探讨基于磁共振成像（MRI）参数的可解释性机器学习模型对老年乳腺癌前哨淋巴结转移的预测价值。

回顾性选取2019年1月至2025年9月于西安医学院第二附属医院收治的老年乳腺癌患者93例，收集其临床资料及动态增强磁共振成像（DCE-MRI）、扩散加权成像（DWI）图像。采用LASSO回归进行特征筛选，基于特征筛选，分别构建多因素Logistic回归（MLR）、极端梯度提升（XGboost）、支持向量机（SVM）、随机森林（RF）预测模型。采用受试者工作特征曲线下面积（AUC）、准确率、敏感度、特异度评估模型区分度，结合校准曲线与决策曲线分析模型校准度与临床净获益。采用SHAP方法对最佳模型进行可解释性分析。计量资料的比较采用t检验，计数资料的比较采用χ²检验。

前哨淋巴结活检阳性45例（转移组），阴性48例（无转移组）。与无转移组相比，转移组Ki-67高表达比例、环形强化比例、邻近血管增多阳性比例、肿瘤最大径、长短径比及早期强化率均更高，相对表观弥散系数（rADC）更低，差异均有统计学意义（χ²=5.604、3.930、3.891，t=2.083、4.313、5.055、2.408，P<0.05或0.01）。经LASSO筛选，进入模型的5个特征为早期强化率、rADC、环形强化、长短径比和Ki-67指数。模型性能比较显示，XGBoost模型的预测效能最高（AUC=0.915，95%CI：0.870～0.950；准确率85.7%，灵敏度83.3%，特异度86.7%），其校准曲线最接近理想对角线，且在阈值概率0.2～1.0区间内临床净获益最高。SHAP分析揭示，早期强化率是对模型贡献最大的特征，其次为rADC、环形强化、Ki-67指数和长短径比。

基于术前多参数MRI与临床变量构建的XGBoost模型，可有效预测老年乳腺癌前哨淋巴结转移状态；SHAP可解释性分析明确了瘤周影像特征的关键作用，增强模型透明度与临床可信度。

To explore the value of interpretable machine learning (ML) models based on magnetic resonance imaging (MRI) parameters in predicting sentinel lymph node metastasis (SLNM) in elderly patients with breast cancer.

This single-center retrospective study enrolled 93 elderly (≥60 years) breast cancer patients diagnosed between January 2019 and September 2025. Clinical data and preoperative dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) images were collected. LASSO regression was used for feature selection. Based on the selected five features, four predictive models were constructed: multivariate logistic regression, extreme gradient boosting (XGBoost), support vector machine (SVM), and random forest (RF). The models' discrimination ability was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Calibration and clinical net benefit were assessed via calibration curves and decision curve analysis. SHapley Additive exPlanations (SHAP) was employed for interpretability analysis of the best-performing model. T test was used to compared the measurement data, Chi-square test was used to compared the count data.

Forty-five cases were positive for sentinel lymph node biopsy (metastasis group), and 48 cases were negative (non-metastasis group). Compared with the non-metastatic group, the metastatic group exhibited higher proportion of Ki-67 high expression, larger tumor maximum diameter, higher long-to-short diameter ratio, higher proportions of annular enhancement and adjacent vascular proliferation, and higher early enhancement rate, along with a lower relative apparent diffusion coefficient (rADC), all with statistically significant differences (χ²=5.604, 3.930, 3.891; t=2.083, 4.313, 5.055, 2.408; P < 0.05 or P < 0.01). Five features were selected by LASSO for model construction: early enhancement rate, rADC, annular enhancement, long-to-short diameter ratio, and Ki-67 index. The XGBoost model demonstrated the best predictive performance (AUC=0.915, 95%CI: 0.870-0.950; accuracy=85.7%, sensitivity=83.3%, specificity=86.7%), with its calibration curve closest to the ideal diagonal and the highest clinical net benefit in the probability threshold range of 0.2-1.0. SHAP analysis revealed that early enhancement rate contributed the most to the model predictions, followed by rADC, annular enhancement, Ki-67 index, and long-to-short diameter ratio.

The XGBoost model integrating preoperative multiparametric MRI and clinical variables can effectively predict sentinel lymph node metastasis status in elderly breast cancer patients. SHAP interpretability analysis elucidates the contribution of key imaging and pathological features, enhancing model transparency and clinical credibility.