Non-invasive Assessment of Axillary Lymph Node Metastasis Risk in Early Invasive Breast Cancer Adopting Automated Breast Volume Scanning-Based Radiomics Nomogram: A Multicenter Study.

OBJECTIVE: The aim of the work described here was to develop a non-invasive tool based on the radiomics and ultrasound features of automated breast volume scanning (ABVS), clinicopathological factors and serological indicators to evaluate axillary lymph node metastasis (ALNM) in patients with early invasive breast cancer (EIBC). METHODS: We retrospectively analyzed 179 ABVS images of patients with EIBC at a single center from January 2016 to April 2022 and divided the patients into training and validation sets (ratio 8:2). Additionally, 97 ABVS images of patients with EIBC from a second center were enrolled as the test set. The radiomics signature was established with the least absolute shrinkage and selection operator. Significant ALNM predictors were screened using univariate logistic regression analysis and further combined to construct a nomogram using the multivariate logistic regression model. The receiver operating characteristic curve assessed the nomogram's predictive performance. DISCUSSION: The constructed radiomics nomogram model, including ABVS radiomics signature, ultrasound assessment of axillary lymph node (ALN) status, convergence sign and erythrocyte distribution width (standard deviation), achieved moderate predictive performance for risk probability evaluation of ALNs in patients with EIBC. Compared with ultrasound, the nomogram model was able to provide a risk probability evaluation tool not only for the ALNs with positive ultrasound features but also for micrometastatic ALNs (generally without positive ultrasound features), which benefited from the radiomics analysis of multi-sourced data of patients with EIBC. CONCLUSION: This ABVS-based radiomics nomogram model is a pre-operative, non-invasive and visualized tool that can help clinicians choose rational diagnostic and therapeutic protocols for ALNM.

Willingness of nurses to report medication administration errors in southern Taiwan: a cross-sectional survey.

BACKGROUND: Underreporting of medication administering errors (MAEs) is a threat to the quality of nursing care. The reasons for MAEs are complex and vary by health professional and institution. AIMS: The purpose of this study was to explore the prevalence of MAEs and the willingness of nurses to report them. METHODS: A cross-sectional study was conducted involving a survey of 14 medical surgical hospitals in southern Taiwan. Nurses voluntarily participated in this study. A structured questionnaire was completed by 605 participants. Data were collected from February 1, 2005 to March 15, 2005 using the following instruments: MAEs Unwillingness to Report Scale, Medication Errors Etiology Questionnaire, and Personal Features Questionnaire. One additional question was used to identify the willingness of nurses to report medication errors: "When medication errors occur, should they be reported to the department?" This question helped to identify the willingness or lack thereof, to report incident errors. RESULTS: The results indicated that 66.9% of the nurses reported experiencing MAEs and 87.7% of the nurses had a willingness to report the MAEs if there were no consequences for reporting. The nurses' willingness to report MAEs differed by job position, nursing grade, type of hospital, and hospital funding. The final logistic regression model demonstrated hospital funding to be the only statistically significant factor. The odds of a willingness to report MAEs increased 2.66-fold in private hospitals (p = 0.032, CI = 1.09 to 6.49), and 3.28 in nonprofit hospitals (p = 0.00, CI = 1.73 to 6.21) when compared to public hospitals. CONCLUSIONS: This study demonstrates that reporting of MAEs should be anonymous and without negative consequences in order to monitor and guide improvements in hospital medication systems.