Noninvasive Failure Load Prediction of Vertebrae with Simulated Lytic Defects and Biomaterial Augmentation

Abstract

The spine is the most common site for secondary bone metastases, and clinical management for fractures is based on size and geometry of the defect. About 75% of the bone needs to be damaged before lesions are detectable, so clinical tools should measure changes in both geometry and material properties. We have developed an automated, user-friendly, Spine Cancer Assessment (SCA) image-based analysis method that builds on a platform designed for clinical practice providing failure characteristics of vertebrae. The objectives of this study were to (1) validate SCA predictions with experimental failure load outcomes; (2) evaluate the planning capabilities for prophylactic vertebroplasty procedures; and (3) investigate the effect of computed tomography (CT) protocols on predicted failure loads. Twenty-one vertebrae were randomly divided into two groups: (1) simulated defect without treatment (negative control) [n = 9] and (2) with treatment [n = 12]. Defects were created and a polymeric biomaterial was injected into the vertebrae in the treated-defect group. Spines were scanned, reconstructed with two algorithms, and analyzed for fracture loads. To virtually plan for prophylactic intervention, vertebrae with empty lesions were simulated to be augmented with either poly(methyl methacrylate) (PMMA) or a novel bone replacement copolymer, poly(propylene fumarate-co-caprolactone) [P(PF-co-CL)]. Axial rigidities were calculated from the CT images. Failure loads, determined from the cross section with the lowest axial rigidity, were compared with experimental values. Predicted loads correlated well with experimental outcomes (R(2) = 0.73, p < 0.0001). Predictions from negative control specimens highly correlated with measured values (R(2) = 0.90, p < 0.0001). Although a similar correlation was obtained using both algorithms, the smooth reconstruction (B30) tended to underestimate predicted failure loads by ∼50% compared with the ∼10% underestimate of the sharp reconstruction (B70). Percent increase in failure loads after virtual vertebroplasty showed a higher increase in samples with PMMA compared with those with copolymer. The SCA method developed in this study calculated failure loads from quantitative computed tomography scans in vertebrae with simulated metastatic lytic defects, with or without treatment, facilitating clinical applicability and providing more reliable guidelines for physicians to select appropriate treatment options. Furthermore, the analysis could accommodate augmentation planning procedures that aimed to determine the optimum material that would increase vertebral body failure load.

FIG. 1.

Overview of the failure load prediction process. DICOM images were imported to the SCA image-based analysis method. Vertebrae were selected and defined in the sagittal and coronal planes to cover the entire vertebral body height. An ROI was defined on the lytic lesion and a Young's modulus value was assigned based on the material within the defect (negative control or copolymer). After defining the lesion, an oval mask was placed on the vertebra of interest and analyzed for fracture characteristics. DICOM, digital imaging and communications in medicine; ROI, region of interest; SCA, spine cancer assessment.

FIG. 2.

(A) Vertebral bodies were selected for analysis (green highlight shows the selected vertebral body; blue highlight shows an already defined vertebral body). (B) Two lines were placed on the sagittal and coronal planes to define the vertebral body range and size. (C) A three-dimensional ROI was delineated that corresponded to the lytic lesion. The ROI can be visualized in all three planes. Color images available online at www.liebertpub.com/tec

FIG. 3.

Schematic of analysis used in the SCA image-based analysis method. Hounsfield unit values in the image were converted to BMD based on a linear relationship obtained from the calibration phantom. A Young's modulus value (E [MPa]) was assigned to each voxel based on a density-dependent elastic modulus relationship previously established [Eq. (3)]. da corresponds to the pixel dimensions. BMD, bone mineral density.

FIG. 4.

Measured versus predicted failure load for all data (negative control and copolymer vertebrae).

FIG. 5.

Measured versus predicted failure load for the copolymer and negative control samples.

FIG. 6.

Measured versus predicted failure load for the negative control samples analyzed with two different computed tomographic reconstruction algorithms (B30 [smooth kernel] and B70 [sharp kernel]).

FIG. 7.

The spine cancer assessment image-based analysis method was used to calculate failure loads after virtually augmenting the negative control samples with two polymers, a copolymer or PMMA. Failure loads of the augmented vertebrae were analyzed using two computed tomographic reconstruction algorithms (B30 [smooth kernel] and B70 [sharp kernel]). PMMA, poly(methyl methacrylate).