目的: 构建并验证基于超声与乳腺X线影像特征的BI-RADS 4类乳腺病变风险预测模型,提升良恶性鉴别诊断效率。方法: 回顾性纳入113例经病理证实的BI-RADS 4类乳腺病变患者（良性42例,恶性71例）,提取其超声与乳腺X线图像特征。通过LASSO回归与多因素Logistic回归筛选关键变量,构建列线图模型,并评估其判别能力（C指数）、准确性（AUC）、灵敏度和特异度。进一步引入XGBoost、ViT和GNN三种机器学习模型,在训练集与测试集上开展性能对比分析,并采用SHAP值与注意力机制等方法提升模型可解释性。结果: 边缘、边界、肿物形状、肿物边缘及密度为独立预测因子。列线图模型C指数为0.779,AUC为0.758。XGBoost、ViT与GNN模型分别达AUC 0.881、0.872与0.890,表现优于传统回归。GNN在灵敏度（88.0%）和F1分数（0.857）方面表现最佳。多模型综合分析显示,边缘毛刺、不规则形状和高密度是预测恶性的重要影像特征。结论: 融合多模态特征的深度学习模型,尤其是GNN与ViT,可有效提升BI-RADS 4类病变良恶性预测准确性,具有良好临床实用前景,为优化乳腺穿刺指征提供智能化决策支持。
Abstract
Objective To develop a nomogram prediction model based on ultrasound and mammographic features for BI-RADS category 4 breast lesions and evaluate its diagnostic value in early differentiation of benign and malignant lesions. Methods A retrospective analysis was conducted on 113 pathologically confirmed BI-RADS 4 breast lesions (42 benign, 71 malignant). Ultrasound and mammographic features were extracted. Independent predictors were selected using LASSO regression and multivariate logistic regression to construct a nomogram model, which was evaluated in terms of discrimination (C-index), accuracy (AUC), sensitivity, and specificity. Additionally, three machine learning models—Extreme Gradient Boosting (XGBoost), Vision Transformer (ViT), and Graph Neural Network (GNN) —were implemented and compared using training and testing datasets. SHAP value analysis and attention mechanisms were employed to enhance model interpretability. Results Lesion margin, boundary, shape, edge, and density were identified as independent predictors. The nomogram model achieved a C-index of 0.779 and an AUC of 0.758. The XGBoost, ViT, and GNN models yielded AUCs of 0.881, 0.872, and 0.890, respectively, outperforming traditional regression. GNN achieved the best sensitivity (88.0%) and F1 score (0.857). Across all models, spiculated margins, irregular shape, and high density were key predictors of malignancy. Conclusion Deep learning models integrating multimodal features—especially GNN and ViT—can significantly improve the prediction accuracy of BI-RADS 4 breast lesion malignancy risk. These models hold great promise for clinical application and may assist in optimizing biopsy decision-making.
关键词
乳腺BI-RADS 4类病变 /
多模态影像 /
极端梯度提升算法 /
视觉Transformer模型 /
图神经网络 /
风险预测模型 /
机器学习
Key words
BI-RADS category 4 breast lesions /
multimodal imaging /
extreme gradient boosting algorithm /
vision transformer model /
graph neural network /
risk prediction model /
machine learning
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基金
商洛市科技计划项目“高频超声联合超声引导下穿刺活检在商洛地区乳腺癌早期筛查中的应用”（2020-Z-0103）
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