超声栖息地影像组学机器学习预测临床淋巴结阴性三阴性乳腺癌腋窝转移
该回顾性双中心研究纳入313例临床淋巴结阴性三阴性乳腺癌患者，从灰阶超声图像提取栖息地影像组学特征并构建机器学习模型预测腋窝淋巴结转移。栖息地影像组学特征诊断效能优于常规影像组学特征，XGboost整合模型在验证队列AUC达0.89，决策曲线显示临床相关阈值5%-40%有净获益。
要点
- 栖息地影像组学特征预测腋窝淋巴结转移优于常规影像组学特征
- XGboost整合模型验证队列AUC=0.89，优于单一特征模型
- SHAP分析显示栖息地影像组学特征是最大贡献因素
核心结论
- 基于栖息地影像组学的机器学习模型可无创预测临床淋巴结阴性三阴性乳腺癌患者的腋窝淋巴结转移。
研究设计与样本
- 从灰阶超声图像提取栖息地影像组学特征和常规影像组学特征，使用LASSO降维和特征选择，10折交叉验证构建机器学习模型。
- 通过ROC分析、校准曲线和决策曲线分析评估模型性能。
诊断标准与关键数值
- 常规影像组学评分由7个影像组学特征构建，栖息地影像组学特征由9个影像组学特征构建。
- 栖息地影像组学特征预测腋窝淋巴结转移的AUC在训练队列为0.84，验证队列为0.82；常规影像组学评分对应AUC分别为0.72和0.69，差异均有统计学意义（p<0.05）。
- 验证队列中XGboost整合模型诊断性能最高，AUC=0.89（95%CI 0.83-0.95），优于常规影像组学评分和栖息地影像组学特征（p<0.05）。
- 决策曲线分析显示在5%-40%临床相关阈值范围内预测腋窝淋巴结转移有净获益。
临床应用与阅片要点
- 对于临床淋巴结阴性的三阴性乳腺癌患者，可关注超声图像中肿瘤栖息地异质性特征以辅助评估腋窝淋巴结转移风险。
- 栖息地影像组学特征较常规影像组学特征对腋窝淋巴结转移的预测价值更高，阅片时可结合机器学习模型输出。
局限性
- 研究为回顾性设计，可能存在选择偏倚。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
英文原文（摘要）· 点击展开
OBJECTIVE: To develop and validate habitat radiomics as a biomarker for predicting axillary lymph node metastasis (ALNM) in clinically node-negative (cN0) patients with triple-negative breast cancer (TNBC).
METHODS: A total of 313 TNBC patients from two institutions between January 2021 and December 2024 with cN0 were retrospectively included. Habitat radiomics features and conventional radiomics features were both extracted from grayscale ultrasound images. Tumor habitat heterogeneity features were derived using superpixel-based segmentation, followed by the construction of a habitat radiomics signature (HRS) and a conventional radiomics score (CRS). Dimensionality reduction and feature selection of radiomics features were conducted using the least absolute shrinkage and selection operator. Machine learning algorithms were then used to construct predictive models with 10-fold cross-validation. Model performance was evaluated using receiver operating characteristic analysis, calibration curves, and decision curve analysis.
RESULTS: Seven radiomics features were used to construct the CRS, and nine radiomics features were used to construct the HRS. The HRS demonstrated superior diagnostic performance over the CRS for predicting ALNM (0.84 vs. 0.72 in the training and 0.82 vs. 0.69 in the validation cohorts, both p < 0.05). In the validation cohort, among the integrated machine learning models, XGboost achieved the highest diagnostic performance (AUC = 0.89, 95%CI 0.83-0.95), outperforming both the CRS and HRS (p < 0.05). Decision curve analysis showed a net benefit at clinically relevant thresholds (5%-40%) for predicting ALNM in TNBC. The SHapley Additive exPlanations analysis identified the HRS as the top contributor.
CONCLUSION: The habitat radiomics-based machine learning model shows potential for non-invasively predicting ALNM in cN0 TNBC patients.
引用
- Du Y, Wang D, Song Y, et al. Machine Learning Integration of Ultrasound Habitat Radiomics for Predicting Axillary Metastasis in Clinically Node-negative Triple-negative Breast Cancer. Ultrasound Med Biol. 2026. PMID: 42140829. doi:10.1016/j.ultrasmedbio.2026.04.019
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。