scholarly journals MetaTREE, a Novel Database Focused on Metabolic Trees, Predicts an Important Detoxification Mechanism: The Glutathione Conjugation

Molecules ◽  
2021 ◽  
Vol 26 (7) ◽  
pp. 2098
Author(s):  
Angelica Mazzolari ◽  
Luca Sommaruga ◽  
Alessandro Pedretti ◽  
Giulio Vistoli
Keyword(s):  
Binary Classification ◽  
Meta Analysis ◽  
False Negative ◽  
Data Accuracy ◽  
Applicability Domain ◽  
Glutathione Conjugation ◽  
Detoxification Mechanism ◽  
Background Data ◽  
Convenient Approach ◽  
Metabolic Data

(1) Background: Data accuracy plays a key role in determining the model performances and the field of metabolism prediction suffers from the lack of truly reliable data. To enhance the accuracy of metabolic data, we recently proposed a manually curated database collected by a meta-analysis of the specialized literature (MetaQSAR). Here we aim to further increase data accuracy by focusing on publications reporting exhaustive metabolic trees. This selection should indeed reduce the number of false negative data. (2) Methods: A new metabolic database (MetaTREE) was thus collected and utilized to extract a dataset for metabolic data concerning glutathione conjugation (MT-dataset). After proper pre-processing, this dataset, along with the corresponding dataset extracted from MetaQSAR (MQ-dataset), was utilized to develop binary classification models using a random forest algorithm. (3) Results: The comparison of the models generated by the two collected datasets reveals the better performances reached by the MT-dataset (MCC raised from 0.63 to 0.67, sensitivity from 0.56 to 0.58). The analysis of the applicability domain also confirms that the model based on the MT-dataset shows a more robust predictive power with a larger applicability domain. (4) Conclusions: These results confirm that focusing on metabolic trees represents a convenient approach to increase data accuracy by reducing the false negative cases. The encouraging performances shown by the models developed by the MT-dataset invites to use of MetaTREE for predictive studies in the field of xenobiotic metabolism.

The role of serum periostin in the diagnosis of asthma: A meta-analysis

2020 ◽  
Vol 41 (4) ◽  
pp. 240-247
Author(s):  
Lei Yang ◽  
Qingtao Zhao ◽  
Shuyu Wang
Keyword(s):  
Diagnostic Accuracy ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
False Negative ◽  
Summary Receiver Operating Characteristic ◽  
True Negative ◽  
Negative Results ◽  
Noninvasive Biomarker ◽  
Sensitivity Specificity ◽  
Serum Periostin

Background: Serum periostin has been proposed as a noninvasive biomarker for asthma diagnosis and management. However, its accuracy for the diagnosis of asthma in different populations is not completely clear. Methods: This meta-analysis aimed to evaluate the diagnostic accuracy of periostin level in the clinical determination of asthma. Several medical literature data bases were searched for relevant studies through December 1, 2019. The numbers of patients with true-positive, false-positive, false-negative, and true-negative results for the periostin level were extracted from each individual study. We assessed the risk of bias by using Quality Assessment of Diagnostic Accuracy Studies 2. We used the meta-analysis to produce summary estimates of accuracy. Results: In total, nine studies with 1757 subjects met the inclusion criteria. The pooled estimates of sensitivity, specificity, and diagnostic odds ratios for the detection of asthma were 0.58 (95% confidence interval [CI], 0.38‐0.76), 0.86 (95% CI, 0.74‐0.93), and 8.28 (95% CI, 3.67‐18.68), respectively. The area under the summary receiver operating characteristic curve was 0.82 (95% CI, 0.79‐0.85). And significant publication bias was found in this meta‐analysis (p = 0.39). Conclusion: Serum periostin may be used for the diagnosis of asthma, with moderate diagnostic accuracy.

scholarly journals Pretherapeutic Imaging for Axillary Staging in Breast Cancer: A Systematic Review and Meta-Analysis of Ultrasound, MRI and FDG PET

2021 ◽  
Vol 10 (7) ◽  
pp. 1543
Author(s):  
Morwenn Le Boulc’h ◽  
Julia Gilhodes ◽  
Zara Steinmeyer ◽  
Sébastien Molière ◽  
Carole Mathelin
Keyword(s):  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
False Negative ◽  
Significant Proportion ◽  
False Negative Rate ◽  
True Positive Rate ◽  
Axillary Staging ◽  
Positron Emission ◽  
Positive Rate

Background: This systematic review aimed at comparing performances of ultrasonography (US), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography (PET) for axillary staging, with a focus on micro- or micrometastases. Methods: A search for relevant studies published between January 2002 and March 2018 was conducted in MEDLINE database. Study quality was assessed using the QUality Assessment of Diagnostic Accuracy Studies checklist. Sensitivity and specificity were meta-analyzed using a bivariate random effects approach; Results: Across 62 studies (n = 10,374 patients), sensitivity and specificity to detect metastatic ALN were, respectively, 51% (95% CI: 43–59%) and 100% (95% CI: 99–100%) for US, 83% (95% CI: 72–91%) and 85% (95% CI: 72–92%) for MRI, and 49% (95% CI: 39–59%) and 94% (95% CI: 91–96%) for PET. Interestingly, US detects a significant proportion of macrometastases (false negative rate was 0.28 (0.22, 0.34) for more than 2 metastatic ALN and 0.96 (0.86, 0.99) for micrometastases). In contrast, PET tends to detect a significant proportion of micrometastases (true positive rate = 0.41 (0.29, 0.54)). Data are not available for MRI. Conclusions: In comparison with MRI and PET Fluorodeoxyglucose (FDG), US is an effective technique for axillary triage, especially to detect high metastatic burden without upstaging majority of micrometastases.

scholarly journals 18F-Fluciclovine Positron Emission Tomography in Prostate Cancer: A Systematic Review and Diagnostic Meta-Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 304
Author(s):  
Giuseppina Biscontini ◽  
Cinzia Romagnolo ◽  
Chiara Cottignoli ◽  
Andrea Palucci ◽  
Fabio Massimo Fringuelli ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Computed Tomography ◽  
Positron Emission Tomography ◽  
Meta Analysis ◽  
False Negative ◽  
Final Analysis ◽  
Emission Tomography ◽  
True Negative ◽  
Positron Emission ◽  
Pet Ct

Background: to explore the diagnostic accuracy of 18F-Fluciclovine positron-emission tomography (PET) in prostate cancer (PCa), considering both primary staging prior to radical therapy, biochemical recurrence, and advanced setting. Methods: A systematic web search through Embase and Medline was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Studies performed from 2011 to 2020 were evaluated. The terms used were “PET” or “positron emission tomography” or “positron emission tomography/computed tomography” or “PET/CT” or “positron emission tomography-computed tomography” or “PET-CT” and “Fluciclovine” or “FACBC” and “prostatic neoplasms” or “prostate cancer” or “prostate carcinoma”. Only studies reporting about true positive (TP), true negative (TN), false positive (FP) and false negative (FN) findings of 18F-fluciclovine PET were considered eligible. Results: Fifteen out of 283 studies, and 697 patients, were included in the final analysis. The pooled sensitivity for 18F-Fluciclovine PET/CT for diagnosis of primary PCa was 0.83 (95% CI: 0.80–0.86), the specificity of 0.77 (95% CI: 0.74–0.80). The pooled sensitivity for preoperative LN staging was 0.57 (95% CI: 0.39–0.73) and specificity of 0.99 (95% CI: 0.94–1.00). The pooled sensitivity for the overall detection of recurrence in relapsed patients was 0.68 (95% CI: 0.63–0.73), and specificity of 0.68 (95% CI: 0.60–0.75). Conclusion: This meta-analysis showed promising results in term of sensitivity and specificity for 18F-Fluciclovine PET/CT to stage the primary lesion and in the assessment of nodal metastases, and for the detection of PCa locations in the recurrent setting. However, the limited number of studies and the broad heterogeneity in the selected cohorts and in different investigation protocols are limitation affecting the strength of these results.

AEC Classifier: A Tree-Based Classifier with Error Control for Medical Disease Diagnosis and Other Applications

2021 ◽  
Vol 15 (02) ◽  
pp. 241-262
Author(s):  
Wasif Bokhari ◽  
Ajay Bansal
Keyword(s):  
Error Control ◽  
Binary Classification ◽  
False Negative ◽  
Disease Diagnosis ◽  
False Negatives ◽  
Medical Disease ◽  
Unique Nature ◽  
Comparable Accuracy ◽  
The Cost ◽  
Roc Score

In medical disease diagnosis, the cost of a false negative could greatly outweigh the cost of a false positive. This is because the former could cost a life, whereas the latter may only cause medical costs and stress to the patient. The unique nature of this problem highlights the need of asymmetric error control for binary classification applications. In this domain, traditional machine learning classifiers may not be ideal as they do not provide a way to control the number of false negatives below a certain threshold. This paper proposes a novel tree-based binary classification algorithm that can control the number of false negatives with a mathematical guarantee, based on Neyman–Pearson (NP) Lemma. This classifier is evaluated on the data obtained from different heart studies and it predicts the risk of cardiac disease, not only with comparable accuracy and AUC-ROC score but also with full control over the number of false negatives. The methodology used to construct this classifier can be expanded to many more use cases, not only in medical disease diagnosis but also beyond as shown from analysis on different diverse datasets.

Large-scale randomized evidence: Trials and meta-analyses of trials

2020 ◽  
pp. 51-66
Author(s):  
Colin Baigent ◽  
Richard Peto ◽  
Richard Gray ◽  
Natalie Staplin ◽  
Sarah Parish ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Large Scale ◽  
Meta Analysis ◽  
False Negative ◽  
Premature Deaths ◽  
Common Causes ◽  
The Common ◽  
Meta Analyses ◽  
Positive Results

Clinical trials generally need to be able to detect or to refute realistically moderate (but still worthwhile) differences between treatments in long-term disease outcome. Large-scale randomized evidence should be able to detect such effects, but medium-sized trials or medium-sized meta-analyses can, and often do, yield false-negative or exaggeratedly positive results. Hundreds of thousands of premature deaths each year could be avoided by seeking appropriately large-scale randomized evidence about various widely practicable treatments for the common causes of death, and by disseminating this evidence appropriately. This chapter takes a look at the use of large-scale randomized evidence—produced from trials and meta-analysis of trials—and how this data should be handled in order to produce accurate result.

The diagnostic value of 11C-methionine PET in hyperparathyroidism with negative 99mTc-MIBI SPECT: a meta-analysis

2016 ◽  
Vol 58 (5) ◽  
pp. 558-564 ◽  
Author(s):  
Leilei Yuan ◽  
Jun Liu ◽  
Ying Kan ◽  
Jigang Yang ◽  
Xufu Wang
Keyword(s):  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
False Negative ◽  
Total Sample ◽  
Diagnostic Value ◽  
Published Data ◽  
Diagnostic Modality ◽  
Positron Emission ◽  
99Mtc Mibi ◽  
Mibi Spect

Background 99mTc-sestamibi (MIBI) parathyroid SPECT is generally regarded as the best preoperative localizing method in patients with hyperparathyroidism (HPT). However, 99mTc-MIBI SPECT is false negative in approximately 25% of adenomas. 11C-methionine positron emission tomography (PET) has been used in HPT with negative 99mTc-MIBI SPECT scan results. Purpose To systematically review and conduct a meta-analysis of published data on the performance of 11C-methionine PET in patients with HPT with negative 99mTc-MIBI SPECT. Material and Methods A comprehensive review of the literature was performed. Pooled sensitivity and specificity of 11C-methionine PET in patients with HPT and a negative 99mTc-MIBI SPECT was calculated on a per-patient basis using receiver-operating characteristic (ROC) methodology. Results Nine studies that met all inclusion and exclusion criteria were included into our meta-analysis, comprising a total sample size of 137 patients. Pooled sensitivity and specificity of 11C-methionine PET in patients with HPT with negative or inconclusive 99mTc-MIBI SPECT scans was 86% and 86%, respectively. The area under the ROC curve was 0.87. Conclusion By merit of the high overall sensitivity, specificity, and accuracy, 11C-methionine PET can potentially complement the diagnostic workup of patients with HPT and negative or inconclusive 99mTc-MIBI SPECT. 11C-methionine PET appears to be a promising diagnostic modality in complicated cases with HPT.

Usefulness of Nasal Endoscopy for Diagnosing Patients With Chronic Rhinosinusitis: A Meta-Analysis

2019 ◽  
Vol 34 (2) ◽  
pp. 306-314
Author(s):  
Do Hyun Kim ◽  
Youngjun Seo ◽  
Kyung Min Kim ◽  
Seoungmin Lee ◽  
Se Hwan Hwang
Keyword(s):  
Diagnostic Accuracy ◽  
Chronic Rhinosinusitis ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
False Negative ◽  
Nasal Endoscopy ◽  
Summary Receiver Operating Characteristic ◽  
True Negative ◽  
Endoscopic Score

Background We evaluated the accuracy of nasal endoscopy in diagnosing chronic rhinosinusitis (CRS) compared with paranasal sinus computed tomography (CT). Methods Two authors independently searched the 5 databases (PubMed, SCOPUS, Embase, the Web of Science, and the Cochrane database) up to March 2019. For all included studies, we calculated correlation coefficients between the endoscopic and CT scores. We extracted data on true-positive and false-positive and true-negative and false-negative results. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool (version 2). Results We included 16 observational or retrospective studies. A high correlation ( r = .8543; 95% confidence interval [CI] [0.7685–0.9401], P < .0001, I2 = 76.58%) between endoscopy and CT in terms of the diagnostic accuracy for CRS was apparent. The odds ratio (Lund–Kennedy endoscopic score ≥1) was 7.915 (95% CI [4.435–14.124]; I2 = 28.361%). The area under the summary receiver operating characteristic curve was 0.765. The sensitivity and specificity were 0.726 (95% CI [0.584–0.834]) and 0.767 (95% CI [0.685–0.849]), respectively. However, high interstudy heterogeneity was evident given the different endoscopic score thresholds used (Lund–Kennedy endoscopic score ≥1 vs 2). In a subgroup analysis of studies using a Lund–Kennedy endoscopic score threshold ≥2, the area under the summary curve was 0.881, and the sensitivity and specificity were 0.874 (95% CI [0.783–0.930]) and 0.793 (95% CI [0.366–0.962]), respectively. Conclusion Nasal endoscopy is a useful diagnostic tool; the Lund–Kennedy score was comparable with that of CT.

scholarly journals Dispersion matters: Diagnostics and control data computer simulation in Concealed Information Test studies

2020 ◽  
Author(s):  
Gáspár Lukács ◽  
Eva Specker
Keyword(s):  
Computer Simulation ◽  
Binary Classification ◽  
Meta Analysis ◽  
Simulated Data ◽  
Lie Detection ◽  
Detection Methods ◽  
Diagnostic Efficiency ◽  
Concealed Information Test ◽  
Concealed Information ◽  
Classification Efficiency

Binary classification has numerous applications. For one, lie detection methods typically aim to classify each tested person either as “liar” or as “truthteller” based on the given test results. To infer practical implications, as well as to compare different methods, it is essential to assess the diagnostic efficiency, such as demonstrating the number of correctly classified persons. However, this is not always straightforward. In Concealed Information Tests (CITs), the key predictor value (probe-irrelevant difference) for “truthtellers” is always similar (zero on average), and “liars” are always distinguished by a larger value (i.e., a larger number resulting from the CIT test, as compared to the zero baseline). Thereby, in general, the larger predictor values a given CIT method obtains for “liars” on average, the better this method is assumed to be. This has indeed been assumed in countless studies, and therefore, when comparing the classification efficiencies of two different designs, the mean difference of “liar” predictor values in the two designs were simply compared to each other (hence not collecting “truthteller” data to spare resources). We show, based on the meta-data of 12 different experimental designs collected in response time-based CIT studies, that differences in dispersion (i.e., variance in the data, e.g. the extent of random deviations from the zero average in case of “truthtellers”) can substantially influence classification efficiency–to the point that, in extreme cases, one design may even be superior in classification despite having a larger mean “liar” predictor value. However, we also introduce a computer simulation procedure to estimate classification efficiency in the absence of “truthteller” data, and validate this procedure via a meta-analysis comparing outcomes based on empirical data versus simulated data.

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