Objective: The objective of our study was to assess the clinical utility of MR-directed ("second-look") ultrasound examination to search for breast lesions detected initially on MRI.
Materials And Methods: A retrospective review was performed of the records of 158 consecutive patients (202 lesions) with breast abnormalities initially detected on MRI between July 2003 and May 2006. All lesions were detected as enhancing findings on a dynamic contrast MR study and were subsequently evaluated with ultrasound. Ultrasound was performed using MR images as a guide to lesion location, size, and morphology. Pathology findings were confirmed by subsequent percutaneous biopsy or lesion excision. Imaging follow-up was used for probably benign lesions, which were not biopsied.
Results: Of the 202 MRI-detected lesions, ultrasound correlation was made in 115 (57%) including 33 malignant lesions and 82 benign lesions. The remaining 87 lesions were not sonographically correlated and included 11 malignant lesions and 76 nonmalignant lesions. Mass lesions identified on MRI were more likely to have a sonographic correlate than nonmasslike lesions (65% vs 12%, respectively); malignant mass lesions were more likely to show an ultrasound correlation (85%). The malignant lesions with successful sonographic correlation tended to present with subtle sonographic findings.
Conclusion: MR-directed ultrasound of MRI-detected lesions was useful for decision making as part of the diagnostic workup. Malignant lesions were likely to have an ultrasound correlate, especially when they presented as masses on MRI. However, the sonographic findings of these lesions were often subtle, and careful scanning technique was needed for successful MRI-ultrasound correlation.
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http://dx.doi.org/10.2214/AJR.09.2707 | DOI Listing |
Sci Rep
December 2024
Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Republic of Korea.
This study aimed to investigate alterations in a multilayer network combining structural and functional layers in patients with end-stage kidney disease (ESKD) compared with healthy controls. In all, 38 ESKD patients and 43 healthy participants were prospectively enrolled. They exhibited normal brain magnetic resonance imaging (MRI) without any structural lesions.
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December 2024
Department of Neuroscience and Padova Neuroscience Center, Università di Padova, Padova, Italy.
Can focal brain lesions, such as those caused by stroke, disrupt critical brain dynamics? What biological mechanisms drive its recovery? In a recent study, we showed that focal lesions generate a sub-critical state that recovers over time in parallel with behavior (Rocha et al., Nat. Commun.
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December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
Medical Image Analysis, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma.
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December 2024
Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs.
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