The potential value of magnetic resonance imaging as a diagnostic tool in infertility was investigated. Twenty-six women with primary or secondary infertility and symptoms of dysmenorrhoea or menorrhagia were studied prospectively using conventional T1- and T2-weighted spin-echo techniques. Positive diagnoses were obtained in 20 of 26 (76.9%) patients. Of these, 18 (69.2%) had lesions likely to be significantly contributing to infertility. Adenomyosis was detected in 14 patients (53.8%) with 11 showing the diffuse pattern while three had discrete adenomyomas. Cystic lesions typical of endometriosis were detected in seven patients (26.9%), four of these also had evidence of adenomyosis. The endometriotic lesions were also seen at laparoscopy in each case. Five patients (19.2%) had leiomyomas, one in a patient with adenomyosis and endometriosis and one in a patient with endometriosis alone. Only one patient had submucous leiomyomas causing significant distortion of the endometrial mucosa likely to affect fertility. Magnetic resonance imaging is valuable in the investigation of unexplained infertility where it provides a high diagnostic yield particularly if uterine pathology is suspected.
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http://dx.doi.org/10.1016/s0009-9260(05)82983-6 | DOI Listing |
BMC Med Imaging
January 2025
Department of Intervention, The First Affiliated Hospital of China Medical University,No.155 The Nanjing North street, Heping District, Shenyang, Liaoning, 110000, P.R. China.
Objective: This study aimed to evaluate the diagnostic value of ColorViz fused images from multi-phase computed tomography angiography (mCTA) using GE Healthcare's FastStroke software for newly diagnosed cerebral infarctions in patients with acute ischemic stroke (AIS).
Methods: A total of 106 AIS patients with unilateral anterior circulation occlusion were prospectively enrolled. All patients underwent mCTA scans during the arterial peak phase, venous peak phase, and venous late phase.
NPJ Precis Oncol
January 2025
Department of Neurosurgery, West China Hospital, Sichuan University, West China Hosptial, No. 37, GuoXue Alley, Chengdu, China.
This study developed and validated a deep learning network using baseline magnetic resonance imaging (MRI) to predict Ki-67 status in meningioma patients. A total of 1239 patients were retrospectively recruited from three hospitals between January 2010 and December 2023, forming training, internal validation, and two external validation cohorts. A representation learning framework was utilized for modeling, and performance was assessed against existing methods.
View Article and Find Full Text PDFEur Radiol
January 2025
Department of Diagnostics and Intervention, Diagnostic Radiology, Umeå University, Umeå, Sweden.
Purpose: To evaluate current MRI-based criteria for malignancy in mesorectal nodal structures in rectal cancer.
Method: Mesorectal nodal structures identified on baseline MRI as lymph nodes were anatomically compared to their corresponding structures histopathologically, reported as lymph nodes, tumour deposits or extramural venous invasion. All anatomically matched nodal structures from patients with primary surgery and all malignant nodal structures from patients with neoadjuvant treatment were included.
Eur Radiol
January 2025
Department of Radiology, Stanford School of Medicine, Stanford, CA, 94305, USA.
Objective: To identify MRI features of desmoid tumors (DTs) that predict the growth of residual disease following ablation.
Methods: Patients who underwent MRI-guided ablation for DTs between February 2013 and April 2021 were included in this single-center IRB-approved retrospective study. MRI scans assessed three suspicious tissue features: intermediate T2 signal [+iT2], nodular appearance [+NOD], and contrast enhancement [+ENH].
Commun Biol
January 2025
Department of Psychology, The University of Hong Kong, Hong Kong, China.
Traditional decision-making models conceptualize humans as adaptive learners utilizing the differences between expected and actual rewards (prediction errors, PEs) to maximize outcomes, but rarely consider the influence of violations of emotional expectations (emotional PEs) and how it differs from reward PEs. Here, we conducted a fMRI experiment (n = 43) using a modified Ultimatum Game to examine how reward and emotional PEs affect punishment decisions in terms of rejecting unfair offers. Our results revealed that reward relative to emotional PEs exerted a stronger prediction to punishment decisions.
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