To investigate the feasibility of noncontrast and contrast-enhanced cone beam breast Computed Tomography (CT) in demonstrating malignant breast lesions in the diagnostic setting. This Institutional Review Board approved, Health Information Portability and Accountability Act compliant, prospective study enrolled BI-RADS four and five patients from 2008 to 2010. Eighty-seven subjects had noncontrast breast CT, 42 had contrast-enhanced breast CT (CE-breast CT) with 70 pathologically confirmed cancer diagnoses. All 70 comprise the study cohort for noncontrast breast CT, and 23 who had CE-breast CT comprise the cohort for CE-breast CT. All had diagnostic work-up. Patient age, breast density, lesion size and characteristics, biopsy method, and core pathology were recorded. A Fisher's exact test was used to detect a difference in detectability. For agreement in size measurement between the imaging modalities, a paired t-test was employed. Reported p-values were based on 2-sided tests. Two one-sided tests were calculated to determine equivalence within ±0.3 cm at a 90% significance level. Noncontrast breast CT identified 67 of 70 malignant lesions, detected by diagnostic work-up. CE-breast CT identified 23 of 23 index malignant lesions and in addition, found three malignant lesions in three cases not previously detected. Noncontrast breast CT demonstrated the index lesion in 67 of 70 cases and CE-breast CT demonstrated the index lesion in all 23 cases. An additional three new malignant lesions not seen with conventional diagnostic work-up were detected. In this preliminary study, breast CT with or without contrast was shown to be accurate at identifying malignant breast lesions in the diagnostic setting.
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http://dx.doi.org/10.1111/tbj.12285 | DOI Listing |
PLoS One
January 2025
Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.
This paper presents a novel approach for generating virtual non-contrast planning computed tomography (VNC-pCT) images from contrast-enhanced planning CT (CE-pCT) scans using a deep learning model. Unlike previous studies, which often lacked sufficient data pairs of contrast-enhanced and non-contrast CT images, we trained our model on dual-energy CT (DECT) images, using virtual non-contrast CT (VNC CT) images as outputs instead of true non-contrast CT images. We used a deterministic method to convert CE-pCT images into pseudo DECT images for model application.
View Article and Find Full Text PDFJ Bone Oncol
February 2025
Center of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Unlabelled: Bone metastasis from breast cancer significantly elevates patient morbidity and mortality, making early detection crucial for improving outcomes. This study utilizes radiomics to analyze changes in the thoracic vertebral bone marrow microenvironment from chest computerized tomography (CT) images prior to bone metastasis in breast cancer, and constructs a model to predict metastasis.
Methods: This study retrospectively gathered data from breast cancer patients who were diagnosed and continuously monitored for five years from January 2013 to September 2023.
Jpn J Radiol
December 2024
Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan.
Recent advancements in breast magnetic resonance imaging (MRI) have significantly enhanced breast cancer detection and characterization. Breast MRI offers superior sensitivity, particularly valuable for high-risk screening and assessing disease extent. Abbreviated protocols have emerged, providing efficient cancer detection while reducing scan time and cost.
View Article and Find Full Text PDFInsights Imaging
December 2024
University Clinic for Radiology and Nuclear Medicine, Otto- von-Guericke-University Magdeburg, Magdeburg, Germany.
Objective: Investigate the association between the relative tumor enhancement (RTE) of gadoxetic acid across various MRI phases and immunohistochemical (IHC) features in patients with liver metastases (LM) from colorectal cancer (CRC), breast cancer (BC), and pancreatic cancer (PC).
Methods: A retrospective analysis was conducted on 68 patients with LM who underwent 1.5-T MRI scans.
J Breast Imaging
October 2024
Breast Imaging Section, Department of Radiology, University of Kansas Medical Center, Kansas City, KS, USA.
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