发表机构
College of Engineering and Applied Science, Department of Biomedical Engineering, University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校工程学院与应用科学学院生物医学工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出形态网络破坏指数（MNDI），利用网络方法整合核特征，在威斯康星数据上区分良恶性病变，AUC达0.941，为乳腺癌诊断提供可解释的患者级生物标志物。
AI 中文摘要
乳腺癌诊断通常考虑单个核形态特征，而它们的联合组织方式较少被评估。我们开发了形态网络破坏指数（MNDI），这是一种相对于良性参考状态衡量形态异常的患者级指标。使用威斯康星诊断性乳腺癌数据集，将十个核特征表示为网络节点，MNDI总结了45对特征配置中的破坏情况。性能通过重复分层五折交叉验证进行评估。恶性病变的MNDI显著高于良性病变（平均值：3.454对比1.165；p = 2.94e-69）。MNDI的AUC为0.941（95%置信区间：0.918-0.961），敏感性为85.9%，特异性为91.6%。使用网络衍生描述符的分类器AUC为0.928 ± 0.024。在BreaKHis组织病理学队列中的独立评估显示区分能力有限（AUC = 0.538，95%置信区间：0.401-0.668），表明依赖于潜在的形态表示。MNDI提供了一个可解释的框架，用于量化患者特定的形态破坏，而个体实施需要在兼容的特征空间内进行验证。
英文摘要
Breast cancer diagnosis commonly considers individual nuclear morphological characteristics, whereas their joint organization is less frequently evaluated. We developed the Morphological Network Disruption Index (MNDI), a patient-level measure of morphological abnormality relative to a benign reference state. Using the Wisconsin Diagnostic Breast Cancer dataset, ten nuclear characteristics were represented as network nodes, with MNDI summarizing disruption across 45 pairwise feature configurations. Performance was evaluated using repeated stratified five-fold cross-validation. Malignant lesions exhibited substantially higher MNDI than benign lesions (mean: 3.454 vs. 1.165; p = 2.94e-69). MNDI achieved an AUC of 0.941 (95% CI: 0.918-0.961), with 85.9% sensitivity and 91.6% specificity. A classifier using network-derived descriptors achieved an AUC of AUC of 0.928 +/- 0.024. Independent evaluation in the BreaKHis histopathology cohort showed limited discrimination (AUC = 0.538, 95% CI: 0.401-0.668), indicating dependence on the underlying morphological representation. MNDI provides an interpretable framework for quantifying patient-specific morphological disruption, while individual implementations require validation within compatible feature spaces.
