Potential of MR Mammography to Predict Tumor Grading of Invasive Breast Cancer
Matthias Dietzel(Friedrich-Alexander-Universität Erlangen-Nürnberg), W. A. Kaiser(Friedrich Schiller University Jena), Mieczysław Gajda(Friedrich Schiller University Jena), Tobias Gröschel(Friedrich Schiller University Jena), Pascal Baltzer(Medical University of Vienna), R Zoubi(Friedrich Schiller University Jena), H Burmeister(Friedrich Schiller University Jena), Tibor Vág(Friedrich Schiller University Jena), Ingo B. Runnebaum
RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren
March 25, 2011
Cited by 19
Related Papers
METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII
|Insights into Imaging|2024|286
Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients
|Investigative Radiology|2018|278
Second International Consensus Conference on lesions of uncertain malignant potential in the breast (B3 lesions)
|Breast Cancer Research and Treatment|2018|251
False-Positive Findings at Contrast-Enhanced Breast MRI: A BI-RADS Descriptor Study
|American Journal of Roentgenology|2010|197
Diffusion-Weighted Imaging With Apparent Diffusion Coefficient Mapping for Breast Cancer Detection as a Stand-Alone Parameter
|Investigative Radiology|2018|161