# Exploratory Cluster-Based Radiographic Phenotyping of Degenerative Cervical Disorder: A Retrospective Study

## TL;DR

This study identifies three distinct radiographic patterns of degenerative cervical disorders using clustering analysis, which could help personalize spinal care.

## Contribution

The novel contribution is the use of unsupervised clustering to define distinct radiographic phenotypes of degenerative cervical myelopathy.

## Key findings

- Three distinct cervical alignment subgroups were identified using k-means clustering.
- Each subgroup showed unique radiographic features like lordosis, sagittal vertical axis, and cervical structure.
- The clustering solution was validated as stable and robust with high Calinski–Harabasz and acceptable Davies–Bouldin indices.

## Abstract

Background and Objectives: Degenerative cervical myelopathy (DCM), a major subtype of degenerative cervical disorders, presents with diverse sagittal alignment patterns. However, radiography-based phenotyping remains underexplored. This study aimed to identify distinct cervical alignment subgroups using unsupervised clustering analysis and to explore their potential clinical relevance. Materials and Methods: We analyzed 1371 lateral cervical radiographs of patients with DCM. C3–C7 sagittal vertical axis (SVA), lordosis, vertical length, and curved length were determined. K-means clustering was applied, and the optimal cluster number was determined using the elbow method and silhouette analysis. Clustering validity was assessed using the Calinski–Harabasz and Davies–Bouldin indices. Results: The final clustering solution was validated with a high Calinski–Harabasz index (1171.70) and an acceptable Davies–Bouldin index (0.99) at k = 3, confirming the stability and robustness of the classification. Cluster 1 (forward-head type) exhibited low lordosis (8.3° ± 4.7°), moderate SVA (95.9 ± 60.2 mm), and a compact cervical structure, consistent with kyphotic alignment and forward-head displacement. Cluster 2 (normal) showed the highest lordosis (24.1° ± 6.8°), moderate SVA (70.6 ± 50.2 mm), and balanced sagittal alignment, indicating a biomechanically stable cervical posture. Cluster 3 (long-neck type) displayed the highest SVA (135.6 ± 76.7 mm), the longest vertical and curved lengths, and moderate lordosis, suggesting a structurally elongated cervical spine with anterior head displacement. Significant differences (p < 0.01) were observed across all clusters, confirming distinct phenotypic patterns in cervical sagittal alignment. Conclusions: This exploratory clustering analysis identified three distinct radiographic phenotypes of DCM, reflecting biomechanical heterogeneity. Although prospective studies linking these phenotypes to clinical outcomes are warranted, our findings provide a framework for personalized spinal care in the future.