Statistical Properties of a Virtual Cohort for In Silico Trials Generated with a Statistical Anatomy Atlas

Abstract

Osteoporosis-related hip fragility fractures are a catastrophic event for patient lives but are not frequently observed in prospective studies, and therefore phase III clinical trials using fractures as primary clinical endpoint require thousands of patients enrolled for several years to reach statistical significance. A novel answer to the large number of subjects needed to reach the desired evidence level is offered by In Silico Trials, that is, the simulation of a clinical trial on a large cohort of virtual patients, monitoring the biomarkers of interest. In this work we investigated if statistical aliasing from a custom anatomy atlas could be used to expand the patient cohort while retaining the original biomechanical characteristics. We used a pair-matched cohort of 94 post-menopausal women (at the time of the CT scan, 47 fractured and 47 not fractured) to create a statistical anatomy atlas through principal component analysis, and up-sampled the atlas in order to obtain over 1000 synthetic patient models. We applied the biomechanical computed tomography pipeline to the resulting virtual cohort and compared its fracture risk distribution with that of the original physical cohort. While the distribution of femoral strength values in the non-fractured sub-group was nearly identical to that of the original physical cohort, that of the fractured sub-group was lower than in the physical cohort. Nonetheless, by using the classification threshold used for the original population, the synthetic population was still divided into two parts of approximatively equal number.