To determine the prevalence of malignancy of additional ultrasound BI-RADS category 3 lesions in women with newly diagnosed breast cancer. Between July 2014 and June 2022, women with newly diagnosed breast cancer and additional ultrasound BI-RADS 3 lesions were retrospectively included. Malignancy was confirmed by pathology, and malignancy rates were assessed at both the patient and lesion levels. Independent risk factors for malignant BI-RADS 3 lesions were identified using multivariable logistic regression. A total of 1458 women (median age, 45 years; IQR: 40–51) with 1995 ultrasound BI-RADS 3 lesions were included. At the patient level, malignancy was identified in 3.5
To evaluate whether large language model assistance improves radiologists’ agreement with a text-based reference standard in inferring the modified American Heart Association carotid plaque classification from free-text MRI reports, and whether a lightweight model can perform well under criteria-guided prompting. This retrospective study included 433 patients from three institutions. In a report-based reader study, four radiologists classified MRI reports with and without large language model assistance. In standalone evaluation, a rule-based baseline and 10 models were compared under naive and criteria-guided prompting. The reference standard was expert consensus based on the report text. Among 433 patients (mean age, 65.9 years ± 13.4; 248 males), AI assistance improved six-class agreement with the text-based reference standard. At the vessel level, junior radiologists improved from 61.75
To evaluate patient acceptance, tolerability and experience with contrast-enhanced mammography (CEM) in women with a personal history of breast cancer undergoing routine post-surgical surveillance. This prospective cohort study, conducted within the COMBO Trial, included consecutive women with a personal history of breast cancer undergoing CEM instead of digital mammography (DM) for routine surveillance from January 2023 to February 2025. Immediately after the examination, participants completed an anonymous 16-question questionnaire assessing discomfort, pain, exam duration, pre-examination reassurance, information received, anxiety, contrast injection, satisfaction and comparative experience with prior DM. Responses used Likert-type scales. Descriptive and non-parametric analyses were performed (p < 0.05 statistically significant). Of 969 eligible women, 936 (mean age 58.2 years, SD ± 10.2 years) completed the questionnaire. CEM was perceived as comparable to prior DM in terms of discomfort (631/936; 67.4
To develop a modified non-contrast O-RADS MRI scoring system and validate its diagnostic performance. The modified non-contrast O-RADS MRI scoring system was developed by incorporating the significant signs from the standard O-RADS MRI system. This study retrospectively enrolled the 176 adnexal masses in cohort A. MRI imaging features were compared between benign and malignant tumors within this cohort. The diagnostic performance of the two scoring models was assessed by estimating the area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). In addition, 352 adnexal lesions from cohorts B and C were included to further evaluate the diagnostic ability of the modified scoring system. In cohort A, the papillary projections exhibiting the sea anemone sign were highly indicative of borderline lesions (p = 0.001). Diffusion restriction in solid components significantly distinguished benign from malignant masses (p = 0.001). The apparent diffusion coefficient (ADC) value of cystic components yielded a cutoff of 1.74 (p < 0.05). The DeLong test revealed no statistically significant difference in AUCs between the two systems in cohort A (p > 0.05). The NRI and IDI for the modified non-contrast O-RADS MRI scoring system were −4.13
To assess the environmental impact of a radiology department’s operations (primary energy consumption and greenhouse gas emissions (GHG); to identify the main levers for reduction. Data collection included measured and calculated energy consumption of imaging machines (2 MRI, 2 CT, 1 interventional radiology (IR) suite, 2 US, 3 radiography machines), workstations, lighting, ventilation; cooling and heating for imaging systems and rooms; materials/pharmaceuticals; external services (e.g. laundry and information technology (IT)). The electrical energy consumption of building systems, lighting, and IT equipment was primarily determined through measurements; other energy consumption figures were estimated using technical data and meteorological data. For the calculation of GHG emissions and primary energy consumption, a specialised tool using specific conversion factors from established environmental databases was developed. Our hospital switched from coal-based electricity to renewable electrical energy (hydropower), leading to a reduction in primary energy consumption (−64
