RADT-10. DEVELOPMENT AND INTERNAL VALIDATION OF A PREDICTIVE SCORE FOR VERTEBRAL COMPRESSION FRACTURE AFTER STEREOTACTIC BODY RADIATION THERAPY FOR SPINAL METASTASES

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi43-vi43
Author(s):  
Roman Kowalchuk ◽  
Benjamin Johnson-Tesch ◽  
Joseph Marion ◽  
Trey Mullikin ◽  
William Harmsen ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
High Risk ◽  
Vertebral Compression Fracture ◽  
Stereotactic Body Radiation Therapy ◽  
Cox Model ◽  
Spinal Metastases ◽  
Compression Fracture ◽  
Predictive Score ◽  
Internal Validation ◽  
Stereotactic Body Radiation

Abstract PURPOSE Vertebral compression fracture (VCF) is a potential adverse effect following stereotactic body radiation therapy (SBRT) for spinal metastases. In this analysis, we developed and internally validated a risk stratification model for VCF. METHODS From an initial set of 680 treatments, we excluded those with proton therapy, prior surgical intervention, or missing data. The final dataset had 464 treatments in 313 patients. Delineations of VCF and all radiographic components of the spinal instability neoplastic score (SINS) were determined by a radiologist. Recursive partitioning analysis (RPA) was conducted using separate training (70%), internal validation (15%), and test (15%) sets. The log-rank test was used as the criterion for node splitting. RESULTS With a median follow-up of 21 months, we identified 84 VCF (18%), including 65 (77%) de novo and 19 (23%) progressive fractures. There was a median 9 months (IQR: 3 – 21) to VCF. From an initial set of 15 candidate variables, six were identified using the backwards selection method, feature importance testing, and a correlation heatmap. Four were then selected in the highest-fidelity RPA models: epidural tumor extension, lumbar location, gross tumor volume > 10 cc, and SINS > 6. One point was assigned to each variable, and the resulting multivariate Cox model had a concordance of 0.760. Each one point increase in score was associated with increasing rates of VCF. Low-risk lesions (score: 0-1, n=273) had 2-year freedom from VCF of 92%, compared to 80% for intermediate-risk (score: 2, n=99) and 56% (score: 3-4, n=92) for high-risk lesions (p < 0.0001). Cumulative incidence curves with death as a competing risk showed increased VCF with higher scores via Gray’s test (p < 0.001). CONCLUSIONS Our internally-validated model identifies a subgroup of patients with high risk for VCF who may benefit from prophylactic surgical stabilization or vertebroplasty.

Radiomic modeling to predict risk of vertebral compression fracture after stereotactic body radiation therapy for spinal metastases

2021 ◽  
pp. 1-9
Author(s):  
Chengcheng Gui ◽  
Xuguang Chen ◽  
Khadija Sheikh ◽  
Liza Mathews ◽  
Sheng-Fu L. Lo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Radiation Therapy ◽  
Clinical Features ◽  
Vertebral Compression Fracture ◽  
Stereotactic Body Radiation Therapy ◽  
Spinal Metastases ◽  
Compression Fracture ◽  
Learning Model ◽  
Stereotactic Body Radiation ◽  
Machine Learning Model

OBJECTIVE In the treatment of spinal metastases with stereotactic body radiation therapy (SBRT), vertebral compression fracture (VCF) is a common and potentially morbid complication. Better methods to identify patients at high risk of radiation-induced VCF are needed to evaluate prophylactic measures. Radiomic features from pretreatment imaging may be employed to more accurately predict VCF. The objective of this study was to develop and evaluate a machine learning model based on clinical characteristics and radiomic features from pretreatment imaging to predict the risk of VCF after SBRT for spinal metastases. METHODS Vertebral levels C2 through L5 containing metastases treated with SBRT were included if they were naive to prior surgery or radiation therapy, target delineation was based on consensus guidelines, and 1-year follow-up data were available. Clinical features, including characteristics of the patient, disease, and treatment, were obtained from chart review. Radiomic features were extracted from the planning target volume (PTV) on pretreatment CT and T1-weighted MRI. Clinical and radiomic features selected by least absolute shrinkage and selection operator (LASSO) regression were included in random forest classification models, which were trained to predict VCF within 1 year after SBRT. Model performance was assessed with leave-one-out cross-validation. RESULTS Within 1 year after SBRT, 15 of 95 vertebral levels included in the analysis demonstrated new or progressive VCF. Selected clinical features included BMI, performance status, total prescription dose, dose to 99% of the PTV, lumbar location, and 2 components of the Spine Instability Neoplastic Score (SINS): lytic tumor character and spinal misalignment. Selected radiomic features included 5 features from CT and 3 features from MRI. The best-performing classification model, derived from a combination of selected clinical and radiomic features, demonstrated a sensitivity of 0.844, specificity of 0.800, and area under the receiver operating characteristic (ROC) curve (AUC) of 0.878. This model was significantly more accurate than alternative models derived from only selected clinical features (AUC = 0.795, p = 0.048) or only components of the SINS (AUC = 0.579, p < 0.0001). CONCLUSIONS In the treatment of spinal metastases with SBRT, a machine learning model incorporating both clinical features and radiomic features from pretreatment imaging predicted VCF at 1 year after SBRT with excellent sensitivity and specificity, outperforming models developed from clinical features or components of the SINS alone. If validated, these findings may allow more judicious selection of patients for prophylactic interventions.

scholarly journals Vertebral Compression Fracture After Spine Stereotactic Body Radiation Therapy: A Review of the Pathophysiology and Risk Factors

Neurosurgery ◽  
2017 ◽  
Vol 83 (3) ◽  
pp. 314-322 ◽  
Author(s):  
Salman Faruqi ◽  
Chia-Lin Tseng ◽  
Cari Whyne ◽  
Majed Alghamdi ◽  
Jefferson Wilson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Multivariate Analysis ◽  
Radiation Therapy ◽  
Vertebral Compression Fracture ◽  
Stereotactic Body Radiation Therapy ◽  
Compression Fracture ◽  
Stereotactic Body Radiation ◽  
Risk Patients ◽  
Spinal Segments ◽  
Selection Of

Abstract BACKGROUND Vertebral compression fracture (VCF) is a challenging and not infrequent complication observed following spine stereotactic body radiation therapy (SBRT). OBJECTIVE To summarize the data from the multiple studies that have been published, addressing the risk and predictive factors for VCF post-SBRT. METHODS A systematic literature review was conducted. Studies were selected if they specifically addressed risk factors for post-SBRT VCF in their analyses. RESULTS A total of 11 studies were identified, reporting both the risk of VCF post-SBRT and an analysis of risk factors based on univariate and multivariate analysis. A total of 2911 spinal segments were treated with a crude VCF rate of 13.9%. The most frequently identified risk factors on multivariate analysis were: lytic disease (hazard ratio [HR] range, 2.76-12.2), baseline VCF prior to SBRT (HR range, 1.69-9.25), higher dose per fraction SBRT (HR range, 5.03-6.82), spinal deformity (HR range, 2.99-11.1), older age (HR range, 2.15-5.67), and more than 40% to 50% of vertebral body involved by tumor (HR range, 3.9-4.46). In the 9 studies that specifically reported on the use of post-SBRT surgical procedures, 37% of VCF had undergone an intervention (range, 11%-60%). CONCLUSION VCF is an important adverse effect following SBRT. Risk factors have been identified to guide the selection of high-risk patients. Evidence-based algorithms with respect to patient selection and intervention are needed.

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