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

Purpose:  To investigate if radiology researchers are increasingly promoting their scientific findings by more frequently using positive words in their publications.

Materials and methods:  This study included all articles that were published in 14 general radiology journals between 2003 and 2022. The title and abstract of each article were assessed for the presence of positive, negative, neutral, and random words, according to predefined sets of words for each category. Usage of positive, negative, neutral, and random words was calculated for each year and corrected for the total number of articles in each year. Temporal trends between 2002 and 2023 and the relationship between positive word usage and journal impact factor (IF) were assessed.

Results:  Positive word usage (Mann-Kendall tau of 0.895, P< 0.001) and neutral word usage (Mann-Kendall tau of 0.463, P = 0.005) showed significant upward temporal trends. Negative word usage and random word usage did not show any significant temporal trends. Five positive words showed significantly increased usage over time and were present in more than 1 % of titles/abstracts in at least one year: "excellent" (Mann-Kendall tau of 0.800, P< 0.001), "favorable" (Mann-Kendall tau of 0.547, P< 0.001), "promising" (Mann-Kendall tau of 0.607, P< 0.001), "robust" (Mann-Kendall tau of 0.737, P< 0.001), and "unique" (Mann-Kendall tau of 0.747, P< 0.001). There was no significant association between positive word usage and journal IF.

Conclusion:  Radiology researchers appear to increasingly promote their scientific findings by more frequently using positive words in their publications over the past two decades.

Key points:   · Positive word usage in titles/abstracts has strongly increased between 2003-2022. · "Excellent", "favorable", "promising", "robust", and "unique" were most often used. · This trend occurred in all general radiology journals, regardless of impact factor.

Citation format: · Kwee T, Kwee R. Promotional language in radiology publications: increasing use of "excellent", "favorable", "promising", "robust", and "unique". Fortschr Röntgenstr 2024; 196: 945 - 955.

150 years ago, on March 1, 1874, Prof. Dr. Alban Köhler, pioneer in X-ray diagnostics and Co-founder of the German Radiological Society, was born. His memoirs "Röntgenarztes Erdenwallen", an extraordinarily personal, interesting and humorous autobiography of a contemporary witness from the early days of the X-ray era, will be used to commemorate him. This work also focuses on his outstanding X-ray books on skeletal diseases and standard variants.

· Breast MRI is an essential part of breast imaging. · The recommendations for performing breast MRI have been updated. · A table provides a compact and quick overview. More detailed comments supplement the table.. · The "classic" breast MRI can be performed based on the recommendations. Tips for special clinical questions, such as implant rupture, mammary duct pathology or local lymph node status, are included.. CITATION FORMAT: · Wenkel E, Wunderlich P, Fallenberg E et al. Aktualisierung der Empfehlungen der AG Mammadiagnostik der Deutschen Röntgengesellschaft zur Durchführung der Mamma-MRT. Fortschr Röntgenstr 2024; 196: 939 - 944.

Purpose:  The aim of this study was to develop an algorithm to automatically extract annotations from German thoracic radiology reports to train deep learning-based chest X-ray classification models.

Materials and methods:  An automatic label extraction model for German thoracic radiology reports was designed based on the CheXpert architecture. The algorithm can extract labels for twelve common chest pathologies, the presence of support devices, and "no finding". For iterative improvements and to generate a ground truth, a web-based multi-reader annotation interface was created. With the proposed annotation interface, a radiologist annotated 1086 retrospectively collected radiology reports from 2020-2021 (data set 1). The effect of automatically extracted labels on chest radiograph classification performance was evaluated on an additional, in-house pneumothorax data set (data set 2), containing 6434 chest radiographs with corresponding reports, by comparing a DenseNet-121 model trained on extracted labels from the associated reports, image-based pneumothorax labels, and publicly available data, respectively.

Results:  Comparing automated to manual labeling on data set 1: "mention extraction" class-wise F1 scores ranged from 0.8 to 0.995, the "negation detection" F1 scores from 0.624 to 0.981, and F1 scores for "uncertainty detection" from 0.353 to 0.725. Extracted pneumothorax labels on data set 2 had a sensitivity of 0.997 [95 % CI: 0.994, 0.999] and specificity of 0.991 [95 % CI: 0.988, 0.994]. The model trained on publicly available data achieved an area under the receiver operating curve (AUC) for pneumothorax classification of 0.728 [95 % CI: 0.694, 0.760], while the models trained on automatically extracted labels and on manual annotations achieved values of 0.858 [95 % CI: 0.832, 0.882] and 0.934 [95 % CI: 0.918, 0.949], respectively.

Conclusion:  Automatic label extraction from German thoracic radiology reports is a promising substitute for manual labeling. By reducing the time required for data annotation, larger training data sets can be created, resulting in improved overall modeling performance. Our results demonstrated that a pneumothorax classifier trained on automatically extracted labels strongly outperformed the model trained on publicly available data, without the need for additional annotation time and performed competitively compared to manually labeled data.

Key points:   · An algorithm for automatic German thoracic radiology report annotation was developed.. · Automatic label extraction is a promising substitute for manual labeling.. · The classifier trained on extracted labels outperformed the model trained on publicly available data..

Zitierweise: · Wollek A, Hyska S, Sedlmeyr T et al. German CheXpert Chest X-ray Radiology Report Labeler. Fortschr Röntgenstr 2024; 196: 956 - 965.