Artificial intelligence on opportunistic computed tomography for predicting vertebral fracture risk in women undergoing estrogen deprivation therapy for breast cancer

Purpose: Fracture risk prediction in women exposed to hormone deprivation therapies (HDTs) for breast cancer is challenging, since bone mineral density may not be reliable in this context. This study aims to identify radiomic features (RFs) on opportunistic computed tomography (CT) images associated with vertebral fractures (VFs) in women under HDTs and to develop a radiomics-based model predictive of VFs. Methods: Radiomics analyses were performed on CT scans of 109 women (median age 61.1 years, range 27-85) exposed to HDTs (median duration of therapy 27.1 months). Lumbar vertebrae were automatically segmented (convolutional neural network) for RFs' extraction. Each feature was tested for its ability to predict VFs, as assessed by a longitudinal morphometric approach before and during HDTs. Patients were randomly divided into training and test cohorts for the development and validation of the predictive model. Results: During HDTs, new VFs were diagnosed in 23 women (21.1%) in association with older age (P = 0.013), lower total hip T-score (P = 0.041), and higher FRAX score for major fractures (P = 0.045). The machine learning (ML) model based on 20 RFs showed a high ability to predict VFs (ROC 0.832), outperforming that of T-score and FRAX score, even when thresholds lower than conventional ones were used (ROC 0.77 and 0.45, respectively). The RF "information measure of correlation" was the most relevant feature in the model, suggesting that a reduction in texture cross-correlation is positively associated with the development of VFs (P < 0.001). Conclusion: The radiomics-based ML model showed high potential in identifying women at high fracture risk during HDTs.