力，曲线下面积分别为0.958（95%CI：0.930 - 0.986）和0.958（95%CI：0.922 - 0.995）。使用Bootstrap方法进行1000次重复的内部验证显示校准曲线和Hosmer - Lemeshow拟合优度检验结果一致（p > 0.05），表明乳腺癌淋巴结转移的预测概率与观察概率之间具有良好的一致性。决策曲线分析显示，在风险阈值范围为0.1 - 0.6时，该模型提供了最大的净效益，表明其具有强大的临床实用性。
肿瘤最大直径、淋巴结皮质与髓质面积比、淋巴结收缩期峰值速度和淋巴结阻力指数是乳腺癌腋窝淋巴结转移的独立危险因素。基于这些因素构建的预测模型在预测乳腺癌淋巴结转移方面具有较高的准确性和临床实用性，为乳腺癌患者的个体化治疗提供了重要指导。
