标题： 放射学和生物学放射组学特征词典：解决个性化乳腺癌中的可理解AI问题；词典版本 BM1.0 显示英文标题

标题： Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Breast Cancer; Dictionary Version BM1.0

摘要： 基于放射组学的AI模型在乳腺癌诊断中显示出前景，但通常缺乏可解释性，限制了临床应用。 本研究通过提出一种双字典框架，填补了放射组学特征（RF）与标准化BI-RADS词典之间的差距。 首先，通过文献和专家评审将56个RF映射到BI-RADS描述符（形状、边缘、内部增强），创建了一个临床知情特征解释字典（CIFID）。 该框架被应用于多机构队列1,549名患者的动态对比增强MRI数据，以分类三阴性乳腺癌（TNBC）与非TNBC。 我们使用27种特征选择方法训练了27个机器学习分类器。 SHapley Additive exPlanations（SHAP）用于解释预测并为52个额外的RF生成互补的数据驱动特征解释字典（DDFID）。 最佳模型结合方差膨胀因子（VIF）选择与Extra Trees分类器，达到了平均交叉验证准确率为0.83。 关键预测RF与临床知识一致：较高的球形度（圆形/椭圆形）和较低的繁忙度（更均匀的增强）与TNBC相关。 该框架确认了已知的影像生物标志物，并发现了新的可解释关联。 这种双字典方法（BM1.0）提高了AI模型的透明度，并支持将RFs整合到常规乳腺癌诊断和个性化护理中。 显示英文摘要

摘要： Radiomics-based AI models show promise for breast cancer diagnosis but often lack interpretability, limiting clinical adoption. This study addresses the gap between radiomic features (RF) and the standardized BI-RADS lexicon by proposing a dual-dictionary framework. First, a Clinically-Informed Feature Interpretation Dictionary (CIFID) was created by mapping 56 RFs to BI-RADS descriptors (shape, margin, internal enhancement) through literature and expert review. The framework was applied to classify triple-negative breast cancer (TNBC) versus non-TNBC using dynamic contrast-enhanced MRI from a multi-institutional cohort of 1,549 patients. We trained 27 machine learning classifiers with 27 feature selection methods. SHapley Additive exPlanations (SHAP) were used to interpret predictions and generate a complementary Data-Driven Feature Interpretation Dictionary (DDFID) for 52 additional RFs. The best model, combining Variance Inflation Factor (VIF) selection with Extra Trees Classifier, achieved an average cross-validation accuracy of 0.83. Key predictive RFs aligned with clinical knowledge: higher Sphericity (round/oval shape) and lower Busyness (more homogeneous enhancement) were associated with TNBC. The framework confirmed known imaging biomarkers and uncovered novel, interpretable associations. This dual-dictionary approach (BM1.0) enhances AI model transparency and supports the integration of RFs into routine breast cancer diagnosis and personalized care.