Improved shape analysis method predicts breast cancer lymph node spread
An integrated geometric quantification and shape analysis framework for axillary lymph node metastasis in breast cancer patients
Computer Vision and Pattern Recognition
Summary
Assessing whether breast cancer has spread to lymph nodes helps guide treatment, but existing imaging techniques sometimes miss important details. The authors created a new way to clean up and analyze 3D images of lymph nodes from CT scans, capturing both large and small shape features more accurately. Their method better predicts cancer spread by combining multiple levels of shape detail, and it was confirmed to work on different patient data. This approach could help doctors evaluate lymph nodes more reliably using medical images.
What this means in practice
- •For radiology departments: Improve identification of metastatic lymph nodes in breast cancer CT scans using enhanced shape analysis for better diagnosis support.
- •For medical imaging software developers: Integrate topology-aware surface correction and multi-resolution shape descriptors into CT analysis tools for more accurate lymph node morphology characterization.
Authors
Zixi Yi, Limeng Qu, Gary P. T. Choi
Abstract
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.