Potential for Early Fracture Risk Assessment in Patients with Metastatic Bone Disease Using Parametric Response Mapping of CT Images

Abstract

Pathologic vertebral compression fractures (PVCFs) cause significant morbidity in patients with metastatic bone disease. Limitations in existing clinical biomarkers leave clinicians without reliable metrics for predicting PVCF, thus impeding efforts to prevent this severe complication. To establish the feasibility of a new method for defining the risk of a PVCF, we retrospectively analyzed serial computed tomography (CT) scans from 5 breast cancer patients using parametric response mapping (PRM) to quantify dynamic bone miniral density (BMD) changes that preceded an event. Vertebrae segmented from each scan were registered to the same spatial frame and voxel classification was accomplished using a predetermined threshold of change in Hounsfield units (HU), resulting in relative volumes of increased (PRMHU+), decreased (PRMHU−), or unchanged (PRMHU0) attenuation. A total of 7 PVCFs were compared to undiseased vertebrae in each patient serving as controls. A receiver operator curve (ROC) analysis identified optimal imaging times for group stratification. BMD changes were apparent by an elevated PRMHU+ as early as 1 year before fracture. ROC analysis showed poor performance of PRMHU− in stratifying PVCFs versus controls. As early as 6 months before PVCF, PRMHU+ was significantly larger (12.9 ± 11.6%) than control vertebrae (2.3 ± 2.5%), with an area under the curve of 0.918 from an ROC analysis. Mean HU changes were also significant between PVCF (26.8 ± 26.9%) and control (−2.2 ± 22.0%) over the same period. A PRM analysis of BMD changes using standard CT imaging was sensitive for spatially resolving changes that preceded structural failure in these patients.

Vascular Deformation Mapping for CT Surveillance of Thoracic Aortic Aneurysm Growth

“…Quantitative mapping of TAA growth allows for investigation of unique parameters (eg, eccentricity, longitudinal extent, multifocality) that are otherwise unable to be easily captured. While VDM represents one of the first techniques for quantitative mapping of disease progression in TAA, similar image analysis techniques using deformable image registration have been used to phenotype and assess progression of diseases of the lungs (19,20), brain (21)(22)(23), and bones (24,25). The development of similar quantitative methods to assess TAA progression promises to improve risk stratification by more clearly separating intervals with slow versus no growth and may serve as a metric to better assess the effects of pharmacologic and surgical interventions.…”

Effectiveness of Rehabilitation for Cancer Patients with Bone Metastasis

“…SINS is an evaluation for predicting the risk of pathological fractures and paralysis due to spinal cord compression in patients with bone metastases, 7) whereas the Mirels score is used to evaluate the risk of pathological fracture caused by metastases in long bones. 8) In recent years, analysis for bone metastases has been conducted using CT-based structural rigidity analysis, [29][30][31] finite element analysis methods, [32][33][34] 18 F-fluorodeoxyglucose PET/CT, 35) and parametric response mapping, 36) but these are not widely available because they require specialized equipment, software, and expertise to implement. 37) For pelvic metastasis, anatomical classification can be made using the Enneking classification.…”

Biomechanical Properties of Metastatically Involved Osteolytic Bone

“…CT-based structural rigidity outcome measures applied to the metastatic spine, have yielded 100% sensitivity, with 44% to 70% specificity, to predict fracture risk [104]. Volumetric assessments of tumour burden and dynamic quantification of bone density changes on CT imaging have also been shown to distinguish between those patients at risk of fracture and those who remain stable [105,106]. Further complication in the bony spine is that many vertebral compression fractures will not require mechanical stabilization and remain mechanically stable, with the presence of local healing or further volumetric collapse showing a relationship with long term stability [107].…”

“…Specimen specific FEA presents an alternative to structural rigidity analysis that is potentially more sensitive to changes in tissue properties and loading conditions at the expense of greater complexity. Biomechanical models generated from FEA that quantify vertebral stability have been applied to clinical data sets with some success [106,[108][109][110],highlighting the ability of biomechanically based guidelines to yield quantitative metrics that may aid in clinical decision making and intervention guidelines [72,76,111]. However, such approaches will require automated pipelines to realize clinical value.…”