After anterior cruciate ligament reconstruction one of the risk factors for graft (re-)rupture is an increased posterior tibial slope (PTS). The current treatment for PTS is a high tibial osteotomy (HTO). This is a free-hand method, with 1 degree of tibial slope correction considered to be equal to 1 or even 1.67 mm of the anterior wedge resection. Error rates in the frontal plane reported in literature vary from 1 – 8.6 degrees, and in the sagittal plane outcomes in a range of 2 – 8 degrees are reported when planned on PTSs of 3 – 5 degrees. Therefore, the free-hand method is considered to have limited accuracy. It is expected that HTO becomes more accurate with patient specific saw guides (PSGs), with an accuracy margin reported in literature of 2 degrees. This proof of concept porcine cadaver case study aimed to investigate whether the use of PSGs improves the accuracy of HTO to less than 2 degrees. Secondly, the reproducibility of tibial slope measurement was evaluated. Preoperative MRI images of porcine cadaver knees (n = 3) were used to create 3D anatomical bone models (Mimics, Materialise, Belgium). These 3D models were subsequently used to develop PSGs (3-Matic, Materialise, Belgium) to correct all tibias for 3 degrees PTS and 4 degrees varus. The PSG mediated HTOs were performed by an experienced orthopaedic surgeon, after which postoperative MRI images were obtained. 3D anatomical models of postoperative tibias were created, and tibial slopes were assessed on both pre- and postoperative tibias. The tibial slope was defined as the angle between the mechanical axis and 3D tibial reference plane in the frontal and sagittal plane. The accuracy of the PSG mediated HTO (median and range) was defined as the difference in all possible combinations of the preoperatively planned and postoperatively obtained tibial slopes. To ensure reproducibility, the pre- and postoperative tibial slopes were measured thrice by one observer. The intra-class correlation coefficients (ICCs) were subsequently calculated to assess the intra-rater reliability (SPSS, IBM Corp., Armonk, N.Y., USA).Introduction and Objective
Materials and Methods
Patients with cancer and bone metastases can have an increased risk of fracturing their femur. Treatment is based on the impending fracture risk: patients with a high fracture risk are considered for prophylactic surgery, whereas low fracture risk patients are treated conservatively with radiotherapy to decrease pain. Current clinical guidelines suggest to determine fracture risk based on axial cortical involvement of the lesion on conventional radiographs, but that appears to be difficult. Therefore, we developed a patient-specific finite element (FE) computer model that has shown to be able to predict fracture risk in an experimental setting and in patients. The goal of this study was to determine whether patient-specific finite element (FE) computer models are better at predicting fracture risk for femoral bone metastases compared to clinical assessments based on axial cortical involvement on conventional radiographs, as described in current clinical guidelines. 45 patients (50 affected femurs) affected with predominantly lytic bone metastases who were treated with palliative radiotherapy for pain were included. CT scans were made and patients were followed for six months to determine whether or not they fractured their femur. Non-linear isotropic FE models were created with the patient-specific geometry and bone density obtained from the CT scans. Subsequently, an axial load was simulated on the models mimicking stance. Failure loads normalized for bodyweight (BW) were calculated for each femur. High and low fracture risks were determined using a failure load of 7.5 × BW as a threshold. Experienced assessors measured axial cortical involvement on conventional radiographs. Following clinical guidelines, patients with lesions larger than 30 mm 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)). Seven femurs (14%) fractured during follow-up. Median time to fracture was 8 weeks. FE models were better at predicting fracture risk in comparison to clinical assessments based on 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). We concluded that patient-specific FE computer models improve fracture risk predictions of femoral bone metastases in advanced cancer patients compared to clinical assessments based on axial cortical involvement, which is currently used in clinical guidelines. Therefore, we are initiating a pilot for clinical implementation of the FE model.
Patients with advanced cancer can develop bone metastases in the femur which are often painful and increase the risk of pathological fracture. Accurate segmentation of bone metastases is, amongst others, important to improve patient-specific computer models which calculate fracture risk, and for radiotherapy planning to determine exact radiation fields. Deep learning algorithms have shown to be promising to improve segmentation accuracy for metastatic lesions, but require reliable segmentations as training input. The aim of this study was to investigate the inter- and intra-operator reliability of manual segmentation of femoral metastatic lesions and to define a set of lesions which can serve as a training dataset for deep learning algorithms. F CT-scans of 60 advanced cancer patients with a femur affected with bone metastases (20 osteolytic, 20 osteoblastic and 20 mixed) were used in this study. Two operators were trained by an experienced radiologist and then segmented the metastatic lesions in all femurs twice with a four-week time interval. 3D and 2D Dice coefficients (DCs) were calculated to quantify the inter- and intra-operator reliability of the segmentations. We defined a DC>0.7 as good reliability, in line with a statistical image segmentation study. Mean first and second inter-operator 3D-DCs were 0.54 (±0.28) and 0.50 (±0.32), respectively. Mean intra-operator I and II 3D-DCs were 0.56 (±0.28) and 0.71 (±0.23), respectively. Larger lesions (>60 cm3) scored higher DCs in comparison with smaller lesions. This study reveals that manual segmentation of metastatic lesions is challenging and that the current manual segmentation approach resulted in dissatisfying outcomes, particularly for lesions with small volumes. However, segmentation of larger lesions resulted in a good inter- and intra-operator reliability. In addition, we were able to select 521 slices with good segmentation reliability that can be used to create a training dataset for deep learning algorithms. By using deep learning algorithms, we aim for more accurate automated lesion segmentations which might be used in computer modelling and radiotherapy planning.
Trabecular metal (TM) cones are designed to fill up major bone defects in total knee arthroplasty. Tibial components can be implanted in combination with a stem, but it is unclear if this is necessary after reconstruction with a TM cone. Implanting a stem may give extra stability, but may also have negative side-effects. Aim of this study was to investigate stability and strain distribution of a tibial plateau reconstruction with a TM cone while the tibal component is implanted with and without a stem, and whether prosthetic stability was influenced by bone mineral density (BMD). Tibial revision arthroplasties were performed after reconstruction of an AORI 2B bone defect with TM cones. Plateaus were implanted in seven pairs of cadaveric tibiae; of each pair, one was implanted with and the other without stem. All specimens were loaded to one bodyweight alternating between the medial and lateral tibia plateau. Implant-bone micro motions, bone strains, BMD and correlations were measured and/or calculated.Background
Methods
Fretting at modular junctions is thought to be a ‘mechanically assisted’ corrosion phenomenon, initiated by mechanical factors that lead to increased contact stresses and micromotions at the taper interface. We adopted a finite element approach to model the head-taper junction, to analyse the contact mechanics at the taper interface. We investigated the effect of assembly force and angle on contact pressures and micromotions, during loads commonly used to test hip implants, to demonstrate the importance of a good assembly during surgery. Models of the Bimetric taper and adaptor were created, with elastic-plastic material properties based on material tests with the actual implant alloy. FE contact conditions were validated against push-on and pull-off experiments. The models were loaded according to ISO 7206-4 and −6, after being assembled at 2-4-15kN, both axially and at a 30° angle. Average micromotions and contact pressures were analysed, and a wear score was calculated based on the contact pressures and micromotions.Background
Methods
Our statistical shape analysis showed that size is the primary geometrical variation factor in the medial meniscus. Shape variations are primarily focused in the posterior horn, suggesting that these variations could influence cartilage contact pressures. Variations in meniscal geometry are known to influence stresses and strains inside the meniscus and the articulating cartilage surfaces. This geometry-dependent functioning emphasizes that understanding the natural variation in meniscus geometry is essential for a correct selection of allograft menisci and even more crucial for the definition of different sizes for synthetic meniscal implants. Moreover, the design of such implants requires a description of 3D meniscus geometry. Therefore, the aim of this study was to quantify 3D meniscus geometry and to determine whether variation in medial meniscus geometry is size or shape driven.Summary
Introduction
