Current Projects

Predicting Bone Fracture in Metastatic Breast Cancer Lesions

3D rendering of the oral cancer mouse model with segmented tumor (red) and skeletal tissue (gold) imaged using microCAT II scanner.

3D reconstruction from in vivo micro-CT scans illustrating femora from NCr nude mice 3 weeks post tumor injection. Breast cancer cells were directly injected into the distal femur, resulting in substantial osteolysis.

PI: Kenneth A. Mann, PhD
Department: Orthopedic Surgery

Investigators: Sarah Arrington, BS (Physiology PhD student); Hui Zhang, PhD (post doctoral fellow); Matthew J. Allen, PhD; Timothy A Damron, MD; Kenneth A. Mann, PhD

Surgical intervention for impending bone fractures in patients with metastatic breast cancer to bone is currently based on a Mirels clinical grading scheme.  However, this scheme has poor specificity in terms of determining which patients require surgery or other treatment to prevent fracture. In a clinical series used to validate the Mirels rating scheme (conducted here at Upstate), only 1/3 of those predicted to fracture actually did so. Therefore, there is great clinical demand to better serve these patients.

The overall goal of this project is to develop an innovative and clinically relevant animal model of breast cancer metastasis to the femur and to develop improved imaging/structural analysis methods to predict bone fracture strength. We leverage Dr. Allen's successful murine breast cancer tumor osteolysis in the distal femur to develop a new model in the proximal femur.In vivo and in vitro micro-CT is used to generate subject-specific structural finite element models of the tumor-burdened bones. The predicted strength of the tumor-burdened bones is compared to laboratory biomechanical tests. We hypothesize that these structural models can predict fracture with greater specificity and sensitivity than a radiographic based scoring system. Once validated in this animal model, this approach could be extended to improve the clinical care of women with metastatic breast cancer lesions by providing accurate estimates of risk of fracture.