What is it like to organize a large-scale educational event for fellow students? A qualitative exploration of student participation in curriculum design.

BACKGROUND: Although students are increasingly involved in curriculum design, empirical research on practices of actual student participation is sparse. The purpose of this study is to explore the experiences of students who collaborated in the organizing committee of a large-scale educational event, the Radboud Student Conference (RSC), for fellow students. METHODS: We conducted three focus group interviews, in which 17 (bio) medical students of three different organizing teams shared their experiences regarding the organization of the large-scale teaching event. The analysis was conducted using thematic content analysis, in which the codes and codebook were constructed on the basis of the data. RESULTS: The following four themes were derived from the data. 1) Collaboration, which concentrated on fellow students, teachers who were involved as supervisors, and persons outside the organizing team such as caterers, educational support office members, lecturers, physicians and researchers. 2) Planning and division of labor, with students experiencing a mutual dependence and noticing a gradual improvement of their skills. 3) Freedom implies responsibility, which indicted that students experienced a significant freedom to develop the RSC week, but at the same time felt the responsibility to deliver a successful final week of the academic year. 4) Personal development, where students mentioned the opportunity to practice skills that differed from standard (bio) medical electives. CONCLUSIONS: We conclude that (bio) medical students are capable of bearing the responsibility to organize a large-scale educational event. Organizing the RSC was an educational experience in the form of cooperative and experiential learning which contributed to students' personal development. Organizing the event gave students both a sense of freedom and the responsibility to succeed. Supervision of faculty members seemed a prerequisite, and tended to be supportive rather than guiding.

Using a statistical shape model to estimate the knee landmarks for aligning femurs for femoral finite element models.

BACKGROUND AND OBJECTIVE: The BOne Strength (BOS) score is a CT-based tool to assess fracture risk for patients with femoral bone metastases using finite element (FE) models. Until now, the knee joint center (KJC) and centers of the condyles (CoCs) were needed to create the FE model, hence BOS scores of incompletely scanned femurs could not be calculated. In this study, a statistical shape model (SSM) was used to align FE models of femurs with a removed knee anatomy. The aim was to determine the effect of using an SSM with different proximal femur fractions on KJC and CoC locations, and on the BOS score. METHODS: QCT scans of 117 femurs were used to generate patient-specific FE models of the proximal femur. These models were aligned using the knee joint center (KJC), center of condyles (CoC) and femoral head center. The femurs were artificially shortened by removing 30 %, 50 % or 70 % of the femur. A recently developed SSM was used to reconstruct the distal femur. For each of the femur fractions, the difference between the original and SSM-reconstructed KJC and CoC were determined and the BOS scores were calculated. RESULTS: Although the individual differences between the original and SSM-reconstructed KJC and CoC location could be large, the effect on the individual BOS scores was limited. The SSM-reconstructed BOS scores were highly correlated to the original BOS scores. CONCLUSION: Using SSM to align femurs with a removed knee anatomy resulted in varying estimation of knee anatomy between patients but relatively accurate BOS scores.

The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis.

Bone ranks as the third most frequent tissue affected by cancer metastases, following the lung and liver. Bone metastases are often painful and may result in pathological fracture, which is a major cause of morbidity and mortality in cancer patients. To quantify fracture risk, finite element (FE) analysis has shown to be a promising tool, but metastatic lesions are typically not specifically segmented and therefore their mechanical properties may not be represented adequately. Deep learning methods potentially provide the opportunity to automatically segment these lesions and change the mechanical properties more adequately. In this study, our primary focus was to gain insight into the performance of an automatic segmentation algorithm for femoral metastatic lesions using deep learning methods and the subsequent effects on FE outcomes. The aims were to determine the similarity between manual segmentation and automatic segmentation; the differences in predicted failure load between FE models with automatically segmented osteolytic and mixed lesions and the models with CT-based lesion values (the gold standard); and the effect on the BOne Strength (BOS) score (failure load adjusted for body weight) and subsequent fracture risk assessments. From two patient cohorts, a total number of 50 femurs with osteolytic and mixed metastatic lesions were included in this study. The femurs were segmented from CT images and transferred into FE meshes. The material behavior was implemented as non-linear isotropic. These FE models were considered as gold standard (Finite Element no Segmented Lesion: FE-no-SL), whereby the local calcium equivalent density of both femur and metastatic lesion was extracted from CT-values. Lesions in the femur were manually segmented by two biomechanical experts after which final lesion segmentation for each femur was obtained based on consensus of opinions between two observers. Subsequently, a self-configuring variant of the popular deep learning model U-Net known as nnU-Net was used to automatically segment metastatic lesions within the femur. For these models with segmented lesions (Finite Element with Segmented Lesion: FE-with-SL), the calcium equivalent density within the metastatic lesions was set to zero after being segmented by the neural network, simulating absence of load-bearing capacity of these lesions. The models (either with or without automatically segmented lesions) were loaded incrementally in axial direction until failure was simulated. Dice coefficient was used to evaluate the similarity of the manual and automatic segmentation. Mean calcium equivalent density values within the automatically segmented lesions were calculated. Failure loads and patterns were determined. Furthermore, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for both groups by comparing the predictions to the occurrence or absence of actual fracture within the patient cohorts. The automatic segmentation algorithm performed in a none-robust manner. Dice coefficients describing the similarity between consented manual and automatic segmentations were relatively low (mean 0.45 ± standard deviation 0.33, median 0.54). Failure load difference between the FE-no-SL and FE-with-SL groups varied from 0 % to 48 % (mean 6.6 %). Correlation analysis of failure loads between the two groups showed a strong relationship (R2 > 0.9). From the 50 cases, four cases showed clear deviations for which models with automatic lesion segmentation (FE-with-SL) showed considerably lower failure loads. In the whole database including osteolytic and mixed lesions, sensitivity and NPV remained the same, but specificity and PPV decreased from 94 % to 83 %, and from 78 % to 54 % respectively from FE-no-SL to FE-with-SL. This study indicates that the nnU-Net yielded none-robust outcomes in femoral lesion segmentation and that other segmentation algorithms should be considered. However, the difference in failure pattern and failure load between FE models with automatically segmented osteolytic and mixed lesions were relatively small in most cases with a few exceptions. On the other hand, the accuracy of fracture risk assessment using the BOS score was lower compared to the FE-no-SL. In conclusion, this study showed that automatic lesion segmentation is a none-solved issue and therefore, quantifying lesion characteristics and the subsequent effect on the fracture risk using deep learning will remain challenging.

The application of an isotropic crushable foam model to predict the femoral fracture risk.

For biomechanical simulations of orthopaedic interventions, it is imperative to implement a material model that can realistically reproduce the nonlinear behavior of the bone structure. However, a proper material model that adequately combines the trabecular and cortical bone response is not yet widely identified. The current paper aims to investigate the possibility of using an isotropic crushable foam (ICF) model dependent on local bone mineral density (BMD) for simulating the femoral fracture risk. The elastoplastic properties of fifty-nine human femoral trabecular cadaveric bone samples were determined and combined with existing cortical bone properties to characterize two forms of the ICF model, a continuous and discontinuous model. Subsequently, the appropriateness of this combined material model was evaluated by simulating femoral fracture experiments, and a comparison with earlier published results of a softening Von-Mises (sVM) material model was made. The obtained mechanical properties of the trabecular bone specimens were comparable to previous findings. Furthermore, the ultimate failure load predicted by the simulations of femoral fractures was on average 79% and 90% for the continuous and discontinuous forms of the ICF model and 82% of the experimental value for the sVM material model. Also, the fracture locations predicted by ICF models were comparable to the experiments. In conclusion, a nonlinear material model dependent on BMD was characterized for human femoral bone. Our findings indicate that the ICF model could predict the femoral bone strength and reproduce the variable fracture locations in the experiments.

An innovative assessment tool for evaluating narrative feedback quality among Medicine and Biomedical Sciences students.

Objectives: To develop a reliable instrument to objectively assess feedback quality, to use it for assessment of the quality of students' narrative feedback and to be used as a self-assessment instrument for students in their learning process. Methods: In a retrospective cohort study, 635 feedback narratives, provided by small groups of Medicine and Biomedical Sciences undergraduate students, have been extracted from available quarterly curriculum evaluation surveys. A rubric was developed based on literature and contents of our feedback education. It consists of seven subitems and has a maximum score of 20 points (sufficient score: >10 points). Rubric reliability was evaluated using intra-class correlation. The rubric was tested by analysing the feedback narratives. To test progression, we compared rubric scores between study years with a Kruskal-Wallis analysis and Dunn's post-hoc testing with Bonferroni correction. Results: The rubric has an intra-class correlation of 0.894. First year students had a mean rubric score of 11.5 points (SD 3.6), second year students 12.4 (SD 3.4) and third year students 13.1 (SD 3.6). Kruskal-Wallis testing showed significant differences in feedback quality between study years (χ2(2, N=635) = 17.53, p<0.001). Dunn's post-hoc test revealed significant differences between study years one and two (p=0.012) and one and three (p<0.001). Conclusions: The developed rubric is a reliable instrument to assess narrative feedback quality. Students were able to provide feedback of sufficient quality and quality improved across study years. The instrument will allow students to assess themselves and learn where there is still room for improvement.

Length Changes of the Medial Patellofemoral Ligament During In Vivo Knee Motion: An Evaluation Using Dynamic Computed Tomography.

BACKGROUND: Medial patellofemoral ligament (MPFL) reconstruction is associated with high complication rates because of graft overloading from incorrect graft positioning. To improve clinical outcomes, it is crucial to gain a better understanding of MPFL elongation patterns. PURPOSE: To assess MPFL length changes in healthy knees from 0° to 90° of dynamic flexion and their relationship with anatomic parameters of the patellofemoral joint. STUDY DESIGN: Descriptive laboratory study. METHODS: Dynamic computed tomography scans of an active flexion-extension-flexion movement in 115 knees from 63 healthy participants were evaluated to construct knee joint models. Using these models, the MPFL length was measured as the shortest wrapping path from the Schöttle point on the femur to 3 insertion points on the superomedial border of the patella (proximal, central, and distal). MPFL length changes (%) relative to the length in full extension were calculated, and their correlations with the tibial tuberosity-trochlear groove distance, Caton-Deschamps index, and lateral trochlear inclination were analyzed. RESULTS: The proximal fiber was the longest in full extension and progressively decreased to a median length of -6.0% at 90° of flexion. The central fiber exhibited the most isometric pattern during knee flexion, showing a median maximal decrease of 2.8% relative to the full extension length and no evident elongation. The distal fiber first slightly decreased in length but increased at deeper flexion angles. The median overall length changes were 4.6, 4.7, and 5.7 mm for the proximal, central, and distal patellar insertion, respectively. These values were either not or very weakly correlated with the tibial tuberosity-trochlear groove distance, Caton-Deschamps index, and lateral trochlear inclination when the anatomic parameters were within the healthy range. CONCLUSION: The median MPFL length changed by approximately 5 mm between 0° and 90° of flexion. Proximally, the length continuously decreased, indicating slackening behavior. Distally, the length increased at deeper flexion angles, indicating tightening behavior. CLINICAL RELEVANCE: In MPFL reconstruction techniques utilizing the Schöttle point to establish the femoral insertion, one should avoid distal patellar insertion, as it causes elongation of the ligament, which may increase the risk for complications due to overloading.

Fracture risk assessment and evaluation of femoroplasty in metastatic proximal femurs. An in vivo CT-based finite element study.

The goal of this study was twofold. First, we aimed to evaluate the accuracy of a finite element (FE) model to predict bone fracture in cancer patients with proximal femoral bone metastases. Second, we evaluated whether femoroplasty could effectively reduce fracture risk. A total of 89 patients were included, with 101 proximal femurs affected with bone metastases. The accuracy of the model to predict fracture was evaluated by comparing the FE failure load, normalized for body weight, against the actual occurrence of fracture during a 6-month follow-up. Using a critical threshold, the model could identify whether femurs underwent fracture with a sensitivity of 92% and a specificity of 66%. A virtual treatment with femoroplasty was simulated in a subset of 34 out of the 101 femurs; only femurs with one or more well-defined lytic lesions were considered eligible for femoroplasty. We modeled their lesions, as well as the surrounding 4 mm of trabecular bone, to be augmented with bone cement. The simulation of femoroplasty increased the median failure load of the FE model by 57% for lesions located in the head/neck of the femur. At this lesion location, all high risk femurs that had fractured during follow-up effectively moved from a failure load below the critical threshold to a value above. For lesions located in the trochanteric region, no definite improvement in failure load was found. Although additional validation studies are required, our results suggest that femoroplasty can effectively reduce fracture risk for several osteolytic lesions in the femoral head/neck.

The effect of variations in CT scan protocol on femoral finite element failure load assessment using phantomless calibration.

Recently, it was shown that fracture risk assessment in patients with femoral bone metastases using Finite Element (FE) modeling can be performed using a calibration phantom or air-fat-muscle calibration and that non-patient-specific calibration was less favorable. The purpose of this study was to investigate if phantomless calibration can be used instead of phantom calibration when different CT protocols are used. Differences in effect of CT protocols on Hounsfield units (HU), calculated bone mineral density (BMD) and FE failure loads between phantom and two methods of phantomless calibrations were studied. Five human cadaver lower limbs were scanned atop a calibration phantom according to a standard scanning protocol and seven additional commonly deviating protocols including current, peak kilovoltage (kVp), slice thickness, rotation time, field of view, reconstruction kernel, and reconstruction algorithm. The HUs of the scans were calibrated to BMD (in mg/cm3) using the calibration phantom as well as using air-fat-muscle and non-patient-specific calibration, resulting in three models for each scan. FE models were created, and failure loads were calculated by simulating an axial load on the femur. HU, calculated BMD and failure load of all protocols were compared between the three calibration methods. The different protocols showed little variation in HU, BMD and failure load. However, compared to phantom calibration, changing the kVp resulted in a relatively large decrease of approximately 10% in mean HU and BMD of the trabecular and cortical region of interest (ROI), resulting in a 13.8% and 13.4% lower failure load when air-fat-muscle and non-patient-specific calibrations were used, respectively. In conclusion, while we observed significant correlations between air-fat-muscle calibration and phantom calibration as well as between non-patient-specific calibration and phantom calibration, our sample size was too small to prove that either of these calibration approaches was superior. Further studies are necessary to test whether air-fat-muscle or non-patient-specific calibration could replace phantom calibration in case of different scanning protocols.

A Patient-Specific Fracture Risk Assessment Tool for Femoral Bone Metastases: Using the Bone Strength (BOS) Score in Clinical Practice.

Patients with femoral metastases are at risk of fracturing bones. It is important to prevent fractures in order to maintain mobility and quality of life. The BOne Strength (BOS) score is based on a computed tomography (CT)-based patient-specific finite element (FE) computer model that objectively calculates bone strength. In this pilot study, the added clinical value of the BOS score towards treatment-related decision making was assessed. In December 2019, the BOS score was implemented in four radiotherapy centers. The BOS scores and fracture risks of individual patients were calculated and returned to the physician to assist in treatment decisions. The physicians filled out a questionnaire, which was qualitatively analyzed. A follow-up to identify fractures and/or death was performed after six months. Until June 2021, 42 BOS scores were delivered (20 high, 9 moderate, and 13 low fracture risk). In 48%, the BOS score led to an adaptation of treatment plans. Physicians indicated that the BOS score provided objective insight into fracture risk, was reassuring for physicians and patients, and improved multidisciplinary discussions and shared decision making. In conclusion, the BOS score is an objective tool to assess fracture risk in femoral bone metastases and aids physicians and patients in making a more informed decision regarding the most appropriate treatment.

Evaluation of inter- and intra-operator reliability of manual segmentation of femoral metastatic lesions.

PURPOSE: Accurate identification of metastatic lesions is important for improvement in biomechanical models that calculate the fracture risk of metastatic bones. The aim of this study was therefore to assess the inter- and intra-operator reliability of manual segmentation of femoral metastatic lesions. METHODS: CT scans of 54 metastatic femurs (19 osteolytic, 17 osteoblastic, and 18 mixed) were segmented two times by two operators. Dice coefficients (DCs) were calculated adopting the quantification that a DC˃0.7 indicates good reliability. RESULTS: Generally, rather poor inter- and intra-operator reliability of lesion segmentation were found. Inter-operator DCs were 0.54 (± 0.28) and 0.50 (± 0.32) for the first and second segmentations, respectively, whereas intra-operator DCs were 0.56 (± 0.28) for operator I and 0.71 (± 0.23) for operator II. Larger lesions scored significantly higher DCs in comparison with smaller lesions. Of the femurs with larger mean segmentation volumes, 83% and 93% were segmented with good inter- and intra-operator DCs (> 0.7), respectively. There was no difference between the mean DCs of osteolytic, osteoblastic, and mixed lesions. CONCLUSION: Manual segmentation of femoral bone metastases is very challenging and resulted in unsatisfactory mean reliability values. There is a need for development of a segmentation protocol to reduce the inter- and intra-operator segmentation variation as the first step and use of computer-assisted segmentation tools as a second step as this study shows that manual segmentation of femoral metastatic lesions is highly challenging.

Nonlinear voxel-based finite element model for strength assessment of healthy and metastatic proximal femurs.

Nonlinear finite element (FE) models can accurately quantify bone strength in healthy and metastatic femurs. However, their use in clinical practice is limited since state-of-the-art implementations using tetrahedral meshes involve a lot of manual work for which specific modelling software and engineering knowledge are required. Voxel-based meshes could enable the transition since they are robust and can be highly automated. Therefore, the aim of this work was to bridge the modelling gap between the tetrahedral and voxel-based approach. Specifically, we validated a nonlinear voxel-based FE method relative to experimental data from 20 femurs with and without artificial metastases that had been mechanically loaded until failure. CT scans of the femurs were segmented and automatically converted into a voxel-based mesh with hexahedral elements. Nonlinear material properties were implemented in an open-source linear voxel-based FE solver by adding an additional loop to the routine such that the material properties could be adapted after each increment. Bone strength, quantified as the maximum force in the force-displacement curve, was evaluated. The results were compared to a previously established nonlinear tetrahedral FE approach as well as to the experimentally measured bone strength. The voxel-based FE model predicted the experimental bone strength very well both for healthy (R2 = 0.90, RMSE = 0.88 kN) and metastatic femurs (R2 = 0.93, RMSE = 0.64 kN). The model precision and accuracy were very similar to the ones obtained with the tetrahedral model (R2 = 0.90/0.93, RMSE = 0.90/0.64 kN for intact/metastatic respectively). The more intuitive voxel-based meshes thus quantified macroscale femoral strength equally well as state-of-the-art tetrahedral models. The robustness, high level of automation and time-efficiency (< 30 min) of the implemented workflow offer great potential for developing FE models to improve fracture risk prediction in clinical practice.

Finite element models for fracture prevention in patients with metastatic bone disease. A literature review.

Patients with bone metastases have an increased risk to sustain a pathological fracture as lytic metastatic lesions damage and weaken the bone. In order to prevent fractures, prophylactic treatment is advised for patients with a high fracture risk. Mechanical stabilization of the femur can be provided through femoroplasty, a minimally invasive procedure where bone cement is injected into the lesion, or through internal fixation with intra- or extramedullary implants. Clinicians face the task of determining whether or not prophylactic treatment is required and which treatment would be the most optimal. Finite element (FE) models are promising tools that could support this decision process. The aim of this paper is to provide an overview of the state-of-the-art in FE modeling for the treatment decision of metastatic bone lesions in the femur. First, we will summarize the clinical and mechanical results of femoroplasty as a prophylactic treatment method. Secondly, current FE models for fracture risk assessment of metastatic femurs will be reviewed and the remaining challenges for clinical implementation will be discussed. Thirdly, we will elaborate on the simulation of femoroplasty in FE models and discuss future opportunities. Femoroplasty has already proven to effectively relieve pain and improve functionality, but there remains uncertainty whether it provides sufficient mechanical strengthening to prevent pathological fractures. FE models could help to select appropriate candidates for whom femoroplasty provides sufficient increase in strength and to further improve the mechanical benefit by optimizing the locations for cement augmentation.

Patient-specific finite element computer models improve fracture risk assessments in cancer patients with femoral bone metastases compared to clinical guidelines.

PURPOSE: To determine whether patient-specific finite element (FE) computer models are better at assessing fracture risk for femoral bone metastases compared to clinical assessments based on axial cortical involvement on conventional radiographs, as described in current clinical guidelines. METHODS: Forty-five patients with 50 femoral bone metastases, who were treated with palliative radiotherapy for pain, were included (64% single fraction (8Gy), 36% multiple fractions (5 or 6x4Gy)) and were followed for six months to determine whether they developed a pathological femoral fracture. All plain radiographs available within a two month period prior to radiotherapy were obtained. Patient-specific FE models were constructed based on the geometry and bone density obtained from the baseline quantitative CT scans used for radiotherapy planning. Femoral failure loads normalized for body weight (BW) were calculated. Patients with a failure load of 7.5 x BW or lower were identified as having high fracture risk, whereas patients with a failure load higher than 7.5 x BW were classified as low fracture risk. Experienced assessors measured axial cortical involvement on conventional radiographs. Following clinical guidelines, patients with lesions larger than 30mm were identified as having a high fracture risk. FE predictions were compared to clinical assessments by means of diagnostic accuracy values (sensitivity, specificity and positive (PPV) and negative predictive values (NPV)). RESULTS: Seven femurs (14%) fractured during follow-up. Median time to fracture was 8 weeks. FE models were better at assessing fracture risk in comparison to axial cortical involvement (sensitivity 100% vs. 86%, specificity 74% vs. 42%, PPV 39% vs. 19%, and NPV 100% vs. 95%, for the FE computer model vs. axial cortical involvement, respectively). CONCLUSIONS: Patient-specific FE computer models improve fracture risk assessments of femoral bone metastases in advanced cancer patients compared to clinical assessments based on axial cortical involvement, which is currently used in clinical guidelines.

Calibration with or without phantom for fracture risk prediction in cancer patients with femoral bone metastases using CT-based finite element models.

The objective of this study was to develop a new calibration method that enables calibration of Hounsfield units (HU) to bone mineral densities (BMD) without the use of a calibration phantom for fracture risk prediction of femurs with metastases using CT-based finite element (FE) models. Fifty-seven advanced cancer patients (67 femurs with bone metastases) were CT scanned atop a separate calibration phantom using a standardized protocol. Non-linear isotropic FE models were constructed based on the phantom calibration and on two phantomless calibration methods: the "air-fat-muscle" and "non-patient-specific" calibration. For air-fat-muscle calibration, peaks for air, fat and muscle tissue were extracted from a histogram of the HU in a standardized region of interest including the patient's right leg and surrounding air. These CT peaks were linearly fitted to reference "BMD" values of the corresponding tissues to obtain a calibration function. For non-patient-specific calibration, an average phantom calibration function was used for all patients. FE failure loads were compared between phantom and phantomless calibrations. There were no differences in failure loads between phantom and air-fat-muscle calibration (p = 0.8), whereas there was a significant difference between phantom and non-patient-specific calibration (p<0.001). Although this study was not designed to investigate this, in four patients who were scanned using an aberrant reconstruction kernel, the effect of the different kernel seemed to be smaller for the air-fat-muscle calibration compared to the non-patient-specific calibration. With the air-fat-muscle calibration, clinical implementation of the FE model as tool for fracture risk assessment will be easier from a practical and financial viewpoint, since FE models can be made using everyday clinical CT scans without the need of concurrent scanning of calibration phantoms.

A unified theory for osteonal and hemi-osteonal remodeling.

The process of bone remodeling is carried out by 'basic multicellular units' of osteoclasts and osteoblasts. Osteoclasts excavate a resorption space that is subsequently filled with new bone by osteoblasts. In cortical bone osteoclasts dig tunnels through solid bone, in cancellous bone they dig trenches across the trabecular surface. Osteoblasts fill these tunnels and trenches, creating osteons and hemi-osteons, respectively. Both the osteons of cortical bone and the trabeculae of cancellous bone are aligned to the dominant loading direction, indicating that BMU's are mechanically regulated. How mechanical forces guide these cells is still uncertain. We hypothesize that strain-induced osteocyte signals inhibit osteoclast activity and stimulate osteoblast activity. This hypothesis was implemented in a finite element-based bone adaptation model, that was extended with a cell simulation model. This allowed us to examine tunneling and trenching by osteoclasts. We found that our simulations capture key features of BMU-based remodeling: (1) cortical BMU's create load-aligned osteons; (2) cancellous BMU's move across the surface of trabeculae instead of piercing them; (3) resorption-formation coupling occurs in response to strains around resorption sites; and (4) resorbing osteoclasts target nearby regions of osteocyte death, thus providing a mechanism for bone repair.

Effect of different CT scanners and settings on femoral failure loads calculated by finite element models.

In a multi-center patient study, using different CT scanners, CT-based finite element (FE) models are utilized to calculate failure loads of femora with metastases. Previous studies showed that using different CT scanners can result in different outcomes. This study aims to quantify the effects of (i) different CT scanners; (ii) different CT protocols with variations in slice thickness, field of view (FOV), and reconstruction kernel; and (iii) air between calibration phantom and patient, on Hounsfield Units (HU), bone mineral density (BMD), and FE failure load. Six cadaveric femora were scanned on four CT scanners. Scans were made with multiple CT protocols and with or without an air gap between the body model and calibration phantom. HU and calibrated BMD were determined in cortical and trabecular regions of interest. Non-linear isotropic FE models were constructed to calculate failure load. Mean differences between CT scanners varied up to 7% in cortical HU, 6% in trabecular HU, 6% in cortical BMD, 12% in trabecular BMD, and 17% in failure load. Changes in slice thickness and FOV had little effect (≤4%), while reconstruction kernels had a larger effect on HU (16%), BMD (17%), and failure load (9%). Air between the body model and calibration phantom slightly decreased the HU, BMD, and failure loads (≤8%). In conclusion, this study showed that quantitative analysis of CT images acquired with different CT scanners, and particularly reconstruction kernels, can induce relatively large differences in HU, BMD, and failure loads. Additionally, if possible, air artifacts should be avoided. © 2018 Orthopaedic Research Society. © 2018 The Authors. Journal of Orthopaedic Research® Published by Wiley Periodicals, Inc. on behalf of the Orthopaedic Research Society. J Orthop Res.

Case-specific non-linear finite element models to predict failure behavior in two functional spinal units.

Current finite element (FE) models predicting failure behavior comprise single vertebrae, thereby neglecting the role of the posterior elements and intervertebral discs. Therefore, this study aimed to develop a more clinically relevant, case-specific non-linear FE model of two functional spinal units able to predict failure behavior in terms of (i) the vertebra predicted to fail; (ii) deformation of the specimens; (iii) stiffness; and (iv) load to failure. For this purpose, we also studied the effect of different bone density-mechanical properties relationships (material models) on the prediction of failure behavior. Twelve two functional spinal units (T6-T8, T9-T11, T12-L2, and L3-L5) with and without artificial metastases were destructively tested in axial compression. These experiments were simulated using CT-based case-specific non-linear FE models. Bone mechanical properties were assigned using four commonly used material models. In 10 of the 11 specimens our FE model was able to correctly indicate which vertebrae failed during the experiments. However, predictions of the three-dimensional deformations of the specimens were less promising. Whereas stiffness of the whole construct could be strongly predicted (R2 = 0.637-0.688, p < 0.01), we obtained weak correlations between FE predicted and experimentally determined load to failure, as defined by the total reaction force exhibiting a drop in force (R2 = 0.219-0.247, p > 0.05). Additionally, we found that the correlation between predicted and experimental fracture loads did not strongly depend on the material model implemented, but the stiffness predictions did. In conclusion, this work showed that, in its current state, our FE models may be used to identify the weakest vertebra, but that substantial improvements are required in order to quantify in vivo failure loads. © 2018 The Authors. Journal of Orthopaedic Research® Published by Wiley Periodical, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 36:3208-3218, 2018.

Inducing targeted failure in cadaveric testing of 3-segment spinal units with and without simulated metastases.

We propose an experimental setup and protocol able to induce targeted failure of the middle vertebra in 3-segment spinal units and to capture the specimens' deformation in their post-failure state. Sixteen 3-segment spinal units with and without artificial metastases were destructively tested in axial compression using one of two failure criteria; either: (A) A clear drop in force (>10-15% of peak force) (n = 4); or (B) A minimum displacement of 5 mm (n = 12). Subsequently, the specimens were fully fixated in polymethylmethacrylate (PMMA), thereby preserving their post-failure state. Pre- and post-experiment computed tomography (CT) scans were acquired to determine the occurrence of failure in one of the vertebral bodies. All specimens were successfully fixated in their post-failure state. When applying failure criterion A, two specimens showed signs of failure. When applying failure criterion B, all specimens showed signs of failure; in 9 out of 12 specimens this occurred in the middle vertebrae only. In conclusion, this research provides an experimental setup and protocol able to induce targeted failure of 3-segment spinal units and to capture the specimens' deformation in their post-failure state. Furthermore, this study illustrates the importance of an adequate failure criterion for successful simulation of vertebral fractures in an experimental setup.

Limited short-term effect of palliative radiation therapy on quantitative computed tomography-derived bone mineral density in femora with metastases.

PURPOSE: The aim of this study was to determine the effect of single fraction (SF) and multiple fraction (MF) radiation therapy (RT) on bone mineral density (BMD) in patients with cancer and bone metastases in the proximal femur. We studied this effect in the radiation field and within metastatic lesions, and differentiated between lytic, blastic, and mixed lesions. METHODS AND MATERIALS: This prospective cohort study comprised 42 patients with painful bone metastases, including 47 irradiated femora with 52 metastatic lesions in the proximal femur. Patients received either 8 Gy SF or 20 to 24 Gy in 5 to 6 fractions (MF). Quantitative computed tomography scans were obtained before RT and 4 and 10 weeks after the initial scan. Patients who received MF additionally underwent quantitative computed tomography on the final day of their treatment. Automated image registration was performed. Mean BMD was determined at each time point for each proximal femur (region of interest [ROI]-PF) and in greater detail for a region of interest that contained the metastatic lesion (ROI-ML). Statistical analysis was performed using linear mixed models. RESULTS: No significant differences in mean BMD were found between SF or MF RT over all time points in both ROI-PF and ROI-ML. Mean BMD did not change in ROI-PF with lytic and mixed lesions, but mean BMD in ROI-PF with blastic lesions increased to 109%. Comparably, when focused on ROI-ML, no differences in mean BMD were observed in lytic ROI-ML but mean BMD in mixed and blastic ROI-ML increased up to 105% and 121%, respectively. CONCLUSIONS: Ten weeks after palliative radiation therapy in patients with femoral metastatic lesions, a limited increase in BMD was seen with no beneficial effect of MF over SF RT. BMD in lytic lesions was unchanged but slightly increased in mixed and blastic lesions.

The effect of radiotherapy, and radiotherapy combined with bisphosphonates or RANK ligand inhibitors on bone quality in bone metastases. A systematic review.

PURPOSE: The role of radiotherapy in stabilizing metastatic bones is unclear. This systematic review assessed the effects of (1) radiotherapy, (2) radiotherapy combined with bisphosphonates, and (3) radiotherapy combined with RANK ligand (RANKL) inhibitors on bone quality and bone strength in bone metastases originating from solid tumors. METHODS: Pubmed, EMBASE and the Cochrane Library were searched. Any type of study design and type and dose of radiotherapy, bisphosphonates and RANKL inhibitors were allowed. RESULTS: 39 articles were identified. Animal studies showed that radiotherapy had similar effects on bone quality and strength as receiving no treatment, whereas adding bisphosphonates to radiotherapy restored bone quality and strength. In patient studies, bone density increased after radiotherapy and radiotherapy combined with bisphosphonates. However, due to the often non-optimal study design and study quality, it was unclear whether this increase could be attributed to these treatments. There was insufficient evidence to assess the additional effect of bisphosphonates or RANKL inhibitors. CONCLUSION: Despite the clinical experience that radiotherapy is an effective treatment for bone metastases, there was no sufficient evidence for a positive effect on bone quality and fracture risk. Animal studies showed that adding bisphosphonates to radiotherapy restored bone quality and strength, whereas this was not proven in patients. There were no studies addressing the adjunct effect of RANKL inhibitors to radiotherapy. Although associated with several methodological, practical and ethical challenges, randomized controlled trials are needed.