Introduction: Suspicious lesions on breast MRI are often initially evaluated using targeted ultrasound. However, workup varies. Data on the rate of correlate detection by morphology [mass, non-mass enhancement (NME), or focus] would be useful for developing practice guidelines.
Materials And Methods: Breast MRI examinations from 1 January 2008 to 31 December 2010 were reviewed. BI-RADS 4 or 5 lesions on MRI evaluated with targeted ultrasound where definitive diagnosis was obtained were included. Statistical analysis was performed on aggregate data and at the lesion level.
Results: A total of 204 lesions were included in the study. A statistically significant difference in ultrasound correlate identification by morphology was found; a correlate was found in 49.3 % of masses, 15 % of NME, and 42.3 % of foci (p = 0.0006). Additional analysis within each morphology demonstrated significantly greater rate of malignancy in masses with an ultrasound correlate than masses without a correlate (p = 0.0062), while the rate of malignancy in NME and foci did not differ with ultrasound correlation.
Conclusions: Morphology of a suspicious lesion on breast MRI affects the probability of identifying an ultrasound correlate. As sonographic correlates are found in nearly half of masses and foci, targeted ultrasound should be the initial step in their workup.
Key Points: • Lesion morphology on breast MRI affects the probability of ultrasound correlate identification. • An ultrasound correlate is significantly more likely for masses and foci. • Mass or focus should undergo targeted ultrasound before MRI-guided biopsy.
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http://dx.doi.org/10.1007/s00330-014-3517-y | DOI Listing |
BMC Med Imaging
January 2025
Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Problem: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques.
View Article and Find Full Text PDFJ Magn Reson Imaging
January 2025
Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA.
Med J Malaysia
January 2025
Department of Ophthalmology, Saveetha Institute of Medical and Technical Sciences (Deemed to be University): SIMATS Deemed University, Chennai, Tamilnadu, India.
Tamoxifen, an oral medication that blocks estrogen activity, is frequently prescribed for the treatment of advanced breast cancer and as an additional therapy following surgical removal of early stage disease. A 45-year-old female with a history of breast carcinoma treated with tamoxifen presented with sudden onset bilateral visual impairment for 4 days. On ocular examination, the patient exhibited optic disc edema with hyperemia and bilateral anterior pathway defects in visual evoked potentials.
View Article and Find Full Text PDFBMC Med Imaging
January 2025
Department of Radiology, Suzhou Ninth People's Hospital, Ludang Street 2666#, Suzhou, Jiangsu, 215200, PR China.
Objective: This study was to develop a multi-parametric MRI radiomics model to predict preoperative Ki-67 status.
Materials And Methods: A total of 120 patients with pathologically confirmed breast cancer were retrospectively enrolled and randomly divided into a training set (n = 84) and a validation set (n = 36). Radiomic features were derived from both the intratumoral and peritumoral regions, extending 5 mm from the tumor boundary, using magnetic resonance imaging (MRI).
Radiol Artif Intell
January 2025
From the Department of Radiology, Weill Cornell Medical College, Cornell University, MRI Research Institute, 407 E 61st St, New York, NY 10065 (E.S., S.G.K.); Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY (E.S., P.M.J., R.T., Y.W.L., F.K., L.M., S.G.K., L.H.); and Department Artificial Intelligence in Biomedical Engineering, University Erlangen-Nuremberg, Erlangen, Germany (Z.T., F.K.).
The fastMRI breast dataset is the first large-scale dataset of radial k-space and DICOM data for breast dynamic contrast-enhanced MRI with case-level labels. Its public availability aims to advance fast and quantitative machine learning research. ©RSNA, 2025.
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