Radiomics-based machine learning model integrating preoperative vertebral computed tomography and clinical features to predict cage subsidence after single-level anterior cervical discectomy and fusion with a zero-profile anchored spacer.

Objective: To develop machine-learning model that combines pre-operative vertebral-body CT radiomics with clinical data to predict cage subsidence after single-level ACDF with Zero-P.

Methods: We retrospectively review 253 patients (2016-2023). Subsidence is defined as ≥ 3 mm loss of fused-segment height at final follow-up. Patients are split 8:2 into a training set (n = 202; 39 subsidence) and an independent test set (n = 51; 14 subsidence). Vertebral bodies adjacent to the target level are segmented on pre-operative CT, and high-throughput radiomic features are extracted with PyRadiomics. Features are z-score-normalized, then reduced by variance, correlation and LASSO. Age, vertebral Hounsfield units (HU) and T1-slope entered a clinical model. Eight classifiers are tuned by cross-validation; performance is assessed by AUC and related metrics, with thresholds optimized on the training cohort.

Results: Subsidence patients are older, lower HU and higher T1-slope (all P < 0.05). LASSO retained 11 radiomic features. In the independent test set, the clinical model had limited discrimination (AUC 0.595). The radiomics model improved performance (AUC 0.775; sensitivity 100%; specificity 60%). The combined model is best (AUC 0.813; sensitivity 80%; specificity 80%) and surpassed both single-source models (P < 0.05).

Conclusion: A pre-operative model integrating CT-based radiomic signatures with key clinical variables predicts cage subsidence after ACDF with good accuracy. This tool may facilitate individualized risk stratification and guide strategies-such as endplate protection, implant choice and bone-quality optimization-to mitigate subsidence risk. Multicentre prospective validation is warranted.