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| http://dx.doi.org/10.4103/1008-682X.196855 | DOI Listing |
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Detecting Hemorrhagic Myocardial Infarction With 3.0-T CMR: Insights Into Spatial Manifestation, Time-Dependence, and Optimal Acquisitions.
JACC Cardiovasc Imaging
January 2025
Department of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA. Electronic address:
Background: Hemorrhagic myocardial infarction (hMI) can rapidly diminish the benefits of reperfusion therapy and direct the heart toward chronic heart failure. T2∗ cardiac magnetic resonance (CMR) is the reference standard for detecting hMI. However, the lack of clarity around the earliest time point for detection, time-dependent changes in hemorrhage volume, and the optimal methods for detection can limit the development of strategies to manage hMI.
View Article and Find Full Text PDFMachine Learning-Based Prediction of Prostate Biopsy Necessity Using PSA, MRI, and Hematologic Parameters.
J Clin Med
December 2024
Department of Urology, Health Science University Eskisehir City Health Application and Research Center, 26080 Eskisehir, Turkey.
To establish a machine learning (ML) model for predicting prostate biopsy outcomes using prostate-specific antigen (PSA) values, multiparametric magnetic resonance imaging (mpMRI) findings, and hematologic parameters. The medical records of the patients who had undergone a prostate biopsy were evaluated. Laboratory findings, mpMRI findings, and prostate biopsy results were collected.
View Article and Find Full Text PDFResearch Progress on Glioma Microenvironment and Invasiveness Utilizing Advanced Multi-Parametric Quantitative MRI.
Cancers (Basel)
December 2024
Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang 110001, China.
Magnetic resonance imaging (MRI) currently serves as the primary diagnostic method for glioma detection and monitoring. The integration of neurosurgery, radiation therapy, pathology, and radiology in a multi-disciplinary approach has significantly advanced its diagnosis and treatment. However, the prognosis remains unfavorable due to treatment resistance, inconsistent response rates, and high recurrence rates after surgery.
View Article and Find Full Text PDFAutomatic Characterization of Prostate Suspect Lesions on T2-Weighted Image Acquisitions Using Texture Features and Machine-Learning Methods: A Pilot Study.
Diagnostics (Basel)
January 2025
Department of Medical Imaging, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania.
: Prostate cancer (PCa) is the most frequent neoplasia in the male population. According to the International Society of Urological Pathology (ISUP), PCa can be divided into two major groups, based on their prognosis and treatment options. Multiparametric magnetic resonance imaging (mpMRI) holds a central role in PCa assessment; however, it does not have a one-to-one correspondence with the histopathological grading of tumors.
View Article and Find Full Text PDFUtility of Multiparametric Breast MRI Radiomics to Predict Cyclin D1 and TGF-β1 Expression.
J Comput Assist Tomogr
January 2025
Department of Radiology, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing, Zhejiang, China.
Objective: To develop a machine learning model that integrates clinical features and multisequence MRI radiomics for noninvasively predicting the expression status of prognostic-related factors cyclin D1 and TGF-β1 in breast cancer, providing additional information for the clinical development of personalized treatment plans.
Methods: A total of 123 breast cancer patients confirmed by surgical pathology were retrospectively enrolled in our Hospital from January 2016 to July 2022. The patients were randomly divided into a training group (87 cases) and a validation group (36 cases).
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