Background/aims: The prognostic accuracy of mild cognitive impairment (MCI) in clinical settings is debated, variable across criteria, cut-offs, subtypes, and follow-up time. We aimed to estimate the prognostic accuracy of MCI and the MCI subtypes for dementia using three different cut-off levels.
Methods: Memory clinic patients were followed for 2 (n = 317, age 63.7 ± 7.8) and 4-6 (n = 168, age 62.6 ± 7.4) years. We used 2.0, 1.5, and 1.0 standard deviations (SD) below the mean of normal controls (n = 120, age 64.1 ± 6.6) to categorize MCI and the MCI subtypes. Prognostic accuracy for dementia syndrome at follow-up was estimated.
Results: Amnestic multi-domain MCI (aMCI-md) significantly predicted dementia under all conditions, most markedly when speed/attention, language, or executive function was impaired alongside memory. For aMCI-md, sensitivity increased and specificity decreased when the cut-off was lowered from 2.0 to 1.5 and 1.0 SD. Non-subtyped MCI had a high sensitivity and a low specificity.
Conclusion: Our results suggest that aMCI-md is the only viable subtype for predicting dementia for both follow-up times. Lowering the cut-off decreases the positive predictive value and increases the negative predictive value of aMCI-md. The results are important for understanding the clinical prognostic utility of MCI, and MCI as a non-progressive disorder.
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http://dx.doi.org/10.1159/000477341 | DOI Listing |
Eur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu City, Sichuan Province, 610041, China.
Background: Pathological grade is a critical determinant of clinical outcomes and decision-making of follicular lymphoma (FL). This study aimed to develop a deep learning model as a digital biopsy for the non-invasive identification of FL grade.
Methods: This study retrospectively included 513 FL patients from five independent hospital centers, randomly divided into training, internal validation, and external validation cohorts.
Front Oncol
December 2024
Gynecologic Oncology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Background: Ovarian cancer (OC) represents a common neoplasm within the female reproductive tract. The prognosis for patients diagnosed at advanced stages is unfavorable, primarily attributable to the absence of reliable screening markers for early detection. An elevated neutrophil-to-lymphocyte ratio (NLR) serves as an indicator of host inflammatory response and has been linked to poorer overall survival (OS) across various cancer types; however, its examination in OC remains limited.
View Article and Find Full Text PDFWomens Health Rep (New Rochelle)
January 2025
Institute of Epidemiology and Preventive Medicine, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
Background: Ovarian cancer is one of the top seven causes of cancer deaths. Incidence of ovarian cancer varies by ethnicity, where Asian women demonstrate lower incidence rates than non-Hispanic Blacks and Whites. Survival prediction models for ovarian cancer have been developed for Caucasians and Black populations using national databases; however, whether these models work for Asians is unclear.
View Article and Find Full Text PDFTurk J Emerg Med
January 2025
Department of Emergency Medicine, Faculty of Medicine, Hacettepe University, Ankara, Türkiye.
Objectives: Traditional scoring systems have been widely used to predict acute pancreatitis (AP) severity but have limitations in predictive accuracy. This study investigates the use of machine learning (ML) algorithms to improve predictive accuracy in AP.
Methods: A retrospective study was conducted using data from 101 AP patients in a tertiary hospital in Türkiye.
Nucl Med Mol Imaging
February 2025
National Cyclotron and PET Centre, Chulabhorn Hospital, 906 Kamphaeng Phet 6 Rd., Talat Bang Khen, Lak Si, Bangkok, 10210 Thailand.
Purpose: Prostate-specific membrane antigen (PSMA) Positron emission tomography/magnetic resonance imaging (PET/MRI) surpasses conventional MRI (cMRI) in prostate cancer (PCa) evaluation. Our objective is to evaluate correlation of quantitative parameters in PCa using Fluorine-18 (F-18) PSMA-1007 PET/MRI and their potential for predicting metastases.
Methods: This retrospective study included 51 PCa patients.
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