MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study

Objectives: To develop and validate an MRI radiomics-based decision support tool for the automated grading of cervical disc degeneration.Methods: The retrospective study included 2,610 cervical disc samples of 435 patients from two hospitals. The cervical magnetic resonance imaging (MRI) analysis of patients confirmed cervical disc degeneration grades using the Pfirrmann grading system. A training set (1,830 samples of 305 patients) and an independent test set (780 samples of 130 patients) were divided for the construction and validation of the machine learning model, respectively. We provided a fine-tuned MedSAM model for automated cervical disc segmentation. Then, we extracted 924 radiomic features from each segmented disc in T1 and T2 MRI modalities. All features were processed and selected using minimum redundancy maximum relevance (mRMR) and multiple machine learning algorithms. Meanwhile, the radiomics models of various machine learning algorithms and MRI images were constructed and compared. Finally, the combined radiomics model was constructed in the training set and validated in the test set. Radiomic feature mapping was provided for auxiliary diagnosis.Results: Of the 2,610 cervical disc samples, 794 (30.4%) were classified as low grade and 1,816 (69.6%) were classified as high grade. The fine-tuned MedSAM model achieved good segmentation performance, with the mean Dice coefficient of 0.93. Higher-order texture features contributed to the dominant force in the diagnostic task (80%). Among various machine learning models, random forest performed better than the other algorithms (p < 0.01), and the T2 MRI radiomics model showed better results than T1 MRI in the diagnostic performance (p < 0.05). The final combined radiomics model had an area under the receiver operating characteristic curve (AUC) of 0.95, an accuracy of 89.51%, a precision of 87.07%, a recall of 98.83%, and an F1 score of 0.93 in the test set, which were all better than those of other models (p < 0.05).Conclusion: The radiomics-based decision support tool using T1 and T2 MRI modalities can be used for cervical disc degeneration grading, facilitating individualized management

Evaluating the relationship between pain empathy, knowledge and attitudes among nurses in North China: a cross-sectional study

Abstract Background Effective pain management is closely related to nurses’ knowledge, attitudes and empathy regarding pain. Nursing educators and managers should understand the relationship between nurses’ pain management knowledge, attitudes and empathy level, and take targeted measures accordingly. Currently, there is limited study exploring the relationship between pain empathy and pain knowledge and attitudes among nurses in North China. Objectives The purpose of this study was to investigate the level of nurses’ pain management knowledge and attitudes and pain empathy, to analyze the factors influencing pain empathy, and to explore the relationship between these two variables. Design This study was a quantitative, descriptive-correlation design. Setting and participants The study population was registered nurses in North China, the sample included 177 registered nurses in North China. Methods Data were collected with the “General data questionnaire”, “Knowledge and attitudes survey regarding pain” (KASRP) and the “Empathy for pain scale” (EPS) via Wechat mini program “Questionnaire Star”. Results The 177 registered nurses completed the survey. The averege correct rate for KASRP was (51.94 ± 9.44)%, and none of the respondents achieved a percentage score of >80%. The mean score for pain empathy was (2.78 ± 0.78), the empathy reactions dimension was (2.99 ± 0.77), and the body and mind discomfort dimension was (2.71 ± 0.80). The results of multiple stepwise linear regression showed that whether they had received empathy training, whether they had greater trauma or severe pain and whether they had negative emotions were independent influencing factors for EPS scores. Pearson correlation analysis showed that KASRP scores were positively correlated with EPS scores (r = 0.242, P < 0.05). Conclusions The pain knowledge and attitudes of nurses in North China are far from optimal. Nurses have a relatively low accuracy rate in areas such as medication knowledge, assessment of patient pain based on case studies, and handling PRN prescriptions. Nursing educators and administrators need to design some pain management courses in a targeted manner. Nurses’ empathy for pain was at a moderate level. Pain empathy was positively correlated with pain knowledge and attitudes, suggesting that empathy for pain can be developed postnatally