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.

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.

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.

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.

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.

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.

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.

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 assisted 6-minute cycling test to assess endurance in children with a neuromuscular disorder.

INTRODUCTION: For late- or non-ambulant children with a neuromuscular disorder no suitable endurance tests are currently available. We developed the assisted 6-minute cycling test (A6MCT) for the legs and arms and investigated its psychometric properties in healthy boys and boys with Duchenne muscular dystrophy (DMD). METHODS: Ninety-nine healthy boys and 30 boys with DMD (12 wheelchair-dependent) performed the A6MCT. Seventy healthy boys also performed the 6-minute walk test (6MWT), and 23 boys performed the A6MCT twice within 2 weeks. Boys with DMD also performed the Motor Function Measure (MFM). RESULTS: The A6MCT was feasible for >90% of all boys. Boys with DMD achieved fewer cycling revolutions than controls. The A6MCT was positively correlated with the 6MWT and was reproducible in healthy boys, and it correlated with disease severity in boys with DMD. CONCLUSIONS: The A6MCT is a promising outcome measure for the follow-up of non-ambulant children with a neuromuscular disorder.

Step performance in persons with rheumatoid arthritis: a case-control study.

OBJECTIVE: To investigate factors that could lead to falls in patients with rheumatoid arthritis (RA). DESIGN: Case-control study. SETTING: Hospital. PARTICIPANTS: Patients with RA (n=15) and age- and sex-matched controls (n=15; mean ± SD age, 60.5 ± 7.1y). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Performance of participants on a step task. Furthermore, manual performance was assessed, as well as questionnaires for balance confidence, fear of falling, and activity level. RESULTS: Patients with RA showed nonsignificantly increased RTs (time to anticipatory postural adjustment and foot lift) and significantly increased movement times (MTs). Push-off force and step velocity were significantly lower in patients with RA. During a manual task, delayed RTs and MTs were seen. Moreover, lower levels of balance confidence and more fear of falling were reported in patients with RA. There were no differences in activity levels. CONCLUSIONS: When performing a quick step, patients with RA show delayed MT and step velocity and decreased push off. Because quick stepping often is used to avoid falls, the increase in step execution time might have undesirable consequences. Furthermore, patients with RA have lower balance confidence and more fear of falling than healthy controls; these factors also contribute to higher fall risk.