Proteomic Characterization of Undifferentiated Small Round Cell Sarcomas With EWSR1 and CIC::DUX4 Translocations Reveals Diverging Tumor Biology and Distinct Diagnostic Markers.

Undifferentiated small round cell sarcomas (USRS) of bone and soft tissue are a group of tumors with heterogenic genomic alterations sharing similar morphology. In the present study, we performed a comparative large-scale proteomic analysis of USRS (n = 42) with diverse genomic translocations including classic Ewing sarcomas with EWSR1::FLI1 fusions (n = 24) or EWSR1::ERG fusions (n = 4), sarcomas with an EWSR1 rearrangement (n = 2), CIC::DUX4 fusion (n = 8), as well as tumors classified as USRS with no genetic data available (n = 4). Proteins extracted from formalin-fixed, paraffin-embedded pretherapeutic biopsies were analyzed qualitatively and quantitatively using shotgun mass spectrometry (MS). More than 8000 protein groups could be quantified using data-independent acquisition. Unsupervised hierarchical cluster analysis based on proteomic data allowed stratification of the 42 cases into distinct groups reflecting the different molecular genotypes. Protein signatures that significantly correlated with the respective genomic translocations were identified and used to generate a heatmap of all 42 sarcomas with assignment of cases with unknown molecular genetic data to either the EWSR1- or CIC-rearranged groups. MS-based prediction of sarcoma subtypes was molecularly confirmed in 2 cases where next-generation sequencing was technically feasible. MS also detected proteins routinely used in the immunohistochemical approach for the differential diagnosis of USRS. BCL11B highly expressed in Ewing sarcomas, and BACH2 as well as ETS-1 highly expressed in CIC::DUX4-associated sarcomas, were among proteins identified by the present proteomic study, and were chosen for immunohistochemical confirmation of MS data in our study cohort. Differential expressions of these 3 markers in the 2 genetic groups were further validated in an independent cohort of n = 34 USRS. Finally, our proteomic results point toward diverging signaling pathways in the different USRS subgroups.

Recommender-based bone tumour classification with radiographs-a link to the past.

OBJECTIVES: To develop an algorithm to link undiagnosed patients to previous patient histories based on radiographs, and simultaneous classification of multiple bone tumours to enable early and specific diagnosis. MATERIALS AND METHODS: For this retrospective study, data from 2000 to 2021 were curated from our database by two orthopaedic surgeons, a radiologist and a data scientist. Patients with complete clinical and pre-therapy radiographic data were eligible. To ensure feasibility, the ten most frequent primary tumour entities, confirmed histologically or by tumour board decision, were included. We implemented a ResNet and transformer model to establish baseline results. Our method extracts image features using deep learning and then clusters the k most similar images to the target image using a hash-based nearest-neighbour recommender approach that performs simultaneous classification by majority voting. The results were evaluated with precision-at-k, accuracy, precision and recall. Discrete parameters were described by incidence and percentage ratios. For continuous parameters, based on a normality test, respective statistical measures were calculated. RESULTS: Included were data from 809 patients (1792 radiographs; mean age 33.73 ± 18.65, range 3-89 years; 443 men), with Osteochondroma (28.31%) and Ewing sarcoma (1.11%) as the most and least common entities, respectively. The dataset was split into training (80%) and test subsets (20%). For k = 3, our model achieved the highest mean accuracy, precision and recall (92.86%, 92.86% and 34.08%), significantly outperforming state-of-the-art models (54.10%, 55.57%, 19.85% and 62.80%, 61.33%, 23.05%). CONCLUSION: Our novel approach surpasses current models in tumour classification and links to past patient data, leveraging expert insights. CLINICAL RELEVANCE STATEMENT: The proposed algorithm could serve as a vital support tool for clinicians and general practitioners with limited experience in bone tumour classification by identifying similar cases and classifying bone tumour entities. KEY POINTS: • Addressed accurate bone tumour classification using radiographic features. • Model achieved 92.86%, 92.86% and 34.08% mean accuracy, precision and recall, respectively, significantly surpassing state-of-the-art models. • Enhanced diagnosis by integrating prior expert patient assessments.

Numerical evaluation of internal femur osteosynthesis based on a biomechanical model of the loading in the proximal equine hindlimb.

Femoral fractures are often considered lethal for adult horses because femur osteosynthesis is still a surgical challenge. For equine femur osteosynthesis, primary stability is essential, but the detailed physiological forces occurring in the hindlimb are largely unknown. The objective of this study was to create a numerical testing environment to evaluate equine femur osteosynthesis based on physiological conditions. The study was designed as a finite element analysis (FEA) of the femur using a musculoskeletal model of the loading situation in stance. Relevant forces were determined in the musculoskeletal model via optimization. The treatment of four different fracture types with an intramedullary nail was investigated in FEA with loading conditions derived from the model. The analyzed diaphyseal fracture types were a transverse (TR) fracture, two oblique fractures in different orientations (OB-ML: medial-lateral and OB-AP: anterior-posterior) and a "gap" fracture (GAP) without contact between the fragments. For the native femur, the most relevant areas of increased stress were located distally to the femoral head and proximally to the caudal side of the condyles. For all fracture types, the highest stresses in the implant material were present in the fracture-adjacent screws. Maximum compressive (-348 MPa) and tensile stress (197 MPa) were found for the GAP fracture, but material strength was not exceeded. The mathematical model was able to predict a load distribution in the femur of the standing horse and was used to assess the performance of internal fixation devices via FEA. The analyzed intramedullary nail and screws showed sufficient stability for all fracture types.

Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors.

OBJECTIVES: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists. METHODS: In 880 patients (age 33.1 ± 19.4 years, 395 women) diagnosed with malignant (n = 213, 24.2%) or benign (n = 667, 75.8%) primary bone tumors, preoperative radiographs were obtained, and the diagnosis was established using histopathology. Data was split 70%/15%/15% for training, validation, and internal testing. Additionally, 96 patients from another institution were obtained for external testing. Machine learning models were developed and validated using radiomic features and demographic information. The performance of each model was evaluated on the test sets for accuracy, area under the curve (AUC) from receiver operating characteristics, sensitivity, and specificity. For comparison, the external test set was evaluated by two radiology residents and two radiologists who specialized in musculoskeletal tumor imaging. RESULTS: The best machine learning model was based on an artificial neural network (ANN) combining both radiomic and demographic information achieving 80% and 75% accuracy at 75% and 90% sensitivity with 0.79 and 0.90 AUC on the internal and external test set, respectively. In comparison, the radiology residents achieved 71% and 65% accuracy at 61% and 35% sensitivity while the radiologists specialized in musculoskeletal tumor imaging achieved an 84% and 83% accuracy at 90% and 81% sensitivity, respectively. CONCLUSIONS: An ANN combining radiomic features and demographic information showed the best performance in distinguishing between benign and malignant bone lesions. The model showed lower accuracy compared to specialized radiologists, while accuracy was higher or similar compared to residents. KEY POINTS: • The developed machine learning model could differentiate benign from malignant bone tumors using radiography with an AUC of 0.90 on the external test set. • Machine learning models that used radiomic features or demographic information alone performed worse than those that used both radiomic features and demographic information as input, highlighting the importance of building comprehensive machine learning models. • An artificial neural network that combined both radiomic and demographic information achieved the best performance and its performance was compared to radiology readers on an external test set.

Arthroscopic lateral retinacular release improves patello-femoral and femoro-tibial kinematics in patients with isolated lateral retinacular tightness.

PURPOSE: Arthroscopic lateral retinacular release (LRR) has long been considered the gold standard for the treatment for anterior knee pain caused by lateral retinacular tightness (LRT). However, one-third of patients experience continuous pain postoperatively, which is thought to be related to persistent maltracking of the patella and altered femoro-tibial kinematics. Therefore, the aim of the present study was to simultaneously assess femoro-tibial and patello-femoral kinematics and identify the influence of arthroscopic LRR. METHODS: Sixteen healthy volunteers and 12 patients with unilateral, isolated LRT were prospectively included. Open MRI scans with and without isometric quadriceps contraction were performed in 0°, 30° and 90° of knee flexion preoperatively and at 12 months after surgery. Patellar shift, tilt angle, patello-femoral contact area and magnitude of femoro-tibial rotation were calculated by digital image processing. RESULTS: Postoperatively, patellar shift was significantly reduced at 90° of knee flexion compared to preoperative values. The postoperative patellar tilt angle was found to be significantly smaller at 30° of knee flexion compared to that preoperatively. Isometric muscle contractions did not considerably influence patellar shift or tilt in either group. The patello-femoral contact area increased after LRR over the full range of motion (ROM), with significant changes at 0° and 90°. Regarding femoro-tibial kinematics, significantly increased femoral internal rotation at 0° was observed in the patient group preoperatively, whereas the magnitude of rotation at 90° of knee flexion was comparable to that of healthy individuals. The pathologically increased femoral internal rotation at 30° without muscular activity could be significantly decreased by LRR. With isometric quadriceps contraction no considerable improvement of femoral internal rotation could be achieved by LRR at 30° of knee flexion. CONCLUSIONS: Patello-femoral and femoro-tibial joint kinematics could be improved, making LRR a viable surgical option in carefully selected patients with isolated LRT. However, pathologically increased femoral internal rotation during early knee flexion remained unaffected by LRR and thus potentially accounts for persistent pain. LEVEL OF EVIDENCE: II.

Different outcomes after proximal femoral replacement in oncologic and failed revision arthroplasty patients - a retrospective cohort study.

BACKGROUND: Proximal femoral replacement (PFR) is a technically demanding procedure commonly performed to restore extensive, oncological or non-oncological bone defects in a severely debilitated patient collective. Depending on different indications, a varying outcome has been reported. The aim of the study was to assess the functional outcomes and complication rates of PFR with the modular Munich-Luebeck (MML) femoral megaprosthesis (ESKA/Orthodynamics, Luebeck, Germany), and to highlight outcome differences in patients treated for failed revision total hip arthroplasty (THA) or malignant bone disease. METHODS: A retrospective review of patients treated with PFR for failed THA or malignant tumor disease between 2000 and 2012 was performed. Patient satisfaction, functional outcome (VAS, SF-12, MSTS, WOMAC, TESS), complications and failure types (Henderson's failure classification) were assessed. A Kaplan-Meier analysis determined implant survival. RESULTS: Fifty-eight patients (age: 69.9 years, BMI: 26.7 kg/m2, mean follow-up: 66 months) were included. The mean SF-12 (physical / mental) was 37.9 / 48.4. MSTS averaged 68% at final follow-up, while mean WOMAC and TESS scored 37.8 and 59.5. TESS and WOMAC scores demonstrated significantly worse outcomes in the revision group (RG) compared to the tumor group (TG). Overall complication rate was 43.1%, and dislocation was the most common complication (27.6%). Implant survival rates were 83% (RG) and 85% (TG; p = n.s.) at 5 years, while 10-year survival was 57% (RG) and 85% (TG, p < 0.05). CONCLUSIONS: PFR is a salvage procedure for restoration of mechanical integrity and limb preservation after extensive bone loss. Complications rates are considerably high. Functional outcomes and 10-year implant survival rate were worse in the RG compared to the TG. Strict indications and disease-specific patient education are essential in preoperative planning and prognosis.

COVID-19-related cancellation of elective orthopaedic surgery caused increased pain and psychosocial distress levels.

PURPOSE: Health care systems in most European countries were temporarily restructured to provide as much capacity as possible for the treatment of coronavirus disease 2019 (COVID-19) patients. Subsequently, all elective surgeries had to be cancelled and postponed for months. The aim of the present study was to assess the pretreatment health status before and after COVID-19-related cancellation and the psychosocial distress caused by the cancellation. METHODS: For this study, a questionnaire was developed collecting sociodemographic data and information on health status before and after the cancellation. To assess psychosocial distress, the validated depression module of the Patient Health Questionnaire (PHQ-9), was implemented. PHQ-9-Scores of 10 and above were considered to indicate moderate or severe depressive symptoms. In total, 119 patients whose elective orthopaedic surgery was postponed due to the COVID-19 pandemic were surveyed once at least 8 weeks after the cancellation. RESULTS: Seventy-seven patients (65%; 34 female, 43 male) completed the questionnaire and were included. The predominant procedures were total knee arthroplasty (TKA), hip arthroscopy and foot and ankle surgery. The mean pain level significantly increased from 5.5 ± 2.2 at the time of the initially scheduled surgery to 6.2 ± 2.5 at the time of the survey (p < 0.0001). The pain level before cancellation of the surgery was significantly higher in female patients (p = 0.029). An increased analgetic consumption was identified in 46% of all patients. A mean PHQ-9 score of 6.1 ± 4.9 was found after cancellation. PHQ-9 scores of 10 or above were found in 14% of patients, and 8% exhibited scores of 15 points or above. Significantly higher PHQ-9 scores were seen in female patients (p = 0.046). No significant differences in PHQ-9 scores were found among age groups, procedures or reasons for cancellation. CONCLUSION: Cancellation of elective orthopaedic surgery resulted in pain levels that were significantly higher than when the surgery was scheduled, leading to increased analgesic use. Additionally, significant psychosocial distress due to the cancellation was identified in some patients, particularly middle-aged women. Despite these results, confidence in the national health care system and in the treating orthopaedic surgeons was not affected. LEVEL OF EVIDENCE: Level III.

Distal femoral replacement with the MML system: a single center experience with an average follow-up of 86 months.

BACKGROUND: The aim of this study was to compare the functional outcomes and complication rates after distal femoral replacement (DFR) performed with the modular Munich-Luebeck (MML) modular prosthesis (ESKA/Orthodynamics, Luebeck, Germany) in patients being treated for malignant disease or failed total knee arthroplasty. METHODS: A retrospective review of patient charts and a functional investigation (involving Musculoskeletal Tumor Society Score [MSTS], American Knee Society Score [AKSS], Oxford Knee Score [OKS], Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC], Toronto Extremity Salvage Score [TESS], the 12-Item Short-Form [SF-12] Health Survey, and a failure classification system developed by Henderson et al.) of DFR cases from 2002 to 2015 were conducted. The indications for DFR were malignant tumor resection in the femur (n = 20, group A) or failure of revision total knee arthroplasty without a history of malignant disease (n = 16, group B). RESULTS: One-hundred and twenty-nine patients were treated during the study period. Of these, 82 were analyzed for complications and implant-survival. Further, 36 patients were available for functional assessment after a mean follow-up of 86 months (range: 24-154). There were 75 complications in total. The overall failure rate for DFR was 64.6% (53/82 patients). The most common failure mechanisms were type III (mechanical failure), followed by type I (soft tissue) and type II (aseptic loosening). The mean MSTS score (out of 30) was 17 for group A and 12 for group B. All the clinical outcome scores revealed an age-dependent deterioration of function. CONCLUSION: DFR is an established procedure to restore distal femoral integrity. However, complication rates are high. Post-procedure functionality depends mainly on the patient's age at initial reconstruction.

Short- to long-term follow-up of total femoral replacement in non-oncologic patients.

BACKGROUND: Compromised bone stock and heavily impaired structural integrity after multiple endoprosthetic revision surgeries can lead to a comparable condition as encountered in musculoskeletal tumor surgery. Total femoral replacement (TFR) can restore femoral integrity and allow patients to resume ambulation. Even though several authors reported their results of TFR, so far many questions are still on debate: Which patients are at risk to experience low functional outcome? Do complications and clinical outcome after TFR depend on the indication for the surgery (e.g. periprosthetic fractures or aseptic loosening) or the age of the patients? The purpose of the present study was to compare complication rates after TFR performed with modular total femur prosthesis MML (Fa. ESKA/Orthodynamics) in patients without malignant disease. METHODS: We conducted a retrospective chart review and functional investigation of patients treated with a TFR for non-oncologic conditions from 1995 to 2015 and a minimum follow-up of 2 years. Complications were recorded according to the Henderson-Classification; outcome was evaluated with established clinical scores. The indication for TFR was periprosthetic fracture (Group A, n = 11) or aseptic loosening (Group B, n = 7) with massive bone defect of the femur deemed unsuitable for conventional arthroplastic or biologic reconstruction. RESULTS: Eighteen patients matched the inclusion criteria and could be investigated clinically after a mean follow-up of 80 months (range: 28-132). Before TFA, all patients had previously undergone multiple operations (range: 1-8). The overall failure rate for any reason was 72% (n = 13/18), leading to a total of 37 surgical revisions with total exchange of TFR in 22% (n = 4/18). Most common failure mechanism was Type I (soft tissue), followed by Type IV (infection) and Type III (mechanical failure). According to Enneking's functional evaluation method (MSTS-Score), the function ranged from 1 to 15 with a mean of 10 ± 4 out of 30. CONCLUSION: TFR is a salvage procedure to restore mechanical integrity and regain functional ability after extensive femoral bone loss. Outcome of the patients in the present study did mainly depend on the age at reconstruction and not on the indication for TFR.

A new model of implant-related osteomyelitis in the metaphysis of rat tibiae.

BACKGROUND: Animal models serve as an important tool to understand peri-implant infection. Most of the models use high bacterial loads (>10(4) colony forming units, CFU) to provide high infection rates. Therefore these animals evolve rather similarly, making comparison between groups and statistical analysis possible. On the other hand, to mimic clinical constellation of surgery-related infections the use of low amounts of bacteria would be more advantageous. METHODS: We developed a metaphyseal rat model of peri-implant bone infection with low amount of bacterial loads (10(2) and 10(3) CFU of Staphylococcus aureus) and investigated osseointegration of the implants coated with hydroxyapatite (HA) and low-dosed HA-silver (HA-Ag). Non-infected implants served as controls. After 6 weeks rats were sacrificed and implants evaluated for osseointegration and infection. RESULTS: Infection of implanted devices was reliably induced, independently whether 10(2) or 10(3) CFU of S. aureus were inoculated and HA or HA-Ag coated implants were used. No systemic infection was present in any of the animals at the time of sacrifice, and no animal developed acute infection requiring premature sacrifice. All CFU counts of the implant and the bone at sacrifice were significantly higher than the inoculated load (p < .05). All sterilely inserted implants showed excellent osseointegration and no infection. CONCLUSIONS: Our present study of a rat tibia model reliably induced osteomyelitis in the metaphysis with low-doses of bacteria. The addition of low-dosed Ag to the implant coating was not able to reduce the infection rates. The results demonstrate that it is possible to develop a model of implant-related osteomyelitis in rats with low amounts of bacteria to better mimic clinical constellations. No other promoters of infection besides insertion of the screw implant were used in this model.