Bivariate Random-effects Model Research Articles

Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis

BackgroundThe use of the deep learning (DL) approach has been suggested or applied to identify childhood autism spectrum disorder (ASD). The capacity to predict ASD, however, differs across investigations. Our study’s objective was to conduct a meta-analysis to determine the DL for ASD in children’s classification accuracy.MethodsEligibility criteria were designed according to the purpose of the meta-analysis; PubMed, EMBASE, Cochrane Library, and Web of Science Database were searched for articles published up to April 16, 2023, on the accuracy of DL methods for ASD classification. Using the Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) to assess the quality of the included studies. Sensitivity, specificity, areas under the curve (AUC), summary receiver operating characteristic (SROC), and corresponding 95% confidence intervals (CIs) were compiled by using the bivariate random-effects models.ResultsA total of 11 predictive trials based on DL models were included, involving 9495 ASD patients from 6 different databases. According to bivariate random-effects models’ results, the overall sensitivity, specificity, and AUC of the DL technique for ASD were, 0.95 (95% CI = 0.88–0.98), 0.93 (95% CI = 0.85–0.97), and 0.98 (95%CI: 0.97–0.99), respectively. Subgroup analysis results found that different datasets did not cause heterogeneity (meta-regression P = 0.55). The Kaggle dataset’s sensitivity and specificity were 0.94 (95%CI: 0.82-1.00) and 0.91 (95%CI: 0.76-1.00), and with 0.97 (95%CI: 0.92-1.00) and 0.97 (95%CI: 0.92-1.00) for ABIDE dataset.ConclusionsDL techniques has satisfactory sensitivity, specificity, and AUC in ASD classification. However, the major heterogeneity of the included studies limited the effectiveness of this meta-analysis. Further trials need to be performed to demonstrate the clinical practicability of DL diagnosis.

Handgrip Strength: An Early Warning Sign for Mobility Decline? A Meta-Analysis of Diagnostic Accuracy Studies

Background: Mobility limitations significantly impact the quality of life of older adults. Early identification of individuals at risk is crucial for timely intervention. This meta-analysis investigates the diagnostic accuracy of handgrip strength (HGS) in predicting future mobility decline in older adults. Methods: A systematic search of PubMed, Scopus, and Web of Science was conducted for diagnostic accuracy studies published between 2018 and 2024, evaluating the ability of baseline HGS to predict incident mobility limitations in older adults (≥60 years). Mobility limitations were defined as difficulties in performing activities of daily living (ADLs) or instrumental activities of daily living (IADLs). The primary outcomes were sensitivity, specificity, and diagnostic odds ratio (DOR) of HGS for predicting mobility decline. A bivariate random-effects model was used to pool data. Results: Seven diagnostic studies with a total of 3,870 participants were included. The pooled sensitivity of HGS for predicting mobility decline was 0.72 (95% CI: 0.65-0.78), and the pooled specificity was 0.70 (95% CI: 0.66-0.74). The pooled DOR was 4.85 (95% CI: 3.21-7.32), indicating good discriminatory ability. Conclusion: This meta-analysis demonstrates that HGS has moderate sensitivity and specificity for predicting future mobility decline in older adults. HGS assessment can be a valuable tool for identifying individuals at risk, although further research is needed to determine optimal cut-off points and combine HGS with other risk factors for improved prediction.

Frozen Section Analysis of Sentinel Nodes in Patients With Oral Squamous Cell Carcinoma

Frozen section (FS) analysis of sentinel nodes offers potential on-table diagnosis and treatment for occult metastasis in oral squamous cell cancer. Systematic analysis of FS during sentinel node biopsy has not been illuminated in the literature. To systematically review pooled data from studies using FS analysis in evaluating sentinel nodes in patients with cT1-T2 N0 oral squamous cell cancer. An academic librarian led the search of CENTRAL, CINAHL, Cochrane Database of Systematic Reviews, Embase, and MEDLINE for studies published in English between January 2000 and January 2023. Two authors independently screened cohort studies, case series, and randomized clinical trials, in which FS analysis was used to evaluate sentinel nodes in patients with cT1-T2 N0 oral squamous cell cancer. Data were extracted by 2 reviewers. Reporting quality was estimated using the Diagnostic Precision Study Quality Assessment Tool. Data analysis was performed between April and July 2023, and the meta-analysis was completed using the bivariate random-effects model. The primary outcome was the pooled sensitivity of FS sentinel node analysis. Secondary outcomes included evaluation of the FS technique, rate of occult metastasis, false-negative rate, and survival. Seventeen articles with 878 patients met the eligibility criteria. Although protocols varied, confirmatory serial step sectioning was performed in all studies. Occult metastasis was found in 263 of 878 patients (30%), and FS analysis identified 173 cases (65.8%). Following serial sectioning, an additional 90 positive results were identified, leading to 47 patients undergoing staged completion neck dissection. The pooled sensitivity of FS was 0.71 (95% CI, 0.60-0.80), the diagnostic odds ratio was 110, and the false-negative rate was 34.2%. The Cochrane Q value was 15.62 (df = 16; P = .48) and τ2 = 0.36. In this systematic review and meta-analysis, evaluated studies showed various techniques, in which pooled sensitivity reached 0.71, providing a benchmark for comparison to other 1-stop approaches. Due to the high false-negative rate of approximately one-third of patients, intraoperative FS must always be supplemented by serial sectioning. On-table diagnosis remains a key objective for sentinel node biopsy, and FS detection may be improved by standardizing protocols.

What Is the Added Value of DWI Compared With Structured Assessment of BI-RADS Criteria by the Kaiser Score? A Systematic Review and Meta-analysis

Objective This systematic review and meta-analysis investigated the added value of DWI compared with the structured assessment of BI-RADS criteria using the Kaiser score. Materials and Methods Articles published in English until May 2024 were included. Two independent reviewers extracted data on the characteristics of studies evaluating the added value of DWI to distinguish benign from malignant breast lesions compared with structured assessment of the BI-RADS criteria. Using bivariate random-effects models, the sensitivity and specificity were calculated. I 2 statistics, Deek's funnel plot asymmetry test for publication bias, and meta-regression were applied for the data analysis. Results Five studies comprising 1005 malignant and 846 benign lesions were eligible for data synthesis. The pooled sensitivity and specificity estimates of structured BI-RADS assessment were 95.7% (95% confidence interval [CI], 92.6%–97.5%) and 68.7% (95% CI, 60.9%–75.6%), respectively. Adding DWI to the structured BI-RADS assessment achieved a pooled sensitivity of 94.4% (95% CI, 90.5%–96.7%) and a pooled specificity of 74.9% (95% CI, 68.8%–80.2%). Adding DWI to the structured BI-RADS assessment significantly changed neither the sensitivity (P = 0.52) nor the specificity (P = 0.20). Conclusions This systematic review and meta-analysis revealed only a limited, statistically nonsignificant added value of DWI compared with the structured assessment of BI-RADS criteria using the Kaiser score.

Artificial intelligence in commercial fracture detection products: a systematic review and meta-analysis of diagnostic test accuracy

Conventional radiography (CR) is primarily utilized for fracture diagnosis. Artificial intelligence (AI) for CR is a rapidly growing field aimed at enhancing efficiency and increasing diagnostic accuracy. However, the diagnostic performance of commercially available AI fracture detection solutions (CAAI-FDS) for CR in various anatomical regions, their synergy with human assessment, as well as the influence of industry funding on reported accuracy are unknown. Peer-reviewed diagnostic test accuracy (DTA) studies were identified through a systematic review on Pubmed and Embase. Diagnostic performance measures were extracted especially for different subgroups such as product, type of rater (stand-alone AI, human unaided, human aided), funding, and anatomical region. Pooled measures were obtained with a bivariate random effects model. The impact of rater was evaluated with comparative meta-analysis. Seventeen DTA studies of seven CAAI-FDS analyzing 38,978 x-rays with 8,150 fractures were included. Stand-alone AI studies (n = 15) evaluated five CAAI-FDS; four with good sensitivities (> 90%) and moderate specificities (80–90%) and one with very poor sensitivity (< 60%) and excellent specificity (> 95%). Pooled sensitivities were good to excellent, and specificities were moderate to good in all anatomical regions (n = 7) apart from ribs (n = 4; poor sensitivity / moderate specificity) and spine (n = 4; excellent sensitivity / poor specificity). Funded studies (n = 4) had higher sensitivity (+ 5%) and lower specificity (-4%) than non-funded studies (n = 11). Sensitivity did not differ significantly between stand-alone AI and human AI aided ratings (p = 0.316) but specificity was significantly higher the latter group (p < 0.001). Sensitivity was significant lower in human unaided compared to human AI aided respectively stand-alone AI ratings (both p ≤ 0.001); specificity was higher in human unaided ratings compared to stand-alone AI (p < 0.001) and showed no significant differences AI aided ratings (p = 0.316). The study demonstrates good diagnostic accuracy across most CAAI-FDS and anatomical regions, with the highest performance achieved when used in conjunction with human assessment. Diagnostic accuracy appears lower for spine and rib fractures. The impact of industry funding on reported performance is small.

Diagnostic Accuracy of Neuromuscular Ultrasound vs. Electrodiagnostic Studies for Carpal Tunnel Syndrome: Systematic Review and Meta-analysis of Paired Accuracy Studies.

This systematic review with meta-analysis compared the diagnostic accuracy of neuromuscular ultrasound (NMUS) and electrodiagnostic studies (EDX) as confirmatory tests for carpal tunnel syndrome (CTS) diagnosis. We used bivariate random-effects models to estimate pooled sensitivity and specificity and generate hierarchical summary receiver-operating characteristic curves to assess diagnostic test accuracy. Nine paired accuracy studies were included, representing 1751 hands (743 clinically diagnosed CTS; 1008 without CTS) that underwent NMUS and EDX. Compared to the clinical diagnosis reference standard, the pooled sensitivity was 86.4% for NMUS and 91.6% for EDX. Pooled specificity was 79.3% for NMUS and 81.9% for EDX. The positive likelihood ratios were 4.2 and 5.1 for NMUS and EDX, respectively, and the negative likelihood ratios were 0.17 and 0.10, respectively. The diagnostic odds ratio was 24 for NMUS and 49 for EDX. No statistically significant differences were identified between NMUS and EDX for sensitivity, specificity, or overall diagnostic accuracy. Overall, the diagnostic accuracy of NMUS is similar to that of EDX for CTS diagnosis, with high sensitivity and moderate specificity for each. The choice between these confirmatory diagnostic tests should incorporate shared decision-making between patients and providers that weighs diagnostic accuracy as well as factors such as patient preferences, test availability, cost, and tolerability.

Bridging Prenatal Diagnostics and AI: A Systematic Review and Meta-Analysis of the Efficacy of Advanced Algorithms in Identifying Congenital Fetal Abnormalities

Congenital fetal abnormalities have emerged as a major cause of infant mortality globally. About 3% of pregnancies are reported as fetal structural anomaly through ultrasound, ranging from minor defect to severe organ system anomalies. Our study aimed to evaluate effectiveness of Artificial intelligence (AI) algorithms in prediction of fetal heart and brain abnormalities by using meta-analysis approach. In this study, the “Reporting Items for Systematic Review and Meta-Analysis (PRISMA)" guidelines were applied for screening and selection of research articles. We searched the research articles according to research aims from Google scholar, PubMed, and Ovid MEDLINE. The data search was limited to January 2015 to May 2024. Two researchers independently screened the studies to detect research aim oriented studies. After screening of titles, those researchers assessed all abstract for eligibility. The risk bias of included studies was assessed by two researchers who used Cochrane library tool (version 5.4.0). Using the DerSimonian-Laird technique, a bivariate random effect model that generated SROC plots, we pooled test estimates. This method is particularly helpful in visualizing variability in the sensitivity and specificity across studies, accounting for the heterogeneity. This improves the reliability of the SROC plot, offering a clear summary of diagnostic performance Using the software RevMan 5.4, we carried out every statistical analysis, particularly heterogeneity through the Q test. About 243,456 screened fetus or pregnant women of 11-32 gestational weeks through 10 studies based on AI algorithms (machine learning, deep learning and AI diagnostic tool). The imaging protocols including ultrasound and MRI were used to take visuals of fetal brain and heart abnormalities in all of including studies. DL and ML algorithms provided high-performance diagnostic predictions, mostly include CNN models that assist in providing accurate diagnostic results with high sensitivity and specificity. The AI algorithm model showed a generally good accuracy scores for detection of brain and heart abnormalities and reference machine learning findings, with sensitivity ranging from 82 to 99% and specificity from 78 to 99%. Fig. 5's area under the SROC curve was 0.960. In contrast to the general detection, ML and DL seems to be more sensitive in diagnosing fetal brain and heart abnormalities. Our study provided the scientific evidence that AI models including deep learning and Machine learning are effective in generating highly reliable detection or classification results for diagnosis of fetal brain and heart anomalies. These strategies can help in reducing infant mortalities, improving treatment outcomes, and postpartum outcomes among women.

The Diagnostic Accuracy of Colon Capsule Endoscopy in Inflammatory Bowel Disease-A Systematic Review and Meta-Analysis.

Colon capsule endoscopy (CCE) has regained popularity for lower gastrointestinal investigations since the COVID-19 pandemic. While there have been systematic reviews and meta-analyses on colonic polyp detection using CCE, there is a lack of comprehensive evidence concerning colonic inflammation. Therefore, this systematic review and meta-analysis aimed to assess the diagnostic accuracy of CCE for colonic inflammation, predominantly ulcerative colitis (UC) and Crohn's disease (CD). Methods: We systematically searched electronic databases (EMBASE, MEDLINE, PubMed Central, and Cochrane Library) for studies comparing the diagnostic accuracy between CCE and optical endoscopy as the standard reference. A bivariate random effect model was used for the meta-analysis. Results: From 3797 publications, 23 studies involving 1353 patients were included. Nine studies focused on UC, and ten focused on CD. For UC, CCE showed a pooled sensitivity of 92% (95% CI, 88-95%), a specificity of 71% (95% CI, 35-92%), and an AUC of 0.93 (95% CI, 0.89-0.97). For CD, the pooled sensitivity was 92% (95% CI, 89-95%), and the specificity was 88% (95% CI, 84-92%), with an AUC of 0.87 (95% CI, 0.76-0.98). Overall, for inflammatory bowel disease, the pooled sensitivity, specificity, and AUC were 90% (95% CI, 85-93%), 76% (95% CI, 56-90%), and 0.92 (95% CI, 0.94-0.97), respectively. Conclusions: Despite the challenges around standardised disease scoring and the lack of histological confirmation, CCE performs well in diagnosing inflammatory bowel disease. It demonstrates high sensitivity in both UC and Crohn's terminal ileitis and colitis and high specificity in Crohn's disease. Further studies are needed to evaluate the diagnostic accuracy of other colonic inflammatory conditions.

Comparison of the Performance of [18F]-FDG PET/CT and [18F]-FDG PET/MRI for Lymph Node Metastasis in Breast Cancer: A Systematic Review and Meta- Analysis.

The primary objective of this study was to conduct a comparative analysis of the diagnostic efficacy of [18F]-FDG PET/CT and [18F]-FDG PET/MRI in the detection of breast cancer lymph node metastasis. We conducted a comprehensive search on PubMed, Embase, and Web of Science databases, encompassing eligible articles until March 2023. The pooled sensitivity and specificity for [18F]-FDG PET/CT and [18F]-FDG PET/MRI have been reported as estimates with 95% Confidence Intervals (CIs) using a bivariate random-effect model. Utilizing the I square (I2) statistic, heterogeneity among pooled studies was evaluated. The quality assessment of the included studies was conducted using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) methodology. We included 18 studies (2057 patients). The sensitivity, specificity, and AUC (Area Under the Curve) values of [18F]-FDG PET/CT for overall lymph node metastasis in breast cancer have been found to be 0.58 (0.39 - 0.75), 0.83 (0.69-0.92), and 0.79 (0.75-0.82), respectively. Correspondingly, the values for [18F]-FDG PET/MRI were found to be 0.76 (0.60-0.88), 0.85 (0.77-0.91), and 0.89 (0.86-0.91), respectively. The sensitivity, specificity, and AUC values of [18F]-FDG PET/CT for axillary lymph node metastasis in breast cancer were 0.52 (0.37-0.67), 0.84 (0.68-0.92), and 0.73 (0.69-0.76), respectively. Correspondingly, the values for [18F]-FDG PET/MRI were 0.84 (0.76-0.89), 0.87 (0.75-0.94), and 0.86 (0.83-0.89), respectively. This study has suggested [18F]-FDG PET/MRI to have greater diagnostic power than [18F]-FDG PET/CT in detecting lymph node metastasis in breast cancer. However, the [18F]-FDG PET/MRI results have been obtained from a small sample size study, and more and larger prospective studies are needed for further confirmation on this issue.</p>.

Diagnostic accuracy of chest X-ray and CT using artificial intelligence for osteoporosis: systematic review and meta-analysis.

Artificial intelligence (AI)-based systems using chest images are potentially reliable for diagnosing osteoporosis. We performed a systematic review and meta-analysis to assess the diagnostic accuracy of chest X-ray and computed tomography (CT) scans using AI for osteoporosis in accordance with the diagnostic test accuracy guidelines. We included any type of study investigating the diagnostic accuracy of index test for osteoporosis. We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, and IEEE Xplore Digital Library on November 8, 2023. The main outcome measures were the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for osteoporosis and osteopenia. We described forest plots for sensitivity, specificity, and AUC. The summary points were estimated from the bivariate random-effects models. We summarized the overall quality of evidence using the Grades of Recommendation, Assessment, Development, and Evaluation approach. Nine studies with 11,369 participants were included in this review. The pooled sensitivity, specificity, and AUC of chest X-rays for the diagnosis of osteoporosis were 0.83 (95% confidence interval [CI] 0.75, 0.89), 0.76 (95% CI 0.71, 0.80), and 0.86 (95% CI 0.83, 0.89), respectively (certainty of the evidence, low). The pooled sensitivity and specificity of chest CT for the diagnosis of osteoporosis and osteopenia were 0.83 (95% CI 0.69, 0.92) and 0.70 (95% CI 0.61, 0.77), respectively (certainty of the evidence, low and very low). This review suggests that chest X-ray with AI has a high sensitivity for the diagnosis of osteoporosis, highlighting its potential for opportunistic screening. However, the risk of bias of patient selection in most studies were high. More research with adequate participants' selection criteria for screening tool will be needed in the future.

Diagnostic performance of ultrasound elastography in differentiating hepatocellular carcinoma and intrahepatic cholangiocarcinoma: a systematic review and meta-analysis.

To evaluate the diagnostic value of ultrasound elastography (USE) for characterizing hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The protocol was pre-registered a priori at ( https://osf.io/namvk/ ). Using PubMed, Web of Science, Embase, and Cochrane Library, we found studies up to April 20, 2024 by searching HCC, ICC, and USE as keywords. Parameters of USE were directly compared between HCC and ICC patients using random-effects bivariate model on STATA 17.0, MedCalc 20.0, and Psychometrica. Trim & fill method and sensitivity analysis were also performed. Eighteen studies were included with 1057 patients, consisting of 863 HCC lesions, 188 ICC lesions, and 6 mixed lesions. The pooled Emean values of HCC and ICC were 28.3 (CI = 19.8 to 36.8) and 44.0 (CI = 20.9 to 67.2). HCC tumors were 34.3% softer than ICC while peritumoral tissue in HCC lesions was 75% stiffer than ICC lesions based on Emean. The strain value index (tumoral-to-peritumoral ratio) in HCC patients was 49.4% less than that of ICC patients. USE demonstrated a pool sensitivity of 87% (CI = 73-95%), specificity of 82% (CI = 65-92%), positive likelihood ratio of 4.8 (CI = 2.2 to 10.3), negative likelihood ratio of 0.16 (CI = 0.07 to 0.37), and diagnostic odds ratio of 31 (CI = 7 to 127) in differentiation of ICC from HCC. By evaluating tumoral and pre-tumoral stiffness, along with strain value index, USE may provide a valuable quantitative diagnostic tool for accurately differentiating HCC and ICC.

Accuracy of transthoracic lung ultrasound for diagnosing pulmonary embolism: An updated systematic review and meta-analysis

BackgroundComputed tomography pulmonary angiography (CTPA) simplifies the diagnosis of pulmonary embolism (PE) but is not suitable for all patients. Transthoracic lung ultrasound (LUS) is a potential alternative; this meta-analysis evaluates its accuracy for diagnosing PE. MethodsWe systematically searched PubMed, Embase and Cochrane Library from the inception of each database up to April 2024 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies guidelines. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool, and a bivariate random effects model was used to pool sensitivity and specificity. ResultsA total of 18 studies with 2158 patients were analyzed. Lung ultrasound showed a sensitivity of 0.80 (95 %, confidence interval (CI): 0.71–0.86; I2 = 85.2 %) and specificity of 0.87 (95 %, CI: 0.81–0.92; I2 = 87.3 %). The diagnostic score was 3.27 (95 %, CI: 2.75–3.78; I2 = 61.9 %), and the diagnostic odds ratio was 26 (95 %, CI: 16–44; I2 = 100.0 %). The pooled positive likelihood ratio was 6.2 (95 %, CI: 4.2–9.1; I2 = 79.2 %), and the negative likelihood ratio was 0.24 (95 %, CI: 0.16–0.34; I2 = 83.7 %). The summary area under the curve was 0.91 (95 %, CI: 0.88–0.93). Significant heterogeneity was observed, which may impact the generalisability of the results, and no publication bias was detected. ConclusionTransthoracic LUS shows potential as an alternative to CTPA for PE diagnosis, but further research is needed to improve its accuracy and establish standardised diagnostic criteria. The observed heterogeneity highlights the need for a cautious interpretation of the results.

Clinical Validity and Utility of Circulating Tumor DNA (ctDNA) Testing in Advanced Non-small Cell Lung Cancer (aNSCLC): A Systematic Literature Review and Meta-analysis.

Circulating tumor DNA (ctDNA) testing has become a promising tool to guide first-line (1L) targeted treatment for advanced non-small cell lung cancer (aNSCLC). This study aims to estimate the clinical validity (CV) and clinical utility (CU) of ctDNA-based next-generation sequencing (NGS) for oncogenic driver mutations to inform 1L treatment decisions in aNSCLC through a systematic literature review and meta-analysis. A systematic literature search was conducted in PubMed/MEDLINE and Embase to identify randomized control trials or observational studies reporting CV/CU on ctDNA testing in patients with aNSCLC. Meta-analyses were performed using bivariate random-effects models to estimate pooled sensitivity and specificity. Progression-free/overall survival (PFS/OS) was summarized for CU studies. A total of 20 studies were identified: 17 CV only, 2 CU only, and 1 both, and 13 studies were included for the meta-analysis on multi-gene detection. The overall sensitivity and specificity for ctDNA detection of any mutation were 0.69 (95% CI 0.63-0.74) and 0.99 (95% CI 0.97-1.00), respectively. However, sensitivity varied greatly by driver gene, ranging from 0.29 (95% CI 0.13-0.53) for ROS1 to 0.77 (95% CI 0.63-0.86) for KRAS. Two studies that compared PFS with ctDNA versus tissue-based testing followed by 1L targeted therapy found no significant differences. One study reported OS curves on ctDNA-matched and tissue-matched therapies but no hazard ratios were provided. ctDNA testing demonstrated an overall acceptable diagnostic accuracy in patients with aNSCLC, however, sensitivity varied greatly by driver mutation. Further research is needed, especially for uncommon driver mutations, to better understand the CU of ctDNA testing in guiding targeted treatments for aNSCLC.

Diagnostic Accuracy and Safety of Nonsurgical Biopsy for Diagnosing Pulmonary Ground-Glass Opacities: A Systematic Review and Meta-Analysis.

Previous meta-analyses have explored the diagnostic accuracy and safety of computed tomography-guided percutaneous lung biopsy of ground-glass opacities (GGOs). However, no research investigated the role of nonsurgical biopsies (including transbronchial approaches). Additionally, studies reporting the diagnostic accuracy of GGOs with different characteristics are scarce, with no quantitative assessment published to date. We performed a systematic review to explore the diagnostic accuracy and safety of nonsurgical biopsy for diagnosing GGOs, especially those with higher ground-glass components and smaller nodule sizes. A thorough literature search of four databases was performed to compile studies evaluating both or either of the diagnostic accuracy and complications of nonsurgical biopsy for GGOs. A bivariate random-effects model and random-effect model were utilized for data synthesis. The methodological quality of the studies was assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Nineteen eligible studies with a total of 1,379 biopsy-sampled lesions were analyzed, of which 1,124 were confirmed to be malignant. Nonsurgical biopsy reported a pooled sensitivity of 0.89, a specificity of 0.99, and a negative predictive value (NPV) of 60.3%. The overall sensitivity, specificity, and NPV of nonsurgical biopsy for diagnosing GGOs according to GGO component were 0.90, 0.99, and 77.2% in pure GGOs; 0.87, 0.99, and 67.2% in GG-predominant lesions; and 0.89, 1.00, and 44.1% in solid-predominant lesions, respectively. Additionally, the diagnostic sensitivity was better in lesions ≥20 mm than in small lesions (0.95 vs. 0.88). Factors that contributed to higher sensitivity were the use of a coaxial needle system and CT fluoroscopy but not the needle gauge. The summary sensitivity of core needle biopsy (CNB) was not significantly higher than fine needle aspiration (FNA) (0.92 vs. 0.84; p = 0.42); however, we found an increased incidence of hemorrhage in CNB compared with FNA (60.9 vs. 14.2%; p = 0.012). Nonsurgical biopsy for diagnosing GGOs shows high sensitivity and specificity with an acceptably low risk of complications. However, negative biopsy results are unreliable in excluding malignancy, necessitating resampling or subsequent follow-up. The applicability of our study is limited due to significant heterogeneity, indirect comparisons, and the paucity of data on bronchoscopic approaches, restricting the generalizability of our findings to patients requiring transbronchial biopsies.

Potential efficacy of digital polymerase chain reaction for non-invasive prenatal screening of autosomal aneuploidies: a systematic review and meta-analysis

BackgroundDigital Polymerase Chain Reaction (dPCR) presents a promising approach for quantifying DNA and analyzing copy number variants, particularly in non-invasive prenatal testing. This method offers a streamlined and time-efficient procedure in contrast to the widely used next-generation sequencing for non-invasive prenatal testing. Studies have reported encouraging results for dPCR in detecting fetal autosomal aneuploidies. Consequently, this systematic review aimed to evaluate the effectiveness of dPCR in screening for trisomy 21, 18, and 13.MethodsA systematic search was conducted in PubMed, Web of Sciences, and Embase for relevant articles published up to December 30, 2023. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was utilized for the quality assessment of the included articles. Furthermore, a bivariate random-effect regression model was used to conduct a meta-analysis on the utility of dPCR for trisomy 21 screening.ResultsA total of 9 articles were included in this review, with all of them assessing the utility of dPCR in trisomy 21 screening, and 2 and 1 studies conducting additional analysis on the screening abilities of dPCR for trisomy 18 and 13, respectively. A bivariate random-effects model calculated pooled sensitivity and specificity with a 95% confidence interval (CI). Meta-analysis of 6 studies comparing trisomy-21 screening with karyotyping demonstrated dPCR's pooled sensitivity of 98% [95% CI: 94 -100] and specificity of 99% [95% CI: 99 -100]. While conducting a meta-analysis for trisomy 13 and 18 proved impractical, reported values for sensitivity and specificity were favorable.ConclusionsThese findings suggest that dPCR holds promise as an effective tool for non-invasive prenatal testing, presenting a less time-consuming and intricate alternative to next-generation sequencing. However, further research is necessary to evaluate dPCR's applicability in clinical settings and to delineate its specific advantages over next-generation sequencing. This study contributes valuable insights into the potential of dPCR for enhancing prenatal screening methodologies.Trial registrationThe protocol of this study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) on 7/3/2024, with a registration code of CRD42024517523.

Liquid Biopsies Based on Cell-Free DNA Integrity as a Biomarker for Cancer Diagnosis: A Meta-Analysis.

Liquid biopsies have been identified as a viable source of cancer biomarkers. We aim to evaluate the diagnostic accuracy of cell-free DNA integrity (cfDI) in liquid biopsies for cancer. A comprehensive literature search was conducted through PubMed, Embase, Web of Science, and Cochrane Library up to June 2024. Seventy-two study units from forty-six studies, comprising 4286 cancer patients, were identified and evaluated. The Quality Assessment for Studies of Diagnostic Accuracy-2 (QUADAS-2) was used to assess study quality. Meta-regression analysis was employed to investigate the underlying factors contributing to heterogeneity, alongside an evaluation of publication bias. The bivariate random-effect model was utilized to compute the primary diagnostic outcomes and their corresponding 95% confidence intervals (CIs). The pooled sensitivity, specificity, and positive and negative likelihood ratios of cfDI in cancer diagnosis were 0.70 and 0.77, 3.26 and 0.34, respectively. The overall area under the curve was 0.84, with a diagnostic odds ratio of 10.63. This meta-analysis suggested that the cfDI index has a promising potential as a non-invasive and accurate diagnostic tool for cancer. Study registration: The study was registered at PROSPERO (reference No. CRD42021276290).

Inferior vena cava ultrasound to predict hypotension after general anesthesia induction: a systematic review and meta-analysis of observational studies.

Hypotension after induction of general anesthesia is common and is associated with significant adverse events. Identification of patients at high risk can inform the use of preoperative mitigation strategies. We conducted a systematic review and meta-analysis to assess the diagnostic accuracy of the inferior vena cava collapsibility index (IVC-CI) and maximal diameter (dIVCmax) in predicting postinduction hypotension and to identify their predictive performance across different threshold ranges. We searched MEDLINE, PubMed®, and Embase from inception to March 2023 for prospective observational studies exploring the performance of IVC-CI and dIVCmax in predicting postinduction hypotension in adults presenting for elective surgery under general anesthesia. We excluded studies reporting on IVC parameters predicting postinduction hypotension in the obstetric patient population or exclusively in patients with obesity. Trials screening and data extraction were conducted independently. We performed meta-analyses to identify the performance of IVC parameters in predicting postinduction hypotension, followed by subgroup analyses that sought the IVC-CI range with the highest hierarchical summary receiver-operating characteristic area under the curve (HSROC-AUC). We used a bivariate random effects model to calculate summary estimates. We evaluated study quality using Newcastle-Ottawa scores and certainty of evidence using the GRADE framework. We included 14 studies involving 1,166 patients. Pooled sensitivity and specificity of the IVC-CI to predict postinduction hypotension was 0.68 (95% confidence interval [CI], 0.55 to 0.79; coverage probability, 0.91) and 0.78 (95% CI, 0.69 to 0.85; coverage probability, 0.9), respectively, with an HSROC-AUC of 0.80 (95% CI, 0.68 to 0.85, high quality of evidence). An IVC-CI threshold range of 40-45% had an HSROC-AUC of 0.86 (95% CI, 0.69 to 0.93, high quality of evidence). Preoperative IVC-CI is a strong predictor of postinduction hypotension. We recommend that future studies use an IVC-CI threshold of 40-45% (low certainty of evidence). Future studies are needed to establish whether ultrasound-guided preoperative optimization improves outcomes in high-risk patients. PROSPERO ( CRD42022316140 ); first submitted 10 March 2022.

Methods of diagnostic meta-analysis: comparing the generalized linear mixed model and the split component synthesis model

Purpose of review Diagnostic meta-analyses combine data from several diagnostic test accuracy (DTA) studies to provide an in-depth assessment of a specific diagnostic test's performance across diverse populations and settings. Additionally, knowledge on common methods of diagnostic meta-analyses is crucial for researchers to make informed decisions on best practice for reporting analyses and results. This article provides an overview of commonly used methods of diagnostic meta-analyses using real-life and simulation data. Recent findings Advances in methods of diagnostic meta-analyses in recent years have increased uncertainty among researchers in relation to the most suitable method to be used. Currently, the most popular approaches for diagnostic evidence synthesis include hierarchical summary operating characteristic (HSROC) and bivariate random effects models though other methods such as the split component synthesis method have been proposed. In addition, different software modules exist for DTA meta-analyses. Summary This article presents a thorough evaluation of current frequentist DTA meta-analysis methods implementing both simulated and real-world data. By understanding the recent methods of diagnostic meta-analyses and their limitations, clinicians may better be equipped in selecting the optimum approach to improve clinical judgement and consequently better patient outcomes.

Accuracy of clinical diagnosis of behavioural variant frontotemporal dementia: a protocol for a systematic review and meta-analysis

IntroductionBehavioural variant frontotemporal dementia (bvFTD) characterisation has evolved, but diagnosis remains challenging, relying on clinical diagnostic criteria that have undergone revisions over time. In this systematic review, our aims are...

The American College of Radiology contrast-enhanced ultrasound Liver Imaging Reporting and Data System and its modified version in diagnosing hepatocellular carcinoma via Sonazoid: a meta-analysis.

The American College of Radiology (ACR) developed the contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) for pure blood contrast agents, but Sonazoid was not included. Modifications to LI-RADS have been proposed for Sonazoid. The purpose of this meta-analysis was to identify and compare the diagnostic efficacy of the two LI-RADS algorithms of Sonazoid. We searched the PubMed, MEDLINE, Web of Science, Embase, and Cochrane Library databases from databases inception to August 31, 2023, to find original studies on the ACR LI-RADS and/or modified LI-RADS algorithm with Sonazoid used as the contrast agent in patients with high-risk hepatocellular carcinoma (HCC). A bivariate random-effects model was used. Data pooling, meta-regression, and sensitivity analysis were performed for meta-analysis. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was used to assess the methodological quality, and the Deeks funnel plot asymmetry test was used to evaluate the publication bias. A meta-analysis of 10 studies with 1,611 observations was conducted. The pooled data for ACR LI-RADS category 5 (LR-5) and modified LR-5 were respectively as follows: pooled sensitivity, 0.70 [95% confidence interval (CI): 0.64-0.75] and 0.81 (95% CI: 0.76-0.86) (P<0.05); pooled specificity, 0.90 (95% CI: 0.82-0.94) and 0.87 (95% CI: 0.81-0.91) (P>0.05); and pooled area under the summary receiver operating characteristic curve, 0.84 and 0.91. The diagnostic performance of LI-RADS category M (LR-M) of the two algorithms was comparable. Study heterogeneity was observed. The results indicated that modified LR-5 algorithm demonstrated improved diagnostic sensitivity compared with the ACR LR-5 algorithm of Sonazoid, with differences observed between the different versions. Further research is needed to validate and explore the optimal diagnostic criteria for HCC using Sonazoid. Before the database search was conducted, this study was registered on PROSPERO (International Prospective Register of Systematic Reviews; CRD42023455220).