Background:Distal radius fracture is common in all ages. Mobility and wrist function is important. The choice of treatment should aim for optimal function with minimal complications.Objectives:In this study we compared two surgical approaches, open reduction and internal fixation (ORIF) and closed reduction with external fixation (CR + EF), for treatment of intra-articular distal radius fractures.Patients and Methods:Ninety-four patients with distal radius fracture (type 3, 4 and 5 Fernandez classification) were treated with two surgical methods (ORIF and CR + EF); 55 were treated with CR + EF and 39 were treated with ORIF by different surgeons. All patients were assessed at the end of the first, third and sixth week; and then after the third, sixth and 12th month. At the end of the follow-up, all patients completed the Michigan hand outcome questionnaire (MHOQ). We compared radiological parameters of distal radius, range of motion (ROM) of the wrist, duration of rehabilitation, complication and patient satisfaction of the methods.Results:In our study, radiological findings for the ORIF group were radial inclination (RI): 19.35, radial length (RL): 10.35, radial tilt (RT): 8.92, and ulnar variance (UV): 1.64, while for the CR + EF group these were RI: 15.13, RL: 8, RT: 4.78, and UV: 0.27. The ROM for ORIF were flexion/extension (F/E): 137, Radial/Ulnar deviation (R/U): 52, and Supination/Pronation (S/P): 141, while for the CR + EF group these were F/E: 117, R/U: 40 and S/P: 116. Michigan hand outcome score for ORIF was 75% and for Ext. fix was 60%. The rate of complication with the ORIF method was 58% and in Ext. fix this was 69%. The patients in CR + EF had more than the ORIF course of physiotherapy and rehabilitation.Conclusions:In comparison of ORIF and CR + EF, all results including functional score, clinical and radiologic criteria were in favor of the ORIF method while there were less complications with this method. We believe that ORIF is a better method for treatment of these types of fractures.
Background Ewing's sarcoma (ES) of the hip and trochanteric region is a rare malignancy. The tumor has a poor prognosis due to the problems in early diagnosis and medical intervention. Case presentation This paper reports a rare case of hip ES presented in a 34y/o female. The clinical, radiological, and histopathological features were all in favor of ES. Following treatment by neoadjuvant/adjuvant chemotherapy, and irradiation the patient is now with complete resolution of the tumor. Conclusion The patient remained free of disease through 4 years of follow-up until now after diagnosis.
Introduction: Low-energy proximal femur fractures in elderly patients result from factors, like osteoporosis and falls. These fractures impose high rates of economic and social costs. In this study, we aimed to build predictive models by applying machine learning (ML) methods on radiomics features to predict low-energy proximal femur fractures. background: Low-energy proximal femur fractures in the elderly patients are resulted from factors like osteoporosis and falls. These fractures impose high rates of economic and social costs. Methods: Computed tomography scans of 40 patients (mean ± standard deviation of age = 71 ± 6) with low-energy proximal femur fractures (before a fracture occurs) and 40 individuals (mean ± standard deviation of age = 73 ± 7) as a control group were included. The regions of interest, including neck, trochanteric, and intertrochanteric, were drawn manually. The combinations of 25 classification methods and 8 feature selection methods were applied to radiomics features extracted from ROIs. Accuracy and the area under the receiver operator characteristic curve (AUC) were used to assess ML models' performance. objective: Prediction of low-energy proximal femur fractures using radiomics features and machine learning models Results: AUC and accuracy values ranged from 0.408 to 1 and 0.697 to 1, respectively. Three classification methods, including multilayer perceptron (MLP), sequential minimal optimization (SMO), and stochastic gradient descent (SGD), in combination with the feature selection method, SVM attribute evaluation (SAE), exhibited the highest performance in the neck (AUC= 0.999, 0.971 and 0.971, respectively; accuracy = 0.988, 0.988, and 0.988, respectively) and the trochanteric (AUC = 1, 1 and 1, respectively; accuracy = 1, 1 and 1, respectively) regions. The same methods demonstrated the highest performance for the combination of the 3 ROIs’ features (AUC = 1, 1 and 1, respectively; accuracy =1, 1 and 1, respectively). In the intertrochanteric region, the combination methods, MLP+SAE, SMO+SAE, and SGD+SAE, as well as the combination of the SAE method and logistic regression (LR) classification method exhibited the highest performance (AUC= 1, 1, 1 and 1, respectively; accuracy= 1, 1, 1 and 1, respectively). method: Computed tomography scans of 40 patients (mean ± standard deviation of age = 71 ± 6) with low-energy proximal femur fractures and 40 individuals (mean ± standard deviation of age = 73 ± 7) as a control group were included. Regions of interest (ROIs) including neck, trochanteric and intertrochanteric were drawn manually. The combinations of 25 classification methods and 8 feature selection methods were applied on radiomics features extracted from ROIs. Accuracy and the area under the receiver operator characteristic curve (AUC) were used to assess ML models performance. Conclusion: Applying machine learning methods to radiomics features is a powerful tool to predict low-energy proximal femur fractures. The results of this study can be verified by conducting more research on bigger datasets. other: Completing the results of this study through conducting research on bigger datasets can be the scope of future works.
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