Rofo-fortschritte Auf Dem Gebiet Der Rontgenstrahlen Und Der Bildgebenden Verfahren最新文献

The diagnosis and treatment of specific back pain is important in radiology. Due to the high number of patients suffering from back pain, it is important to provide excellent diagnostic and therapeutic support.Based on a recent literature search and considering the relevant guidelines as well as expert opinions, the aspects of specific back pain important for radiologists in terms of pathogenesis, diagnosis, and treatment are presented.Clinical examination in combination with the medical history provides a valid suspected diagnosis. This should subsequently be verified radiologically. MRI is the most effective cross-sectional diagnostic method for investigating specific back pain. A conventional X-ray on two planes in a standing position can be a useful addition if postural causes are suspected. If the clinical symptoms match the morphological findings, radiological treatment can be carried out for nerve root involvement as well as for inflammatory changes of the facet joints. The improvement in symptoms after radiological therapy is considered good overall; at least a short-term improvement in symptoms can generally be achieved, but no reliable data is available regarding the long-term outcome. Using preparations containing triamcinolone, low dosages should be selected in accordance with the guidelines. Embedding in a multimodal pain therapy treatment concept should be considered.Radiology provides essential diagnostic findings regarding specific back pain. Interventional pain therapy is an effective and safe method of treating proven specific back pain. · First examine clinically, then confirm the suspected diagnosis radiologically.. · MRI is usually the method of choice.. · Interventional pain therapy should show success after a maximum of 2 interventions.. · The anti-inflammatory drug dose should be kept as low as possible.. · The individual course determines the number of interventions.. · Stueckle CA, Haage P. Specific back pain - effective diagnosis and treatment from the radiologist's point of view. Fortschr Röntgenstr 2024; DOI 10.1055/a-2371-1752.

Investigation of motivation and identification of success factors in radiology research in Germany.Using a German online survey (54 questions, period: 3.5 months), demographic aspects, intrinsic and extrinsic success characteristics, as well as personal and organizational success factors were surveyed based on a career success model. The survey results were reported descriptively. The correlations between success factors and success characteristics were examined using linear, binary-logistic, and multinomial regression models.176 people (164 academically active, 10 not academically active) answered the survey. Most participants (80%, 139/174) worked at a university hospital. 32% had privatdozent or professor as their highest academic title (56/173). The researchers' main motivation was intrinsic interest in research (55%, 89/163), followed by a desire to increase their own career opportunities (25%, 41/163). The following were identified as factors for intrinsic success: i) support from department management (estimate=β=0.26, p<0.001), ii) good work-life balance (β=0.37, p<0.001), and iii) the willingness to pursue science even after reaching the career goal (β=0.16, p<0.016). Relevant factors for extrinsic scientific success were mentoring, protected research time, and activities in professional societies.Researchers in German radiology are mainly intrinsically motivated. Factors known from the literature that determine intrinsic and extrinsic scientific success were confirmed in this study. Knowledge of these factors allows targeted systematic support and could thus increase scientific success in German radiology. · Main motivation for German radiology research is intrinsic interest, followed by career opportunities.. · Factors for intrinsic scientific success are good work-life balance and support by department management.. · Factors for extrinsic scientific success are mentoring, activities in professional societies, and protected research time.. · Wegner F, Heinrichs H, Stahlmann K et al. Motivation and success factors in radiological research in Germany - results of a survey by the Methodology and Research Working Group of the German Radiological Society. Fortschr Röntgenstr 2025; 197: 424-435.

To explore the value of CT-based radiomics machine learning models for differentiating enchondroma from atypical cartilaginous tumor (ACT) in long bones and methods to improve model performance.59 enchondromas and 53 ACTs in long bones confirmed by pathology were collected retrospectively. The features were extracted from preoperative CT images of these patients, and least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and dimensionality reduction. The selected features were used to construct classification models by thirteen machine learning algorithms. The data set was randomly divided into a training set and a test set at a proportion of 7:3 by ten-fold cross-validation to evaluate the performance of these models.A total of 1199 features were extracted, 9 features were selected, and 13 radiomics machine learning models were constructed. The area under the curve (AUC) of 11 models was more than 0.8, and that of 3 models was more than 0.9. The Extremely Randomized Trees model achieved the best performance (AUC = 0.9375 ± 0.0884), followed by the Adaptive Boosting model (AUC = 0.9188 ± 0.1010) and the Linear Discriminant Analysis model (AUC = 0.9062 ± 0.1459).CT-based radiomics machine learning models had great ability to distinguish enchondroma and ACT in long bones. By using filters to deeply mine high-order features in the original image and selecting appropriate machine learning algorithms, the performance of the model can be improved. · CT-based radiomics machine learning models can distinguish enchondroma and ACT in long bones.. · Using filters and selecting advanced machine learning algorithms can improve model performance.. · Clinical features have limited utility in distinguishing enchondroma and ACT in long bones.. · Hong R, Li Q, Ma J et al. Computed tomography-based radiomics machine learning models for differentiating enchondroma and atypical cartilaginous tumor in long bones. Fortschr Röntgenstr 2025; 197: 416-423.