乳腺癌患者腋窝淋巴结转移的集成几何量化与形态分析框架
An integrated geometric quantification and shape analysis framework for axillary lymph node metastasis in breast cancer patients
- Central South University（中南大学）
- The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）
- Clinical Research Center for Breast Disease in Hunan Province（湖南省乳腺疾病临床研究中心）
- The Chinese University of Hong Kong（香港中文大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出集成拓扑感知表面处理与多分辨率球谐分析的框架，用于定量表征乳腺癌腋窝淋巴结形态，其混合分辨率模型AUC达0.918，优于传统基线，并验证了可移植性。
AI中文摘要：
淋巴结形态的定量表征对于评估乳腺癌腋窝淋巴结转移具有重要意义。然而，从计算机断层扫描（CT）分割重建的表面可能包含几何和拓扑缺陷，从而影响后续分析，而传统形状描述符主要表征全局形态。为解决这些问题，我们开发了一个集成框架，将拓扑感知的表面处理与CT来源腋窝淋巴结的多分辨率球谐（SH）分析相结合。该处理流程生成了拓扑有效的属0表面，并改善了网格质量，随后以多个SH阶表示，并使用20个预定义的几何特征族进行表征。几何保真度随SH阶增加而提高，而预测性能在中等分辨率时达到峰值。不同特征族偏好的SH阶也有所不同。一个特定于特征族的混合分辨率模型达到了0.918的AUC，而传统PyRadiomics Shape14基线的AUC为0.884，对应0.0344的提升。受控扰动实验表明，较高的SH阶传递了更多细尺度几何变化，并导致基于曲率的预测稳定性较低。代表性的几何描述符提供了与转移相关的表面形态的可解释表征。独立验证进一步支持了该框架的可移植性：在多中心淋巴结队列中的无标签复制重现了特征族特定的分辨率效应，而有标签的LIDC-IDRI肺结节实验重现了SH阶与预测性能之间的分辨率依赖性关系。总之，该框架为淋巴结形态和转移相关影像表型的定量表征提供了拓扑有效的基础。
英文摘要：
Quantitative characterization of lymph node morphology is important for assessing axillary lymph node metastasis in breast cancer. However, surfaces reconstructed from computed tomography (CT) segmentation may contain geometric and topological defects that compromise subsequent analysis, while conventional shape descriptors predominantly characterize global morphology. To address these issues, we developed an integrated framework combining topology-aware surface processing with multi-resolution spherical harmonic (SH) analysis of CT-derived axillary lymph nodes. The processing pipeline produced topology-valid genus-0 surfaces with improved mesh quality, which were then represented at multiple SH degrees and characterized using 20 predefined geometric feature families. Geometric fidelity increased with SH degree, whereas predictive performance peaked at intermediate resolutions. Preferred SH degree also differed across feature families. A family-specific mixed-resolution model achieved an AUC of 0.918, compared with 0.884 for the conventional PyRadiomics Shape14 baseline, corresponding to an improvement of 0.0344. Controlled perturbation experiments showed that higher SH degrees transmitted more fine-scale geometric variation and yielded lower stability of curvature-based predictions. Representative geometric descriptors provided interpretable characterization of metastasis-associated surface morphology. Independent validation further supported the framework's transportability: label-free replication in a multicenter lymph node cohort reproduced the family-specific resolution effects, while a labeled LIDC-IDRI lung-nodule experiment reproduced the resolution-dependent relationship between SH degree and predictive performance. Altogether, the framework provides a topology-valid basis for quantitative characterization of lymph node morphology and metastasis-associated imaging phenotypes.
