Vertebral compression fractures (VCFs) occur in approximately 14% of patients with spinal metastases following treatment with Stereotactic Body Radiotherapy (SBRT). The Spinal Instability Neoplastic Score (SINS) is the current clinical standard for assessing potential mechanical instability in these patients; however, it has several limitations such as it is manually assessed, has an inconsistent relationship with fracture risk and is only semi-quantitative. This study used quantitative CT imaging biomarkers derived from SBRT treatment planning imaging and machine learning models (Logistic Regression, Random Forest, XGBoost, SVM, Gradient Boosting, AdaBoost, Neural Network) to predict vertebral compression fractures (VCF) following SBRT in spinal metastases patients (in 300 thoraco-lumbar vertebrae from 179 patients). The Random Forest model achieved the best performance (sensitivity: 0.64, specificity: 0.76, F1-score: 0.47), showing a 36% improvement in balanced accuracy over SINS. Feature importance analysis identified the quantitative imaging biomarkers, spinal alignment and bone lesion composition (lytic or blastic disease) as the strongest predictors. ML models demonstrated meaningful improvements over existing SINS assessment.

seglearn is an open-source Python package for performing machine learning on time series or sequences. The implementation provides a flexible pipeline for tackling classification, regression, and forecasting problems with multivariate sequence and contextual data. Sequences and series may be learned directly with deep learning models or via feature representation with classical machine learning estimators. This package is compatible with scikit-learn and is listed under scikit-learn Related Projects. The package depends on numpy, scipy, and scikit-learn. seglearn is distributed under the BSD 3-Clause License. Documentation includes a detailed API description, user guide, and examples. Unit tests provide a high degree of code coverage. Source code and documentation can be downloaded from https://github.com/dmbee/seglearn.