In vivo assessment of architecture and micro-finite element analysis derived indices of mechanical properties of trabecular bone in the radius

Measurement of microstructural parameters of trabecular bone noninvasively in vivo is possible with high-resolution magnetic resonance (MR) imaging. These measurements may prove useful in the determination of bone strength and fracture risk, but must be related to other measures of bone properties. In this study in vivo MR imaging was used to derive trabecular bone structure measures and combined with micro-finite element analysis (microFE) to determine the effects of trabecular bone microarchitecture on bone mechanical properties in the distal radius. The subjects were studied in two groups: (I) postmenopausal women with normal bone mineral density (BMD) (n = 22, mean age 58 +/- 7 years) and (II) postmenopausal women with spine or femur BMD -1 SD to -2.5 SD below young normal (n = 37, mean age 62 +/- 11 years). MR images of the distal radius were obtained at 1.5 T, and measures such as apparent trabecular bone volume fraction (App BV/TV), spacing, number and thickness (App TbSp, TbN, TbTh) were derived in regions of interest extending from the joint line to the radial shaft. The high-resolution images were also used in a micro-finite element model to derive the directional Young's moduli (E1, E2 and E3), shear moduli (G12, G23 and G13) and anisotropy ratios such as E1/E3. BMD at the distal radius, lumbar spine and hip were assessed using dual-energy X-ray absorptiometry (DXA). Bone formation was assessed by serum osteocalcin and bone resorption by serum type I collagen C-terminal telopeptide breakdown products (serum CTX) and urinary CTX biochemical markers. The trabecular architecture displayed considerable anisotropy. Measures of BMD such as the ultradistal radial BMD were lower in the osteopenic group (

Assessment of cancellous bone mechanical properties from micro-FE models based on micro-CT, pQCT and MR images.

Recently, new micro-finite element (micro-FE) techniques have been introduced to calculate cancellous bone mechanical properties directly from high-resolution images of its internal architecture. Also recently, new peripheral quantitative computed tomography (pQCT) and magnetic resonance (MR) imaging techniques have been developed that can create images of whole bones in vivo with enough detail to visualize the internal cancellous bone architecture. In this study we aim to investigate if the calculation of cancellous bone mechanical properties from micro-FE models based on such new pQCT and MR images is feasible. Three bone specimens were imaged with the pQCT scanning system and the MR-imaging system. The specimens were scanned a second time using a micro-CT scanner with a much higher resolution. Digitized reconstructions were made based on each set of images and converted to micro-FE models from which the bone elastic properties were calculated. It was found that the results of both the pQCT and the MR-based FE-models compared well to those of the more accurate micro-CT based models in a qualitative sense, but correction factors will be needed to get accurate values

Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype. © 2020, The Author(s).Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]