Objective: To assess the diagnostic value of the Copenhagen index for ovarian malignancy.
Methods: PubMed, Web of Science, the Cochrane Library, Embase, CBM, CNKI, and WanFang databases were searched throughout June 2021. Statistical analyses were performed using Stata 12, Meta-DiSc, and RevMan 5.3. The pooled sensitivity, specificity, and diagnostic odds ratio were calculated, the summary receiver operating characteristic curve was drawn, and the area under the curve was calculated.
Results: Ten articles, including 11 studies with a total of 5266 patients, were included. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.82 [95% CI (0.80-0.83)], 0.88 [95% CI (0.87-0.89)], and 57.31 [95% CI (32.84-100.02)], respectively. The area under the summary receiver operating characteristics curve and the Q index were 0.9545 and 0.8966, respectively.
Conclusion: Our systematic review shows that the sensitivity and specificity of the Copenhagen index are high enough for it to be used in a clinical setting to provide accurate ovarian cancer diagnosis without considering menopausal status.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10266642 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0286650 | PLOS |
Biomed Phys Eng Express
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Radiation Oncology, Emory University, Emory Midtown Hospital, Atlanta, Georgia, 30322, UNITED STATES.
Although radiotherapy techniques are the primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity, and side effect. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on features extracted from pre-treatment imaging data. This study aims to compare different models in predicting radiation-induced xerostomia and sticky saliva in both early and late stage of HNC patients using CT and MRI image features along with demographics and dosimetric information.
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January 2025
Escola Nacional de Saúde Pública, Fundação Oswaldo Cruz. Rio de Janeiro RJ Brasil.
The scope of this study was to determine the diagnostic performance of ABSI for obesity and sarcopenic obesity, compared to the results of bioimpedance analysis (BIA) and BMI, by sex and age group. It involved a cross-sectional study with 12,793 participants in the second round of ELSA-Brasil (Longitudinal Study of Adult Health in Brazil), which obtained measurements of body fat percentage using BIA and anthropometry, verifying the performance of the diagnostic tests in order to compare the indices. The results showed that for obesity in men in all three age groups, the sensitivity was below 49%.
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January 2025
Programa de Pós-graduação em Distúrbios da Comunicação Humana, Departamento de Fonoaudiologia, Universidade Federal de Santa Maria - UFSM - Santa Maria (RS), Brasil.
Purpose: To present the criterion validity, sensitivity, specificity, and cut-off scores for the Profiles of Early Expressive Phonological Skills Test - Brazilian Portuguese (PEEPS-BP) - Expanded List.
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Arq Bras Oftalmol
January 2025
Department of Ophthalmology and Visual Sciences, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, SP, Brazil.
Purpose: To assess the sensitivity and specificity of the retinopathy of prematurity score (ROPScore) and weight, insulin-like growth factor-1, retinopathy of prematurity algorithm in predicting the risk of developing severe retinopathy of prematurity (prethreshold type 1) in a sample of preterm infants in Brazil.
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Brief Bioinform
November 2024
Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, United States.
Pathway analysis plays a critical role in bioinformatics, enabling researchers to identify biological pathways associated with various conditions by analyzing gene expression data. However, the rise of large, multi-center datasets has highlighted limitations in traditional methods like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), which struggle with low signal-to-noise ratios (SNR) and large sample sizes. To tackle these challenges, we use a deep learning-based classification method, Gene PointNet, and a novel $P$-value computation approach leveraging the confusion matrix to address pathway analysis tasks.
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