Publications by authors named "T W Vomweg"
Article Synopsis
- AI in mammography shows promise through a study comparing AI-supported double reading to standard double reading among women aged 50-69 across 12 German sites.
- AI assistance led to a significantly higher breast cancer detection rate (6.7 per 1,000) than the control group's detection rate (5.7 per 1,000), with a 17.6% increase.
- The AI group had a slightly lower recall rate (37.4 per 1,000) compared to the control (38.3 per 1,000) and showed better positive predictive values for both recall (17.9% vs. 14.9%) and biopsy (64.5% vs. 59.2%), indicating improved
Background: Chemical shift-encoding based water-fat MRI is an emerging method to noninvasively assess proton density fat fraction (PDFF), a promising quantitative imaging biomarker for estimating tissue fat concentration. However, in vivo validation of PDFF is still lacking for bone marrow applications.
Purpose: To determine the accuracy and precision of MRI-determined vertebral bone marrow PDFF among different readers and across different field strengths and imager manufacturers.
Purpose: To evaluate the association of dynamic enhancement parameters of benign and malignant breast lesions at magnetic resonance (MR) imaging with microvessel distribution and histologic prognostic tumor characteristics.
Materials And Methods: Regional review board approval and informed consent were obtained. Surgical resection specimens of breast lesions (32 benign, 86 malignant) in 118 patients (age range, 28-86 years; mean, 58 years) who had undergone dynamic T1-weighted MR imaging of both breasts were included in the study.
Purpose: To assess the effect of a second diagnostic reading of breast imaging at a university department of radiology.
Material And Methods: The diagnostic reports of first readers from different private radiology practices and the reports of second readers from the university department of radiology were compared with the histological results (n = 214) and outcome of follow-ups for 4 years (n = 74) in 236 patients (mean age 55 years). BI-RADS categories were used for this purpose.
Purpose: Investigation and statistical evaluation of "Self-Organizing Maps," a special type of neural networks in the field of artificial intelligence, classifying contrast enhancing lesions in dynamic MR-mammography.
Material And Methods: 176 investigations with proven histology after core biopsy or operation were randomly divided into two groups. Several Self-Organizing Maps were trained by investigations of the first group to detect and classify contrast enhancing lesions in dynamic MR-mammography.