Endocrine Abstracts

JOINT1736

Introduction: Women under hormone-deprivation therapies (HDTs) for breast cancer are at high fracture risk, but currently available fracture prediction tools are imprecise. Emerging evidence suggests that radiomics may contribute to fracture risk assessment in general population, but its application in this context is unknown.

Objective: To identify radiomic features (RFs) on opportunistic computed tomography (CT) 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 27. 1 months). Lumbar vertebrae were automatically segmented (convolutional neural network) for RFs extraction. Each feature was tested for its ability to predict VFs. Patients were randomly divided into training and test cohorts for the development and validation of the predictive model.

Results: Morphometric 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 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 lower thresholds 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 machine learning model showed high potential in identifying women at high fracture risk during HDTs.