Conventional chest computed tomography-based radiomics for predicting the risk of thoracolumbar osteoporotic vertebral fractures.

Abstract

Our study focused on predicting thoracolumbar osteoporotic vertebral fractures through radiomic analysis of non-fractured thoracic vertebrae using conventional chest CT. Four types of radiomics models were developed and showed acceptable prediction performance. Radiomics models incorporating both cortical-appendicular and trabecular bone may have superior performance compared to those using either feature set individually. The RAD score models based on thoracic vertebral combinations achieved comparable performance with lumbar bone mineral density (BMD) measurements.

Purpose: To develop and validate radiomics models based on chest CT for predicting the risk of thoracolumbar osteoporotic vertebral fractures (OVFs).

Methods: A total of 494 patients (including 198 patients with thoracolumbar OVFs) who underwent conventional chest CT scans were included in this retrospective analysis and were divided into training set 1 (n = 334) and validation set 1 (n = 160). Radiomics features (RFs) were extracted from each thoracic vertebral level on chest CT images. Four types of radiomics models (trabecular RFs, cortical-appendicular RFs, mixed RFs, and RAD score) were constructed and compared. Additionally, RAD score models based on trabecular and cortical-appendicular bone of different vertebral combinations (T1-T6, T7-T12, and top 3 vertebrae) were performed, respectively. A subset of patients with available bone mineral density (BMD) data formed training set 2 (n = 199) and validation set 2 (n = 88). We combined RAD score of different vertebral combinations with lumbar BMD for predicting thoracolumbar OVFs, and further adjusted for age. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC).

Results: Among the radiomics models, the RAD score model based on trabecular and cortical-appendicular bone achieved highest AUC at the most vertebral levels. The RAD score model of top 3 (T5 + T8 + T10) vertebrae achieved higher AUC (0.813) than T7-T12 (AUC = 0.780) with a statistically significant difference (P = 0.02) and T1-T6 (AUC = 0.772) without a statistically significant difference (P = 0.062). Prior to adjusting for age, both RAD score models (AUCs 0.774-0.807) and RAD score + BMD models (AUCs 0.771-0.800) demonstrated slightly superior performance compared to BMD (AUC = 0.736) alone in predicting OVFs, although the differences were not statistically significant (P > 0.05). Following adjustment for age, our RAD score models, which utilized different vertebral combinations (AUCs 0.784-0.804), were found to be comparable to lumbar BMD (AUC = 0.785) in predicting OVFs (P > 0.05).

Conclusion: Radiomics analysis based on conventional chest CT can provide valuable information for predicting thoracolumbar OVFs. Radiomics models incorporating both cortical-appendicular and trabecular bone may have superior performance compared to those using either feature set alone. RAD score models based on thoracic vertebral combinations comparable performance compared to lumbar BMD highlights its clinical utility.