Diagnostic Odds Ratio Research Articles

Diagnostic efficacy of [99mTc]Tc-PSMA SPECT/CT for prostate cancer: a meta-analysis

BackgroundPrompt and accurate diagnosis of prostate cancer (PCa) is of paramount importance for effective treatment planning. While Gallium-68 labeled prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) has proven efficacy in detecting PCa, limited availability poses challenges. As a potential alternative, [99mTc]Tc-PSMA single photon emission computed tomography (SPECT)/computed tomography (CT) holds promise. This systematic review and meta-analysis aimed to evaluate the diagnostic value of [99mTc]Tc-PSMA SPECT/CT for prostate cancer.MethodsA comprehensive search of PubMed, Cochrane, EMBASE, Scopus, Ovid, and Web of Science databases was conducted until July 2024. Sensitivity and specificity data were extracted to assess the diagnostic accuracy of [99mTc]Tc-PSMA SPECT/CT, while the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to evaluate study quality. Statistical analyses were performed using STATA 18, with MetaDisc 1.4 employed to detect threshold effects. Diagnostic accuracy indicators, including sensitivity, specificity, diagnostic odds ratio (DOR), negative likelihood ratio (LR-), and positive likelihood ratio (LR+), were pooled. The area under the curve (AUC) of the combined model was calculated using summary receiver-operating characteristic (SROC) curves.ResultsSeven studies meeting the inclusion criteria were identified from an initial pool of 1467 articles, with no publication bias observed. The pooled sensitivity, specificity, and AUC of [99mTc]Tc-PSMA SPECT/CT were found to be 0.89 (95% CI, 0.84–0.93), 0.92 (95% CI, 0.67–0.99), and 0.93 (95% CI, 0.90–0.95), respectively. Additionally, the comprehensive diagnostic odds ratio, diagnostic score, positive likelihood ratio, and negative likelihood ratio were calculated as 95.24 (95% CI, 17.30-524.41), 4.56 (95% CI, 2.85–6.26), 11.35 (95% CI, 2.31–55.71), and 0.12 (95% CI, 0.08–0.18), respectively.ConclusionsIn conclusion, our findings demonstrate that [99mTc]Tc-PSMA SPECT/CT exhibits favorable diagnostic performance for prostate cancer and can provide valuable supplementary information, particularly in regions and settings where [68Ga]Ga-PSMA PET/CT availability is limited, such as remote areas. These results highlight the potential of [99mTc]Tc-PSMA SPECT/CT as a valuable tool in the diagnosis and management of prostate cancer, warranting further investigation and validation in larger patient cohorts.

Predicting telomerase reverse transcriptase promoter mutation in glioma: A systematic review and diagnostic meta-analysis on machine learning algorithms.

Glioma is one of the most common primary brain tumors. The presence of the telomerase reverse transcriptase promoter (pTERT) mutation is associated with a better prognosis. This study aims to investigate the TERT mutation in patients with glioma using machine learning (ML) algorithms on radiographic imaging. This study was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The electronic databases of PubMed, Embase, Scopus, and Web of Science were searched from inception to August 1, 2023. The statistical analysis was performed using the MIDAS package of STATA v.17. A total of 22 studies involving 5371 patients were included for data extraction, with data synthesis based on 11 reports. The analysis revealed a pooled sensitivity of 0.86 (95% CI: 0.78-0.92) and a specificity of 0.80 (95% CI 0.72-0.86). The positive and negative likelihood ratios were 4.23 (95% CI: 2.99-5.99) and 0.18 (95% CI: 0.11-0.29), respectively. The pooled diagnostic score was 3.18 (95% CI: 2.45-3.91), with a diagnostic odds ratio 24.08 (95% CI: 11.63-49.87). The Summary Receiver Operating Characteristic (SROC) curve had an area under the curve (AUC) of 0.89 (95% CI: 0.86-0.91). The study suggests that ML can predict TERT mutation status in glioma patients. ML models showed high sensitivity (0.86) and moderate specificity (0.80), aiding disease prognosis and treatment planning. However, further development and improvement of ML models are necessary for better performance metrics and increased reliability in clinical practice.

Diagnostic Value of Hepcidin in Sepsis: A Systematic Review and Meta-Analysis.

Objectives: To investigate the diagnostic value of hepcidin for sepsis diagnosis. Methods: The relevant literature on hepcidin for sepsis diagnosis published up to October 20, 2023, was systematically searched in the Web of Science, PubMed, Embase, and China Knowledge Network databases. Two researchers screened the literature and extracted relevant data according to the inclusion and exclusion criteria. Study quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Meta-analysis and calculation of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were performed using State16 and Review Manager 5.3 software. Furthermore, receiver operating characteristic curve (ROC) was plotted, and the respective area under the curve (AUC) was calculated to assess the accuracy of hepcidin. Publication bias was evaluated using Deeks' funnel plot asymmetry test. Results: Overall, 1047 patients from 8 studies were included (625 patients with sepsis and 422 controls). The quality of the literature was relatively moderate. Meta-analysis demonstrated the presence of heterogeneity in the data (I2 > 50%, P < .05), and a randomized model was employed to combine the diagnostic indicators. Regarding its accuracy for sepsis diagnosis, hepcidin demonstrated a pooled sensitivity of 0.88 (95% confidence interval [CI]: 0.76-0.94) and specificity of 0.91 (95% CI: 0.76-0.97). The diagnostic odds ratio was 69.00 (95% CI: 19.00-253.00), and the ROC curve revealed an AUC of 0.95. Additionally, Deeks' funnel plot asymmetry test demonstrated absence of publication bias. Conclusions: Our meta-analysis suggested that hepcidin has a high diagnostic value in sepsis and may be a valuable diagnostic tool.

Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.

The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential for unbiased, highly accurate diagnostic insights. A meta-analysis was designed to analyze the diagnostic accuracy of deep learning of MRI images on AD and MCI models. A meta-analysis was performed across PubMed, Embase, and Cochrane library databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focusing on the diagnostic accuracy of deep learning. Subsequently, methodological quality was assessed using the QUADAS-2 checklist. Diagnostic measures, including sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) were analyzed, alongside subgroup analyses for T1-weighted and non-T1-weighted MRI. A total of 18 eligible studies were identified. The Spearman correlation coefficient was -0.6506. Meta-analysis showed that the combined sensitivity and specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.84, 0.86, 6.0, 0.19, and 32, respectively. The AUROC was 0.92. The quiescent point of hierarchical summary of receiver operating characteristic (HSROC) was 3.463. Notably, the images of 12 studies were acquired by T1-weighted MRI alone, and those of the other 6 were gathered by non-T1-weighted MRI alone. Overall, deep learning of MRI for the diagnosis of AD and MCI showed good sensitivity and specificity and contributed to improving diagnostic accuracy.

Deep learning for temporomandibular joint arthropathies: Asystematic review and meta-analysis.

The accurate diagnosis of temporomandibular disorders continues to be a challenge, despite the existence of internationally agreed-upon diagnostic criteria. The purpose of this study is to review applications of deep learning models in the diagnosis of temporomandibular joint arthropathies. An electronic search was conducted on PubMed, Scopus, Embase, Google Scholar, IEEE, arXiv, and medRxiv up to June 2023. Studies that reported the efficacy (outcome) of prediction, object detection or classification of TMJ arthropathies by deep learning models (intervention) of human joint-based or arthrogenous TMDs (population) in comparison to reference standard (comparison) were included. To evaluate the risk of bias, included studies were critically analysed using the quality assessment of diagnostic accuracy studies (QUADAS-2). Diagnostic odds ratios (DOR) were calculated. Forrest plot and funnel plot were created using STATA 17 and MetaDiSc. Full text review was performed on 46 out of the 1056 identified studies and 21 studies met the eligibility criteria and were included in the systematic review. Four studies were graded as having a low risk of bias for all domains of QUADAS-2. The accuracy of all included studies ranged from 74% to 100%. Sensitivity ranged from 54% to 100%, specificity: 85%-100%, Dice coefficient: 85%-98%, and AUC: 77%-99%. The datasets were then pooled based on the sensitivity, specificity, and dataset size of seven studies that qualified for meta-analysis. The pooled sensitivity was 95% (85%-99%), specificity: 92% (86%-96%), and AUC: 97% (96%-98%). DORs were 232 (74-729). According to Deek's funnel plot and statistical evaluation (p =.49), publication bias was not present. Deep learning models can detect TMJ arthropathies high sensitivity and specificity. Clinicians, and especially those not specialized in orofacial pain, may benefit from this methodology for assessing TMD as it facilitates a rigorous and evidence-based framework, objective measurements, and advanced analysis techniques, ultimately enhancing diagnostic accuracy.

Diagnostic Accuracy of Cone-Beam CT for Acute Intracranial Hemorrhage: A Systematic Review and Meta-Analysis

ObjectiveOur purpose was to synthesize evidence in the literature to determine the diagnostic accuracy of cone-beam CT (CBCT) for the detection of intracranial hemorrhage (ICH) and hemorrhage types, including intraparenchymal hemorrhage (IPH), subarachnoid hemorrhage (SAH), and intraventricular hemorrhage (IVH). MethodsWe performed a meta-analysis following the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines. Our protocol was registered with International Prospective Register of Systematic Reviews (PROSPERO-CRD42021261915). Systematic searches were last performed on April 30, 2024, in EMBASE, PubMed, Web-of-Science, Scopus, and Cumulative Index to Nursing and Allied Health Literature databases. Inclusion criteria were (1) studies reporting diagnostic metrics of CBCT for ICH and (2) studies using a reference standard to determine ICH. Exclusion criteria were (1) case reports, abstracts, reviews and (2) studies without patient-level data. Pooled estimates and 95% confidence intervals (CIs) were calculated for diagnostic odds ratios (DORs), sensitivity, and specificity using random-effects and common-effects models. Mixed methods appraisal tool was used to evaluate risk of bias. ResultsSeven studies were included in the meta-analysis yielding 466 patients. Mean or median age ranged from 54 to 75 years. Female patients represented 51.4% (222 of 432) in reported studies. Multidetector CT was the reference standard in all studies. DOR, pooled sensitivity, and pooled specificity for ICH were 5.28 (95% CI: 4.11-6.46), 0.88 (95% CI: 0.79-0.97), and 0.99 (95% CI: 0.98-1.0). Pooled sensitivity for IPH, SAH, and IVH was 0.98 (95% CI: 0.95-1.0), 0.82 (95% CI: 0.57-1.0), and 0.78 (95% CI: 0.55-1.0). Pooled specificity for IPH, SAH, and IVH was 0.99 (95% CI: 0.98-1.0), 0.99 (95% CI: 0.97-1.0), and 1.0 (95% CI: 0.98-1.0). DiscussionCBCT had moderate DOR and high pooled specificity for ICH and hemorrhage types. However, pooled sensitivity varied by hemorrhage type, with the highest sensitivity for IPH, followed by SAH and IVH.

Diagnostic Accuracy of Artificial Intelligence Compared to Biopsy in Detecting Early Oral Squamous Cell Carcinoma: A Systematic Review and Meta Analysis.

To summarize and compare the existing evidence on diagnostic accuracy of artificial intelligence (AI) models in detecting early oral squamous cell carcinoma (OSCC). Review was performed in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis - Diagnostic Test Accuracy (PRISMA- DTA) checklist and the review protocol is registered under PROSPERO(CRD42023456355). PubMed, Google Scholar, EBSCOhost were searched from January 2000 to November 2023 to identify the diagnostic potential of AI based tools and models. True-positive, false-positive, true-negative, false-negative, sensitivity, specificity values were extracted or calculated if not present for each study. Quality of selected studies was evaluated based on QUADAS (Quality assessment of diagnostic accuracy studies)- 2 tool. Meta-analysis was performed in Meta-Disc 1.4 software and Review Manager 5.3 RevMan using a bivariate model parameter for the sensitivity and specificity and summary points, summary receiver operating curve (SROC), diagnostic odds ratio (DOR) confidence region, and area under curve (AUC) were calculated. Fourteen studies were included for qualitative synthesis and for meta-analysis. Included studies had presence of low to moderate risk of bias. Pooled sensitivity and specificity of 0.43 (CI 0.18- 0.71) and 0.50 (CI 0.20- 0.80) was observed with a pooled positive likelihood ratio of (PLR) 0.86 (0.43 - 1.71) and negative likelihood ratio (NLR) of 1.04 (0.42 - 1.68) was observed with DOR of 0.78 (0.12 - 5.18) and overall accuracy (AUC) being 0.45 respectively. AI based tools has poor to moderate overall diagnostic accuracy. However, to validate our study findings further more standardized diagnostic accuracy studies should be conducted with proper reporting through QUADAS-2 tool. Thus, we can conclude AI based based tool for secondary level of prevention for early OSCC under early diagnosis and prompt treatment.

Performance of the IMMY® sona Aspergillus lateral flow assay for the detection of galactomannan in tracheal aspirate samples from Brazilian patients with COVID-19-associated pulmonary aspergillosis: Cross-sectional and systematic review of literature.

During the COVID-19 pandemic, many patients in intensive care units (ICUs) were affected by invasive fungal infections, including aspergillosis, contributing to a high mortality rate. Diagnosing proven COVID-19-associated pulmonary aspergillosis (CAPA) requires clinical and radiological evaluations, along with laboratory testing of bronchoalveolar lavage samples or lung biopsies. However, these procedures and equipment are often inaccessible in developing countries or regions with limited resources, including Brazil. Consequently, alternative diagnostic methods, such as measuring Aspergillus galactomannan (GM) in tracheal aspirate (TA), have been explored for CAPA diagnosis. Nonetheless, research on the efficacy of TA-based diagnostic tests is limited. This study aimed to assess the performance of the IMMY® Sona Aspergillus lateral flow assay (LFA) for GM detection in TA samples from 60 ICU patients with suspected CAPA at two tertiary hospitals in Campo Grande, Brazil. The ELISA method (Platelia Aspergillus AG, Bio-Rad®) was used to detect Aspergillus GM in TA samples, serving as the microbiological criterion and reference test. Fifteen patients (12.4%) were identified as having possible CAPA. The overall accuracy of LFA was 94%, and the tests demonstrated an agreement of 93.1% (Cohen's kappa of 0.83). Based on our findings, the LFA for Aspergillus GM detection in TA samples exhibited excellent performance, proving to be a valuable diagnostic tool for potential CAPA. In a systematic review, two studies were included, and the meta-analysis revealed pooled estimates provided a sensitivity of 86% (95% CI, 80%-91%) and specificity of 93% (95% CI, 86%-97%). The diagnostic odds ratio (DOR) for identification of Aspergillus using LFA was 103.38 (95% CI, 38.03-281.03). Despite its lower sensitivity compared to our study, the LFA appears to be a promising diagnostic option for CAPA, particularly in suspected cases that have not received antifungal therapy. This enables timely antifungal treatment and could reduce mortality rates in regions where bronchoscopy is unavailable or limited.

Diagnostic Accuracy of Contrast Enhanced Computed Tomography (CECT) in Cervical Lymph Node Metastasis of Oral Carcinoma: A Systematic Review and Meta-Analysis.

To compare the diagnostic accuracy of Contrast Enhanced Computed Tomography (CECT) compared to conventional imaging modalities and histopathological investigation in cervical lymph node metastasis in adults through a novel meta- analysis. The review protocol is registered under PROSPERO(CRD42021225704) and performed in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis - Diagnostic Test Accuracy (PRISMA- DTA) checklist. Databases like PubMed, Google Scholar, EBSCOhost were searched from 2000 to 2023 to identify the diagnostic potential of CECT in cervical lymph node metastasis of oral carcinoma. True-positive, false-positive, true-negative, false-negative, sensitivity, specificity values were extracted or calculated if not present for each study. Quality of selected studies was evaluated based on Quality assessment of diagnostic accuracy studies (QUADAS)- 2 tool. Meta-analysis was performed in Meta-Disc 1.4 software and Review Manager 5.3 using a bivariate model parameter for the pooled sensitivity and pooled specificity. Additional analysis was performed in terms of positive likelihood ratio (+LR), negative likelihood ratio (-LR), diagnostic odds ratio (DOR) and summary receiver operating characteristics (SROC) with Area Under Curve (AUC) and p<0.05 as statistically significant. Six studies were included for qualitative synthesis and as well as for meta-analysis. Two studies had high risk of bias while four studies had low risk of bias. 651 patients underwent CECT and were taken for meta-analysis. The meta-analysis revealed that CECT for diagnosing cervical lymph node metastasis had a pooled sensitivity of 71%, pooled specificity of 14% with 60% Area Under Curve (AUC). +LR of 0.84, -LR of 1.36 and DOR of 0.59. CECT has an overall fair diagnostic ability and is a valid and reliable tool in diagnosing the target condition overcoming high reliance on master specialized capacity for their execution and understanding like other conventional imaging techniques. CECT can be concluded for secondary level of prevention for cervical node metastasis of oral carcinoma under early diagnosis and prompt treatment. However, further standardized accuracy studies are indicated to improve the overall diagnostic accuracy of CECT.

Association between lipid accumulation product index and chronic kidney disease: A systematic review and meta-analysis.

Diabetes mellitus and lipid metabolism disorders are increasingly recognized as key contributors to the development of chronic kidney disease (CKD). The lipid accumulation product (LAP) index, a novel marker of lipid accumulation, has potential implications for CKD risk assessment. The present meta-analysis aimed to assess the association between LAP index and CKD, with an emphasis on varying impacts in diabetic and non-diabetic populations. A comprehensive search for relevant publications was performed using PubMed/MEDLINE, Scopus, Cochrane Library, ScienceDirect and Google Scholar databases, and a meta-analysis of 17 studies was performed to investigate the relationship between LAP index and CKD. The random-effects inverse-variance model employing the DerSimonian-Laird estimator for τ² was utilized to calculate pooled odds ratios (ORs). Diagnostic accuracy was assessed using summary receiver operating characteristic (ROC) curves, with calculations of the area under the ROC curve (AUROC), sensitivity, specificity, likelihood ratios and diagnostic OR. The pooled OR for the association between higher quintiles or tertiles of LAP index and CKD was 1.098 (95% CI: 1.043-1.152), with substantial heterogeneity (I²=91.2%) and evidence of publication bias. Subgroup analysis revealed a stronger association in non-diabetic (OR=2.422, 95% CI: 1.802-3.042) compared with diabetic patients (OR=1.018, 95% CI: 0.993-1.043). The diagnostic accuracy of LAP index for CKD was moderate (AUROC=0.64), with sensitivity and specificity estimates of 0.58 and 0.63, respectively. In conclusion, in the present study, LAP index demonstrated a modest but significant association with CKD, particularly in non-diabetic patients. Despite its moderate diagnostic accuracy, the LAP index could serve as a valuable tool in CKD risk stratification, particularly when integrated with other clinical markers.

Prognostic significance of non-coding RNAs related to the tumorigenic epithelial-mesenchymal transition (EMT) process among ovarian cancer patients: A systematic review and meta-analysis

IntroductionOvarian cancer is the seventh most prevalent cancer among women. It has high mortality and morbidity and imposes a great burden on healthcare systems worldwide. Unraveling the mechanisms behind the Epithelial-Mesenchymal Transition and finding a panel for predicting the prognosis of the disease may help find the appropriate treatment approaches for the management of the disease. The overarching aim of this systematic review was to define a panel of different types of EMT-associated non-coding RNAs (ncRNAs) with significant prognostic value in all types of ovarian cancers. MethodsWe searched PubMed, Web of Science, Scopus, and Embase till Jun 2024 to retrieve relevant papers. Two independent reviewers screened papers, and discrepancies were resolved by consensus. Publications related to the dysregulation of different types of ncRNAs, including microRNAs, lncRNAs, and circRNAs, only in patients with ovarian cancer were included. The participation of ncRNAs in epithelial-mesenchymal transformation should be assessed via methods evaluating different EMT-related proteins. To assess the quality and risk of bias for the included case-control and cohort studies, refined Newcastle-Ottawa Scale (NOS) and Quadas-2 were recruited. A bivariate meta-analysis was performed to analyze extracted data. ResultsA total of 37 studies with overall 42 non-coding RNAs (15 microRNA, 24 long non-coding RNAs, and 3 circular RNAs) were entered into the analysis. Overall diagnostic odds ratio for ncRNAs in lymph node metastasis, distant metastasis, TNM stage, and clinical stage were 4.19, 3.80, 6.52, and 3.97, respectively. Also, a hazard ratio of 1.39 (P = 0.32) for overall survival was observed. Bioinformatic analyses on the Pan-cancer database demonstrated a significant correlation between low expression of miRNA and high expression of lncRNAs with poor prognosis of ovarian cancer. ConclusionBased on the results, the defined panel of ncRNAs can properly predict prognostic factors related to EMT in ovarian cancer without involving potentially invasive methods.

Diagnostic accuracy of a machine learning-based radiomics approach of MR in predicting IDH mutations in glioma patients: a systematic review and meta-analysis.

To assess the diagnostic accuracy of machine learning (ML)-based radiomics for predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma. A systematic search of PubMed, Web of Science, Embase, and the Cochrane Library from inception to 1 September 2023, was conducted to collect all articles investigating the diagnostic performance of ML for the prediction of IDH mutations in gliomas. Two reviewers independently screened all papers for eligibility. Methodological quality and risk of bias were assessed using the METhodological RadiomICs Score and Quality Assessment of Diagnostic Accuracy Studies-2, respectively. The pooled sensitivity, specificity, and 95% confidence intervals were calculated, and the area under the receiver operating characteristic curve (AUC) was obtained. In total, 14 original articles assessing 1740 patients with gliomas were included. The AUC of ML for predicting IDH mutation was 0.90 (0.87-0.92). The pooled sensitivity, specificity, and diagnostic odds ratio were 0.83 (0.71-0.90), 0.84 (0.74-0.90), and 25 (12,50) respectively. In subgroup analyses, modeling methods, glioma grade, and the combination of magnetic resonance imaging and clinical features affected the diagnostic performance in predicting IDH mutations in gliomas. ML-based radiomics demonstrated excellent diagnostic performance in predicting IDH mutations in gliomas. Factors influencing the diagnosis included the modeling methods employed, glioma grade, and whether the model incorporated clinical features. https://www.crd.york.ac.uk/PROSPERO/#myprospero, PROSPERO registry (CRD 42023395444).

Prognostic value of CT scan-based radiomics in intracerebral hemorrhage patients: A systematic review and meta-analysis

ObjectivesWe conducted a systematic review and meta-analysis of current publications on the potential role of non-contrast-enhanced computed tomography (NCCT) radiomics as a prognostic indicator in patients with intracerebral hemorrhage (ICH). MethodsWe systematically searched PubMed, EMBASE, and the Web of Science from inception until January 8, 2024. Studies with NCCT-based radiomics features for predicting the prognostic outcomes of ICH patients were included. We calculated the pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under curve (AUC) values. The radiomics quality score (RQS), METhodological RadiomICs Score (METRICS), and the quality assessment of diagnostic accuracy studies (QUADAS-2) were used for quality assessment. ResultsTwenty-two studies were included. The pooled sensitivity, specificity, DOR, and AUC of radiomics models were 0.73, 0.78, 10.03, and 0.83, respectively, while on the combined radiomics models with other non-radiomics features were 0.80, 0.80, 16.28, and 0.86. Subgroup analysis showed that studies with the following covariates have a higher accuracy: single center, modified Rankin Scale (mRS) criteria for the ICH outcomes assessment, following patients for evaluation of ICH outcomes for more than a month, automatic segmentation, capturing the radiomics feature from the only intra-hematomal region, using PyRadiomic tool for features extraction, and using non-logistic regression for modeling. The quality of literature using QUADAS-2 and METRICS tools was good and was under-average using the RQS tool. No publication bias was detected. ConclusionsRadiomics features showed moderate to high accuracy for predicting ICH prognostic outcomes. Although the QUADAS-2 and METRICS assessments indicated good quality, the radiomics pipeline quality was under-average. Clinical RelevanceNCCT-based radiomics features can provide information about the prognostic outcomes of ICH patients after patient admission. This study exploits the value of current evidence on NCCT-based radiomics methodology in the prediction of ICH prognosis.

Deep Learning Algorithms for the Detection of Suspicious Pigmented Skin Lesions in Primary Care Settings: A Systematic Review and Meta-Analysis.

Early detection of suspicious pigmented skin lesions is crucial for improving the outcomes and survival rates of skin cancers. However, the accuracy of clinical diagnosis by primary care physicians (PCPs) is suboptimal, leading to unnecessary referrals and biopsies. In recent years, deep learning (DL)algorithms have shown promising results in the automated detection and classification of skin lesions. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of DL algorithms for the detection of suspicious pigmented skin lesions in primary care settings. A comprehensive literature search was conducted using electronic databases, including PubMed, Scopus, IEEE Xplore, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science. Data from eligible studies were extracted, including study characteristics, sample size, algorithm type, sensitivity, specificity, diagnostic odds ratio (DOR), positivelikelihood ratio (PLR), negative likelihood ratio(NLR), and receiver operating characteristic curve analysis. Three studies were included. The results showed that DL algorithms had a high sensitivity (90%, 95% CI: 90-91%) and specificity (85%, 95% CI: 84-86%) for detecting suspicious pigmented skin lesions in primary care settings. Significant heterogeneity was observed in both sensitivity (p = 0.0062, I2 = 80.3%) and specificity (p < 0.001, I2 = 98.8%). The analysis of DOR and PLR further demonstrated the strong diagnostic performance of DL algorithms. The DOR was 26.39, indicating a strong overall diagnostic performance of DL algorithms. The PLR was 4.30, highlighting the ability of these algorithms to influence diagnostic outcomes positively. The NLR was 0.16, indicating that a negative test result decreased the odds of misdiagnosis. The area under the curve of DL algorithms was 0.95, indicating excellent discriminative ability in distinguishing between benign and malignant pigmented skin lesions. DL algorithms have the potential to significantly improve the detection of suspicious pigmented skin lesions in primary care settings. Our analysis showed that DL exhibited promising performance in the early detection of suspicious pigmented skin lesions. However, further studies are needed.

Artificial intelligence/machine learning for neuroimaging to predict hemorrhagic transformation: Systematic review/meta-analysis.

Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for treatment decisions and early intervention. The purpose of this study was to conduct a systematic review and meta-analysis on the performance of artificial intelligence (AI) and machine learning (ML) models that utilize neuroimaging to predict HT. A systematic search of PubMed, EMBASE, and Web of Science was conducted until February 19, 2024. Inclusion criteria were as follows: patients with AIS who received reperfusion therapy; AI/ML algorithm using imaging to predict HT; or presence of sufficient data on the predictive performance. Exclusion criteria were as follows: articles with less than 20 patients; articles lacking algorithms that operate solely on images; or articles not detailing the algorithm used. The quality of eligible studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging. Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using a random-effects model, and a summary receiver operating characteristic curve was constructed using the Reitsma method. We identified six eligible studies, which included 1640 patients. Aside from an unclear risk of bias regarding flow and timing identified in two of the studies, all studies showed low risk of bias and applicability concerns in all categories. Pooled sensitivity, specificity, and DOR were.849,.878, and 45.598, respectively. AI/ML models can reliably predict the occurrence of HT in AIS patients. More prospective studies are needed for subgroup analyses and higher clinical certainty and usefulness.

Diagnostic performance of the (1-3)-β-d-glucan assay in patients with different risks for invasive fungal diseases.

We evaluated the diagnostic performance of the β-d-glucan (BDG) test (Beijing Gold Mountain River Tech) in diagnosing invasive fungal disease (IFD) and its variations among patients with different risks. Patients≥18 years old who underwent a serum BDG test (positive cutoff value>80 pg/ml) from April 2017 through May 2018 were collected consecutively. Patients were classified into three groups: group 1, patients with host factors as defined by the prior 2008 European Organization for Research and Treatment (EORTC) criteria; group 2, those with extended host factors in 2020 EORTC criteria; and group 3, those without any risk factor mentioned in the criteria. IFD was defined by 2020 EORTC criteria, but BDG was not considered. Diagnostic performance of the serum BDG test was measured by the area under the curve (AUC) of the receiver-operating characteristic curve. Among 469 patients, 15.4% (72/469) were diagnosed with IFD (48/191 [25.1%], 14/144 [9.7%], and 10/134 [7.5%] in groups 1, 2, and 3, respectively). The BDG assay showed fair performance (AUC 0.748 [95% CI: 0.688-0.810]). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 77.8%, 60.7%, 26.4%, and 93.8%, respectively. PPV was higher in group 1, and NPV was higher in group 3. Additionally, diagnostic odds ratios were 6.73, 2.88, and 5.92 in groups 1, 2, and 3. Immunosuppressant use, non-IFD/Candida colonization, and central venous catheter were associated with false positivity. Clinicians should cautiously interpret the BDG assay, considering the various diagnostic performances depending on the different levels of risk.

Meta-analysis of the value of dual-energy computed tomography in the diagnosis of anterior cruciate ligament injuries of the knee

BackgroundThis meta-analysis assessed the efficacy of dual-energy computed tomography (DECT) in the diagnosis of anterior cruciate ligament (ACL) injuries.MethodsThe literature search was performed up to December 8, 2023, and included a comprehensive examination of several databases: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP. Diagnostic metrics sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and a summary receiver operating characteristic (SROC) were determined using a bivariate model analysis. Heterogeneity within the data was explored through subgroup analyses, which considered variables including geographical region, use of magnetic resonance imaging (MRI), arthroscopy, and study design.ResultsThe analysis included ten studies encompassing 544 patients. DECT demonstrated substantial diagnostic utility for ACL injuries of the knee, with a sensitivity of 0.91 (95% confidence interval [CI]: 0.88–0.94), a specificity of 0.90 (95% CI: 0.81–0.95), a PLR of 9.20 (95% CI: 4.50–19.00), a NLR of 0.10 (95% CI: 0.06–0.14), a DOR of 97.00 (95% CI: 35.00–268.00), and an area under the curve (AUC) of 0.95 (95% CI: 0.93–0.97). The subgroup analyses consistently showed high diagnostic precision for ACL injuries across Asian population (sensitivity: 0.91, specificity: 0.91, PLR: 9.90, NLR: 0.09, DOR: 105.00, AUC: 0.96), in MRI subgroup (sensitivity: 0.85, specificity: 0.94, PLR: 9.57, NLR: 0.18, DOR: 56.00, AUC: 0.93), in arthroscopy subgroup (sensitivity: 0.92, specificity: 0.89, PLR: 8.40, NLR: 0.09, DOR: 94.00, AUC: 0.95), for prospective studies (sensitivity: 0.92, specificity: 0.88, PLR: 7.40, NLR: 0.09, DOR: 78.00, AUC: 0.95), and for retrospective studies (sensitivity: 0.91, specificity: 0.93, AUC: 0.93).ConclusionDECT exhibits a high value in diagnosing ACL injuries. The significant diagnostic value of DECT provides clinicians with a powerful tool that enhances the accuracy and efficiency of diagnosis and optimizes patient management and treatment outcomes.

Evaluation of preoperative difficult airway prediction methods for adult patients without obvious airway abnormalities: a systematic review and meta-analysis

BackgroundThis systematic review aims to assist clinical decision-making in selecting appropriate preoperative prediction methods for difficult tracheal intubation by identifying and synthesizing literature on these methods in adult patients undergoing all types of surgery.MethodsA systematic review and meta-analysis were conducted following PRISMA guidelines. Comprehensive electronic searches across multiple databases were completed on March 28, 2023. Two researchers independently screened, selected studies, and extracted data. A total of 227 articles representing 526 studies were included and evaluated for bias using the QUADAS-2 tool. Meta-Disc software computed pooled sensitivity (SEN), specificity (SPC), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Heterogeneity was assessed using the Spearman correlation coefficient, Cochran’s-Q, and I2 index, with meta-regression exploring sources of heterogeneity. Publication bias was evaluated using Deeks’ funnel plot.ResultsOut of 2906 articles retrieved, 227 met the inclusion criteria, encompassing a total of 686,089 patients. The review examined 11 methods for predicting difficult tracheal intubation, categorized into physical examination, multivariate scoring system, and imaging test. The modified Mallampati test (MMT) showed a SEN of 0.39 and SPC of 0.86, while the thyromental distance (TMD) had a SEN of 0.38 and SPC of 0.83. The upper lip bite test (ULBT) presented a SEN of 0.52 and SPC of 0.84. Multivariate scoring systems like LEMON and Wilson’s risk score demonstrated moderate sensitivity and specificity. Imaging tests, particularly ultrasound-based methods such as the distance from the skin to the epiglottis (US-DSE), exhibited higher sensitivity (0.80) and specificity (0.77). Significant heterogeneity was identified across studies, influenced by factors such as sample size and study design.ConclusionNo single preoperative prediction method shows clear superiority for predicting difficult tracheal intubation. The evidence supports a combined approach using multiple methods tailored to specific patient demographics and clinical contexts. Future research should focus on integrating advanced technologies like artificial intelligence and deep learning to improve predictive models. Standardizing testing procedures and establishing clear cut-off values are essential for enhancing prediction reliability and accuracy. Implementing a multi-modal predictive approach may reduce unanticipated difficult intubations, improving patient safety and outcomes.

Evaluating the diagnostic accuracy of microRNA-494 as a biomarker in cancers: A systematic review and meta-analysis

Abstract MicroRNA-494 (miR-494) has emerged as a potential diagnostic biomarker for cancer detection, but conflicting reports have led to uncertainty regarding its clinical utility. This study aims to address these discrepancies by conducting a comprehensive meta-analysis of miR-494 diagnostic performance across various cancer types. A comprehensive literature search was performed across multiple databases, including PubMed, Web of Science, Wanfang databases, and CNKI (China National Knowledge Infrastructure), with a cutoff date of April 23, 2024. Eligible studies were identified using predefined inclusion criteria and various search strategies to ensure a thorough coverage of the available evidence. To evaluate the diagnostic performance of miR-494 in cancer detection, relevant measures such as sensitivity, specificity, and other diagnostic accuracy indicators were extracted from the included studies. These data were synthesized using bivariate meta-analysis models to generate pooled estimates of miR-494 diagnostic performance. All statistical analyses were conducted using the STATA 16.0 software. This meta-analysis pooled data from 8 studies, comprising a total of 647 cancer cases and 407 healthy controls. The aggregated diagnostic performance of miR-494 was as follows: a sensitivity of 0.67 (95% confidence interval [CI], 0.52–0.80), a specificity of 0.85 (95% CI, 0.77–0.91), and an area under the curve of 0.86 (95% CI, 0.82–0.88), indicating good overall diagnostic accuracy. The diagnostic odds ratio (DOR) was 12.11 (95% CI, 7–21), suggesting that miR-494 has strong discriminatory power in distinguishing cancer patients from healthy individuals. The positive likelihood ratio of 4.62 (95% CI, 3.1–6.8) and negative likelihood ratio of 0.38 (95% CI, 0.26–0.56) further support the diagnostic utility of miR-494. Deeks’ funnel plot asymmetry test was employed to assess potential publication bias, yielding a P value of 0.50, which suggests the absence of significant bias in the included studies. The meta-analysis results suggest that miR-494 exhibits promising diagnostic performance in detecting cancer, with moderate accuracy. The pooled sensitivity, specificity, and high area under the receiver operating characteristic curve highlight its potential as a cancer biomarker, indicating its utility in early detection and accurate diagnosis.

GLIM Achieves Best Diagnostic Performance in Non-Cancer Patients with Low BMI: A Hierarchical Bayesian Latent-Class Meta-Analysis.

Global Leadership Initiative on Malnutrition (GLIM) and Patient-Generated Subjective Global Assessment (PG-SGA) are commonly used nutrition assessment tools, whose performance does not reach a consensus due to different and imperfect reference standards. This study aimed to evaluate and compare the diagnostic accuracy of GLIM and PG-SGA, using a hierarchical Bayesian latent class model, in the absence of a gold standard. A systematic search was undertaken in PubMed, Embase, and Web of Science from inception to October 2022. Diagnostic test studies comparing (1) the GLIM and/or (2) PG-SGA with "semi-gold" standard assessment tools for malnutrition were included. Two authors independently extracted data on sensitivity, specificity, and other key characteristics. The methodological quality of each included study was appraised according to the criteria in the Quality Assessment of Diagnostic Accuracy Studies-2. A total of 45 studies, comprising 20 876 individuals evaluated for GLIM and 11 575 for PG-SGA, were included. The pooled sensitivity was 0.833 (95% CI 0.744 to 0.896) for GLIM and 0.874 (0.797 to 0.925) for PG-SGA, while the pooled specificity was 0.837 (0.780 to 0.882) for GLIM and 0.778 (0.707 to 0.836) for PG-SGA. GLIM showed slightly better performance than PG-SGA, with a higher diagnostic odds ratio (25.791 vs 24.396). The diagnostic performance of GLIM was most effective in non-cancer patients with an average body mass index (BMI) of <24kg/m2, followed by non-cancer patients with an average age of ≥60 years. PG-SGA was most powerful in cancer patients with an average age of <60 years, followed by cancer patients with an average BMI of <24 kg/m2. Both GLIM and PG-SGA had moderately high diagnostic capabilities. GLIM was most effective in non-cancer patients with a low BMI, while PG-SGA was more applicable in cancer patients. PROSPERO registration No. CRD42022380409.