SROC Curve Research Articles

Diagnostic and predictive value of radiomics-based machine learning for intracranial aneurysm rupture status: a systematic review and meta-analysis.

Currently, the growing interest in radiomics within the clinical practice has prompted some researchers to differentiate the rupture status of intracranial aneurysm (IA) by developing radiomics-based machine learning models. However, systematic evidence supporting its performance remains scarce. The purpose of this meta-analysis and systematic review is to assess the diagnostic performance of radiomics-based machine learning for the early detection of IA rupture and to offer evidence-based recommendations for the application of radiomics in this area.PubMed, Cochrane, Embase, and Web of Science databases were searched systematically up to March 2, 2024. The Radiomics Quality Score (RQS) was employed to assess the risk of bias in all included primary studies. We separately discussed the diagnostic or predictive performance of machine learning for IA rupture status based on task type (diagnosis or prediction).We finally included 15 original studies covering 9,111 IA cases. In the validation cohort, radiomics demonstrated a sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, as well as SROC curve of 0.84 (95% CI: 0.76-0.90), 0.82 (95% CI: 0.77-0.86), 4.7 (95% CI: 3.7-5.8), 0.19 (95% CI: 0.13-0.29), and 24 (95% CI: 15-40), respectively, for the diagnostic task of aneurysm rupture status. Only 2 studies (3 models) addressed predictive tasks, with sensitivity and specificity ranging from 0.77 to 0.89 and from 0.69 to 0.87, respectively.Radiomics-based machine learning exhibits promising accuracy for early identification of IA rupture status, whereas evidence for its predictive capability is limited. Further research is needed to validate predictive models and provide insights for developing specialized strategies to prevent aneurysm rupture.

The Systemic Immune Inflammation Index as a Novel Predictive Biomarker for Contrast-Induced Acute Kidney Injury Risk Following Percutaneous Coronary Intervention: A Meta-Analysis of Cohort Studies.

Contrast-induced Acute Kidney Injury (CI-AKI) frequently occurs as a complication following PCI, making the identification of high-risk patients challenging. While the systemic immune inflammation index (SII) might aid in predicting CI-AKI, the current evidence remains insufficient. We conducted a systematic literature search using PubMed, Web of Science, Embase, and the Cochrane Library, with a cut-off date of 3/20/2024. We included observational studies that examined the predictive value of SII for the risk of CI-AKI. This meta-analysis encompassed 8 studies with a combined total of 6301 participants. Results showed pooled sensitivity and specificity of 0.73 (95% CI 0.69-0.76) and 0.68 (95% CI 0.57- 0.77), respectively. The sROC curve analysis indicated an AUC of 0.74 (95% CI 0.70-0.78). The risk of publication bias was low (p = 0.18). The results of this study suggest that SII has a relatively high sensitivity and could function as a biomarker for the prediction of CI-AKI risk in people receiving PCI treatment.

Effects of amide proton transfer imaging in diagnosis, grading and prognosis prediction of cervical cancer: a systematic review and meta-analysis

PurposeTo assess the effectiveness of Amide Proton Transfer (APT) imaging in predicting the histopathological characteristics of cervical cancer. MethodsA comprehensive literature search was conducted across multiple databases, covering studies until December 27, 2023. The meta-analysis was performed using Stata 15 and Review Manager 5.4 software. Key metrics analyzed included pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and summary receiver operating characteristic (sROC) curves. The analysis focused on differentiating cervical cancer types, squamous carcinoma differentiation, and lymph node involvement. Meta-regression was employed to investigate heterogeneity. ResultsThirteen studies involving 868 patients were included in the meta-analysis. For differentiating adenocarcinoma from squamous carcinoma, the pooled sensitivity was 0.82 (95%CI: 0.71-0.90), specificity was 0.65 (95%CI: 0.48-0.79), and DOR was 9 (95%CI: 1.6-3.5). When distinguishing poorly differentiated from moderately/well-differentiated squamous carcinoma, the sensitivity was 0.74 (95%CI: 0.66-0.81), specificity was 0.83 (95%CI: 0.75-0.89), and DOR was 14 (95%CI: 8-23). For identifying lymph node involvement, the sensitivity was 0.87 (95%CI: 0.78-0.92), specificity was 0.66 (95%CI: 0.59-0.73), and DOR was 13 (95%CI: 7-26). No publication bias was detected. ConclusionsAPT imaging demonstrates high sensitivity and specificity in distinguishing between cervical cancer types, grading squamous carcinoma, and detecting lymph node involvement. It can be considered a reliable technique for predicting the pathological features of cervical cancer in clinical practice.

Predictive value of radiomics for intracranial aneurysm rupture: a systematic review and meta-analysis.

To systematically review the literature on radiomics for predicting intracranial aneurysm rupture and conduct a meta-analysis to obtain evidence confirming the value of radiomics in this prediction. A systematic literature search was conducted in PubMed, Web of Science, Embase, and The Cochrane Library databases up to March 2024. The QUADAS-2 tool was used to assess study quality. Stata 15.0 and Review Manager 5.4.1 were used for statistical analysis. Outcomes included combined sensitivity (Sen), specificity (Spe), positive likelihood ratio (+LR), negative likelihood ratio (-LR), diagnostic odds ratio (DOR), and their 95% confidence intervals (CI), as well as pre-test and post-test probabilities. The SROC curve was plotted, and the area under the curve (AUC) was calculated. Publication bias and small-study effects were assessed using the Deeks' funnel plot. The 9 included studies reported 4,284 patients, with 1,411 patients with intracranial aneurysm rupture (prevalence 32.9%). The overall performance of radiomics for predicting intracranial aneurysm rupture showed a combined Sen of 0.78 (95% CI: 0.74-0.82), Spe of 0.74 (95% CI: 0.70-0.78), +LR of 3.0 (95% CI: 2.7-3.4), -LR of 0.29 (95% CI: 0.25-0.35), DOR of 10 (95% CI: 9-12), and AUC of 0.83 (95% CI: 0.79-0.86). Significant heterogeneity was observed in both Sen (I2 = 90.93, 95% CI: 89.00-92.87%) and Spe (I2 = 94.28, 95% CI: 93.21-95.34%). Radiomics can improve the diagnostic efficacy of intracranial aneurysm rupture. More large-sample, prospective, multicenter clinical studies are needed to further evaluate its predictive value. https://www.crd.york.ac.uk/PROSPERO/.

Use of muscular ultrasound to detect intensive care unit-acquired weakness: a systematic review and Meta-analysis.

This systematic review and meta-analysis aims to detecting performance of muscular ultrasound for intensive care unit (ICU)-acquired weakness (ICUAW). We searched PubMed, Web of Science, Embase, Cochrane library, CNKI, VIP, and Wanfang databases for articles published before July 2024. A random-effects model was utilized to derive the summary estimates of sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence interval (CI). Additionally, the sources of heterogeneity were explored by subgroup analysis and meta-regression. This meta-analysis comprised ten prospective studies involving 561 participants, of whom 241 (42.96%) were diagnosed as ICUAW. Overall, muscular ultrasound exhibited good performance for detecting ICUAW, with the area of SROC curve of 0.85 (95%CI 0.82-0.88), sensitivity of 0.76 (95%CI 0.70-0.81), specificity of 0.80 (95%CI 0.74-0.84), and DOR of 12.43 (95%CI 7.98-19.38). Upon pre-defined subgroup analysis, the rectus femoris exhibited significantly superior discriminatory ability in identifying ICUAW than the non-rectus femoris, with higher SROC (0.88 [95%CI 0.85-0.91] vs. 0.76 [95%CI 0.72-0.79], p < 0.01). Moreover, cross-sectional area was more effective than thickness, with higher specificity (0.86 [95%CI 0.80-0.91] vs. 0.74 [95%CI 0.68-0.79], p = 0.02) and SROC (0.89 [95%CI 0.86-0.92] vs. 0.76 [95%CI 0.72-0.80], p < 0.01). Furthermore, integrated analysis of these two indicators revealed that the cross-sectional area of rectus femoris was statistically superior to the thickness of rectus femoris, with higher sensitivity (0.82 [95%CI 0.74-0.87] vs. 0.75 [95%CI 0.65-0.83], p < 0.05) and AUC (0.91 [95%CI 0.88-0.93] vs. 0.80 [95%CI 0.76-0.83], p < 0.01). Muscular ultrasound could be a reliable tool for ICUAW detection. Compared with alternative indices, the cross-sectional area of the rectus femoris exhibits superior detection efficacy and may be considered as a valuable parameter for clinical application.

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 serological diagnostic value of EBV-related IgA antibody panels for nasopharyngeal carcinoma: a diagnostic test accuracy meta-analysis

BackgroundNasopharyngeal carcinoma (NPC) is diagnosed relatively late and has a poor prognosis, requiring early detection to reduce the disease burden. This diagnostic test accuracy meta-analysis evaluated the serological diagnostic value of nine EBV-related IgA antibody panels (EBNA1-IgA, VCA-IgA, EA-IgA, Zta-IgA, EBNA1-IgA + VCA-IgA, VCA-IgA + EA-IgA, VCA-IgA + Rta-IgG, EBNA1-IgA + VCA-IgA + Zta-IgA and VCA-IgA + EA-IgA + Rta-IgG), aiming to identify suitable serological detection biomarkers for NPC screening.MethodsPubMed, Embase, China National Knowledge Infrastructure and Chinese BioMedical Literature Database were searched from January 1st, 2000 to September 30th, 2023, with keywords nasopharyngeal carcinoma, IgA, screening, early detection, early diagnosis, sensitivity and specificity. Articles on the diagnostic value of serum EBV-related IgA antibody panels for NPC were included. Study selection, data extraction, and quality assessment were performed independently by two researchers, and a third researcher was consulted in the case of disagreement. Bivariate models were used for statistical analysis. The quality of included studies was evaluated through Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2).ResultsA total of 70 articles were included, involving 11 863 NPC cases and 34 995 controls. Among the nine EBV-related IgA antibody panels, EBNA1-IgA + VCA-IgA [0.928 (0.898, 0.950)], VCA-IgA + Rta-IgG [0.925 (0.890, 0.949)], EBNA1-IgA + VCA-IgA + Zta-IgA [0.962 (0.909, 0.985)] and VCA-IgA + EA-IgA + Rta-IgG [0.945 (0.918, 0.964)] demonstrated higher pooled sensitivity (95%CI). In terms of diagnostic odds ratio (DOR) (95%CI), EBNA1-IgA + VCA-IgA [107.647 (61.173, 189.430)], VCA-IgA + Rta-IgG [105.988 (60.118, 186.857)] and EBNA1-IgA + VCA-IgA + Zta-IgA [344.450 (136.351, 870.153)] showed superior performance. Additionally, the SROC curves for EBNA1-IgA + VCA-IgA and VCA-IgA + Rta-IgG were more favorable. However, publication bias was detected for VCA-IgA (P = 0.005) and EBNA1-IgA + VCA-IgA (P = 0.042).ConclusionsIn general, parallel detection of serum EBNA1-IgA, VCA-IgA and Zta-IgA antibodies using ELISA demonstrates better pooled sensitivity and DOR among the studied panels. In the cases where fewer indicators are used, serum VCA-IgA and EBNA1-IgA/Rta-IgG antibody panel exhibits a comparable performance.Trial registrationThe International Prospective Register of Systematic Reviews registration number: CRD42023426984, registered on May 28, 2023.

The association between salivary pepsin and gastroesophageal reflux disease: A meta-analysis.

The definitive diagnosis of gastroesophageal reflux disease (GERD) often requires invasive investigations like upper gastrointestinal endoscopy or reflux monitoring. We aimed to explore the relationship between salivary pepsin and GERD and its value as a non-invasive diagnostic tool. Databases (PubMed, Web of Science, Cochran Library, and EMBASE) were searched from their inception to January 22, 2024 to explore the correlation of salivary pepsin with GERD. The meta-analysis data retrieved were summarized, including the salivary pepsin concentration, sensitivity of diagnosis (SEN), specificity of diagnosis (SPE), negative likelihood ratio, positive likelihood ratio, diagnostic odds ratio, and receiver operating characteristic (ROC) curve. The meta-analysis comparing salivary pepsin concentration in two groups (proven GERD and non-GERD) with 18 studies revealed that the proven GERD group had higher salivary pepsin concentration than the non-GERD group (SMD = 1.74 [95% CI 1.14-2.34]). The meta-analysis of salivary pepsin diagnostic value for proven GERD incorporated 23 studies. The results showed pooled SEN (0.73 [95% CI 0.66-0.80]), SPE (0.72 [95% CI 0.65-0.78]), positive likelihood ratio (2.61 [95% CI 2.02-3.39]), negative likelihood ratio (0.37 [95% CI 0.28-0.50]), diagnostic odds ratio (7.03 [95% CI 4.24-11.66]) and area under the SROC curve (0.79 [95% CI 0.75-0.82]). GERD patients presented a higher salivary pepsin concentration. Salivary pepsin is both sensitive and specific in identifying GERD, making it a promising non-invasive marker for diagnosis.

Systematic review and meta-analysis of diagnostic test accuracy in chronic wound's microbiology.

This study aims to assess the diagnostic accuracy of non-culture-based methodologies for detecting microorganisms in chronic wounds. We systematically reviewed studies that evaluated the diagnostic accuracy of alternative tests in chronic wound samples, excluding studies focused on animal samples or unrelated conditions. The search encompassed PubMed, CINAHL, Scopus and Web of Science databases, employing the QUADAS-2 tool for risk of bias assessment. Our search included the PubMed, CINAHL, Scopus and Web of Science databases, and we assessed the risk of bias using the QUADAS-2 tool. A meta-analysis was conducted on polymerase chain reaction (PCR) and colorimetric methods to determine sensitivity, specificity, diagnostic odds ratio, and summary receiver-operating characteristic (sROC) curves using a random-effects model. For methods not suitable for quantitative synthesis, a narrative synthesis was performed. Nineteen studies involving various types of chronic wounds were analysed, revealing diverse diagnostic methods including fluorescence, PCR, colorimetry, voltammetry, electronic nose, biosensors, enzymatic methods, staining and microscopy. Combining fluorescence with clinical signs and symptoms (CSS) versus culture showed significant accuracy. Colorimetry demonstrated low sensitivity but high specificity, with a diagnostic odds ratio of 6.3. PCR generally exhibited good accuracy, although significant heterogeneity was noted, even in subgroup analyses. This study identified a broad spectrum of diagnostic approaches, highlighting the superior diagnostic accuracy achieved when microbiological analysis is combined with clinical assessments. However, the heterogeneity and methodological variations across studies present challenges in meta-analysis. Future research should aim for standardized and homogeneous study designs to enhance the assessment of diagnostic accuracy for alternative methods.

Identification of Biomarkers for Endometrial Cancer Based on Natural Killer Cell-Related Genes

Endometrial cancer (EC) is a malignant tumor. Natural killer (NK) cells play a crucial role in various cancers, but their role in EC is unclear. To this purpose, in this paper, differential expression analysis was performed on transcriptome data from the TCGA database, and the obtained DEGs and the collected NRGs were intersected, and single-factor Cox regression analysis and Lasso-Cox regression analysis were performed on the intersected genes to obtain prognosis-related genes and risk model, respectively. These genes and models were validated by Kaplan-Meier (KM) survival curve analysis and ROC analysis on the internal and external test sets. In addition, nomogram models were constructed based on prognosis-associated genes, sample risk scores, and clinical factors. Finally, we explored the immune landscape of high- and low-risk groups of EC. The results showed that the risk models constructed in this paper exhibited excellent predictive effects, which will facilitate research on the precision treatment of EC. There were significant differences in prognosis, immune cell infiltration abundance, immune checkpoint-associated genes, and HLA gene expression between high- and low-risk groups of EC. The risk model in this paper can provide a reference for the personalized treatment of EC. In addition, we performed RT-qPCR to validate the levels of genes significantly associated with prognosis.

Gulf War Illness Is Associated with Host Gut Microbiome Dysbiosis and Is Linked to Altered Species Abundance in Veterans from the BBRAIN Cohort.

Gulf War Illness (GWI) is a debilitating condition marked by chronic fatigue, cognitive problems, pain, and gastrointestinal (GI) complaints in veterans who were deployed to the 1990-1991 Gulf War. Fatigue, GI complaints, and other chronic symptoms continue to persist more than 30 years post-deployment. Several potential mechanisms for the persistent illness have been identified and our prior pilot study linked an altered gut microbiome with the disorder. This study further validates and builds on our prior preliminary findings of host gut microbiome dysbiosis in veterans with GWI. Using stool samples and Multidimensional Fatigue Inventory (MFI) data from 89 GW veteran participants (63 GWI cases and 26 controls) from the Boston biorepository, recruitment, and integrative network (BBRAIN) for Gulf War Illness, we found that the host gut bacterial signature of veterans with GWI showed significantly different Bray-Curtis beta diversity than control veterans. Specifically, a higher Firmicutes to Bacteroidetes ratio, decrease in Akkermansia sp., Bacteroides thetaiotamicron, Bacteroides fragilis, and Lachnospiraceae genera and increase in Blautia, Streptococcus, Klebsiella, and Clostridium genera, that are associated with gut, immune, and brain health, were shown. Further, using MaAsLin and Boruta algorithms, Coprococcus and Eisenbergiella were identified as important predictors of GWI with an area under the curve ROC predictive value of 74.8%. Higher self-reported MFI scores in veterans with GWI were also significantly associated with an altered gut bacterial diversity and species abundance of Lachnospiraceae and Blautia. These results suggest potential therapeutic targets for veterans with GWI that target the gut microbiome and specific symptoms of the illness.

68Ga-PSMA PET/CT and 18F-FDG PET/CT in the diagnosis of prostatic ductal cancer

PurposesTo explore the characteristics of PSMA PET/CT and FDG PET/CT images in prostatic ductal adenocarcinoma (DA) patients. MethodsWe retrospectively enrolled prostatic DA patients with PET/CT scans at Tongji Hospital from 2018 to 2022. Patients with prostatic acinar adenocarcinoma (AA) and benign pathology (BP) were enrolled by 1:1 matching. Differences in the uptake of primary and metastatic foci on PET among the groups were analyzed. ResultsA total of 42 patients were enrolled: 14 in each group. In primary foci, the mean PSMA uptake in the DA group was lower than that in the AA group (14.2 ± 9.6 vs. 27.1 ± 14.3, P = 0.009) and greater than that in the BP group (14.2 ± 9.6 vs. 4.7 ± 1.3, P = 0.003). The AUCs of the DA-AA ROC curve and DA-BP ROC curve were 0.781 and 0.872, respectively. The median PSMA uptake of metastatic lymph nodes in the DA group was lower than that in the AA group (5.6 vs. 14.2, P = 0.033), with no significant difference in metastatic bone lesions (9.5 vs 19.1, P = 0.485). No significant difference was found in the FDG uptake of primary and metastatic foci between the DA and AA groups (P > 0.05). ConclusionProstatic DA has greater PSMA uptake than BP diseases, but lower uptake in both primary foci and metastatic lymph nodes than AA on PSMA PET/CT, aiding in the differential diagnosis of DA, AA and BP diseases. Clinicians should combine traditional imaging with PSMA PET/CT to avoid underestimating the clinical stage of DA patients.

Diagnostic accuracy of neutrophil lymphocyte ratio and platelet lymphocyte ratio for IVIG-resistance Kawasaki disease: an updated meta-analysis

&lt;p style="text-align: justify;"&gt;Background: Neutrophil lymphocyte ratio (NLR) and platelet lymphocyte ratio (PLR) are biomarkers for the diagnosis and prognosis of many diseases. However, the diagnostic accuracy of IVIG resistant Kawasaki disease is still unclear. This study aims to evaluate the diagnostic value of NLR and PLR in IVIG resistant Kawasaki disease. Method: We used Pubmed, Embase, Web of science, CNKI and Google academic database to find all articles that met the inclusion criteria until January 5 2024.Result: After two independent researchers carefully read the title, abstract, and full text, five eligible studies were included in the study analysis, with a total of 3359 children included. The research results showed that the combined sensitivity and specificity were 0.57 (95% CI: 0.48-0.66) and 0.73 (95% CI: 0.67-0.78), respectively; The pooled positive likelihood ratio and Negative likelihood ratio were2.12（95%CI:1.91-2.34）and 0.58（95%CI:0.50-0.68）, respectively;The pooled DOR was 3.62（95% CI:2.99-4.39）;The area under the SROC curve (AUC) was 0.71 (95% CI: 0.67-0.75), indicating high diagnostic performance. Conclusion: This study analysis indicates that the PLR+NLR levels in children with Kawasaki disease can serve as an important parameter in the risk scoring system, with relatively high accuracy.&lt;/p&gt;

Diagnostic value of combined ultrasound contrast and elastography for differentiating benign and malignant thyroid nodules: a meta-analysis

The diagnostic value of contrast-enhanced ultrasound combined with ultrasound elastography for benign and malignant thyroid nodules is still controversial, so we used meta-analysis to seek controversial answers. The PubMed, OVID, and CNKI databases were searched according to the inclusion and exclusion criteria. The literature was selected from the establishment of each database to February 2024. The QUADAS-2 tool assessed diagnostic test accuracy. SROC curves and Spearman's correlation coefficient were made by Review Manager 5.4 software to assess the presence of threshold effects in the literature. Meta-Disc1.4 software was used for Cochrane-Q and χ2 tests, which be used to evaluate heterogeneity, with P-values and I2 indicating heterogeneity levels. The appropriate effect model was selected based on the results of the heterogeneity test. Stata18.0 software was used to evaluate publication bias. The diagnostic accuracy of contrast-enhanced ultrasound combined with ultrasound elastography for benign and malignant thyroid nodules was evaluated by calculating the combined sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, DOR, and area under the SROC curve. A total of 31 studies included 3811 patients with 4718 nodules were analyzed. There is no heterogeneity caused by the threshold effect, but there is significant non-threshold heterogeneity. Combined diagnostic metrics were: sensitivity = 0.93, specificity = 0.91, DOR = 168.41, positive likelihood ratio = 10.60, and negative likelihood ratio = 0.07. The SROC curve area was 0.97. Contrast-enhanced ultrasound and elastography show high diagnostic accuracy for thyroid nodules, offering a solid foundation for early diagnosis and treatment.Trial registration. CRD42024509462.

Preoperative prediction power of radiomics and non-radiomics methods based on MRI for early recurrence in hepatocellular carcinoma: a systemic review and meta-analysis.

To compare radiomics and non-radiomics in predicting early recurrence (ER) in patients with hepatocellular carcinoma (HCC) after curative surgery. We systematically searched PubMed and Embase databases. Studies with clear reference criteria were selected. Data were extracted and assessed for quality using the quality in prognosis studies tool (QUIPS)by two independent authors. All included radiomics studies underwent radiomics quality score (RQS) assessment. We calculated sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) using random or fixed models with a 95%CI. Forest maps visualized the data, and summary receiver operating characteristic (sROC) curves with the area under the curve (AUC) were generated. Meta-regression and subgroup analyses explored sources of heterogeneity. We compared sensitivity, specificity, PLR, and NLR using the z-test and compared AUC values using the Delong test. Our meta-analysis included 10 studies comprising 1857 patients. For radiomics, the pooled sensitivity, specificity, AUC of sROC, PLR and NLR were 0.84(95%CI: 0.78-0.89), 0.80(95%CI: 0.75-0.85), 0.89(95%CI: 0.86-0.91), 4.28(95%CI: 3.48-5.27) and 0.20(95%CI: 0.14-0.27), respectively, but with significant heterogeneity (I2 = 60.78% for sensitivity, I2 = 55.79% for specificity) and potential publication bias (P = 0.04). The pooled sensitivity, specificity, AUC of sROC, PLR, NLR for non-radiomics were 0.75(95%CI:0.68-0.81), 0.78(95%CI:0.72-0.83), 0.83(95%CI: 0.80-0.86), 3.45(95%CI: 2.68-4.44) and 0.32(95%CI: 0.24-0.41), respectively. There was no significant heterogeneity in this group (I2 = 0% for sensitivity, I2 = 17.27% for specificity). Radiomics showed higher diagnostic accuracy (AUC: 0.89 vs. 0.83, P = 0.0456), higher sensitivity (0.84 vs. 0.75, P = 0.0385) and lower NLR (0.20 vs. 0.32, P = 0.0287). The radiomics from preoperative MRI effectively predicts ER of HCC and has higher diagnostic accuracy than non-radiomics. Due to potential publication bias and suboptimal RQS scores in radiomics, these results should be interpreted cautiously.

Value of Dynamic Computed Tomography Myocardial Perfusion in CAD: A Systematic Review and Meta-analysis.

Dynamic stress computed tomography (CT) perfusion is a non-invasive method for quantifying myocardial ischemia by assessing myocardial blood flow (MBF). In this meta-analysis, we evaluated the diagnostic accuracy of dynamic CT perfusion for the detection of significant coronary artery disease (CAD) across various CT scanners, obese patients, and its prognostic value. We systematically searched PubMed, Embase, Web of Science, and Cochrane library for published studies evaluating the accuracy of CT myocardial perfusion in diagnosing functional significant ischemia by invasive fractional flow reserve. The diagnostic performance of dynamic CT perfusion in detecting ischemia was evaluated using a summary receiver operating characteristic (sROC) curve. A total of 23 studies underwent meta- analysis. In myocardial region without ischemia, MBF was measured at 1.44 ml/min/g (95% confidence interval [CI]: 1.13-1.75), while in region with ischemia, it was 0.94 ml/min/g (95% CI: 0.80-1.08) (p<0.001). On the patient-based analysis, the area under the sROC curve of CT-MBF was 0.93, with a sensitivity of 0.84 and specificity of 0.88. Differences in CT type (dual source vs. single source), and body mass index (BMI) did not significantly affect the diagnostic performance. The pooled hazard ratio of dynamic CT perfusion for predicting adverse events was 4.98 (95%CI: 2.08-11.93, p=<0.001, I2=61%, p for heterogeneity = 0.07). Dynamic CT perfusion has high diagnostic performance in the quantitative assessment of ischemia and detection of functional myocardial ischemia as defined by invasive FFR, and may be useful in risk stratification of CAD patients.

Comprehensive analysis and validation reveal DEPDC1 as a potential diagnostic biomarker associated with tumor immunity in non-small-cell lung cancer.

Current evidence suggests that DEP domain containing 1 (DEPDC1) has an important effect on non-small-cell lung cancer (NSCLC). However, the diagnostic value and the regulatory function within NSCLC are largely unclear. This work utilized publicly available databases and in vitro experiments for exploring, DEPDC1 expression, clinical features, diagnostic significance and latent molecular mechanism within NSCLC. According to our results, DEPDC1 was remarkably upregulated in the tissues of NSCLC patients compared with non-carcinoma tissues, linked with gender, stage, T classification and N classification based on TCGA data and associated with smoking status and stage according to GEO datasets. Meanwhile, the summary receiver operating characteristic (sROC) curve analysis result showed that DEPDC1 had a high diagnostic value in NSCLC (AUC = 0.96, 95% CI: 0.94-0.98; diagnostic odds ratio = 99.08, 95%CI: 31.91-307.65; sensitivity = 0.89, 95%CI: 0.81-0.94; specificity = 0.92, 95%CI: 0.86-0.96; positive predictive value = 0.94, 95%CI: 0.89-0.98; negative predictive value = 0.78, 95%CI: 0.67-0.90; positive likelihood ratio = 11.77, 95%CI: 6.11-22.68; and negative likelihood ratio = 0.12, 95%CI: 0.06-0.22). Subsequently, quantitative real-time PCR (qRT-PCR) and western blotting indicated that DEPDC1 was high expressed in NSCLC cells. According to the in vitro MTS and apoptotic assays, downregulated DEPDC1 expression targeting P53 signaling pathway inhibited the proliferation of NSCLC cells while promoting apoptosis of NSCLC cells. Moreover, DEPDC1 was significantly correlated with immune cell infiltrating levels in NSCLC based on TCGA data, which were primarily associated with T cells CD4 memory activated, macrophages M1, B cells memory, mast cells resting, T cells regulatory, monocytes, and T cells CD4 memory resting. Compared with the group with high expression of DEPDC1, the group with low expression level had higher scores for immune checkpoint inhibitors (ICIs) treatment. GSEA confirmed that DEPDC1 was involved in gene expression and tumor-related signaling pathways. Finally, DEPDC1 and its associated immune-related genes were shown to be enriched in 'receptor ligand activity', 'external side of plasma membrane', 'regulation of innate immune response', and 'Epstein-Barr virus infection' pathways. The present study demonstrates that DEPDC1 may contribute to NSCLC tumorigenesis and can be applied as the biomarker for diagnosis and immunology.

The predictive value of confusion assessment method-intensive care unit and intensive care delirium screening checklist for delirium in critically ill patients in the intensive care unit: A systematic review and meta-analysis.

Approximately 16%-89% of patients developed delirium during hospitalization in the intensive care unit (ICU). Studies on the accuracy and clinical application of ICU delirium screening tools exist, but the results are inconsistent. Moreover, the accuracy of different screening tools varied greatly. To compare the diagnostic accuracy of Confusion Assessment Method-Intensive Care Unit (CAM-ICU) and Intensive Care Delirium Screening Checklist (ICDSC) for delirium screening in critically ill patients in the ICU. We searched PubMed, Embase, Cochrane Central Register of Controlled Trials, Medline, and SciELO databases for relevant studies by combining relevant medical subject headings (MeSH) and keywords. Each database was searched from its creation to 30 January 2024. The included literature was screened by title, abstract, and full text. The diagnostic studies were summarized using Stata 14.0 software. SEN, SPE, PLR, NLR, DOR, and 95% confidence interval (CI) of the diagnostic studies were combined, the SROC analysis was performed, and the area under curve was estimated. Thirty-two articles from the database met the inclusion criteria. The number of studies on CAM-ICU and ICDSC was 28 and 14, respectively. For CAM-ICU, the pooled sensitivity and specificity were 0.81 (95% CI: 0.81-0.81) and 0.94 (95% CI: 0.94-0.94), and the hierarchical SROC curve was 0.96 (95% CI: 0.93-0.97). Regarding the ICDSC, The pooled sensitivity and specificity were 0.79 (95% CI: 0.68-0.86) and 0.90 (95% CI: 0.84-0.93), and the hierarchical SROC curve was 0.92 (95% CI: 0.89-0.94). Regarding the likelihood ratio, the CAM-ICU has a high PLR of 14.24 (95% CI: 14.24-14.24) and a low NLR of 0.20 (95% CI: 0.20-0.20). The ICDSC has a low PLR of 7.64 (95% CI: 5.37-10.87) and a high NLR of 0.24 (95% CI: 0.16-0.35). CAM-ICU showed good performance in terms of screening and diagnostic efficacies for delirium in critically ill patients. In view of the diagnostic accuracy of these two tools in delirium assessment, the strategies on how to increase their implementation in delirium screening among ICU patients are the focus of future research. CAM-ICU is recommended as the first choice to evaluate delirium in clinical practice, followed by ICDSC. Future studies can explore the predictive value of CAM-ICU and ICDSC in different special populations and different types of delirium.

Orbital CT scan parameters in dysthyroid optic neuropathy: a systematic review and meta-analysis.

Addressing Dysthyroid Optic Neuropathy (DON) is crucial due to its debilitating impact in thyroid eye disease (TED). Prompt treatment can preserve vision. Despite lacking definitive diagnostic criteria, computed tomography's (CT) parameters are commonly used for diagnosis. However, these parameters exist without consensus on their diagnostic performance. Systematic review and meta-analysis. We conducted a meta-analysis of studies assessing orbital CT diagnostic performance for DON in adults with TED. We searched various databases including Medline, PubMed, Scopus, and EMBASE, and others electronic databases, until July 2023. Evaluated CT parameters includes Barrett index (BI), fat prolapse via superior-orbital-fissure (SOF), superior-ophthalmic-vein-dilatation (SOVD), and the Nugent score. Diagnostic Test Accuracy analysis (DTA) was performed using R. A total of 9 articles with documented target parameters, collectively analysed 212 orbits with DON. Nugent score exhibited highest diagnostic ability with a log diagnostic odd ratio (logDOR) of 2.64 (95% CI, 2.02, 3.25). Another significant DON indicator was a BI ≥ 50%, with a logDOR of 1.97 (95% CI, 1.17; 2.77). Conversely, fat prolapse via SOF and SOVD proved less sensitive, with a logDOR of 1.42 and 1.09 respectively. Regarding the SROC curve, Nugent score and the BI have the greatest AUC. Variations in study locale, participant demographics, and measurement methods accounted for heterogeneity in meta-analysis. Nugent score and a BI ≥ 50% prove to be significant diagnostic parameters for DON, distinguishing them from fat prolapse via SOF and SOVD. Prioritizing these parameters can lead to prompt treatment and thus enhanced visual outcomes. CRD42023446376.

Accuracy of respiratory muscle assessments to predict weaning outcomes: a systematic review and comparative meta-analysis

BackgroundSeveral bedside assessments are used to evaluate respiratory muscle function and to predict weaning from mechanical ventilation in patients on the intensive care unit. It remains unclear which assessments perform best in predicting weaning success. The primary aim of this systematic review and meta-analysis was to summarize and compare the accuracy of the following assessments to predict weaning success: maximal inspiratory (PImax) and expiratory pressures, diaphragm thickening fraction and excursion (DTF and DE), end-expiratory (Tdiee) and end-inspiratory (Tdiei) diaphragm thickness, airway occlusion pressure (P0.1), electrical activity of respiratory muscles, and volitional and non-volitional assessments of transdiaphragmatic and airway opening pressures.MethodsMedline (via Pubmed), EMBASE, Web of Science, Cochrane Library and CINAHL were comprehensively searched from inception to 04/05/2023. Studies including adult mechanically ventilated patients reporting data on predictive accuracy were included. Hierarchical summary receiver operating characteristic (HSROC) models were used to estimate the SROC curves of each assessment method. Meta-regression was used to compare SROC curves. Sensitivity analyses were conducted by excluding studies with high risk of bias, as assessed with QUADAS-2. Direct comparisons were performed using studies comparing each pair of assessments within the same sample of patients.ResultsNinety-four studies were identified of which 88 studies (n = 6296) reporting on either PImax, DTF, DE, Tdiee, Tdiei and P0.1 were included in the meta-analyses. The sensitivity to predict weaning success was 63% (95% CI 47–77%) for PImax, 75% (95% CI 67–82%) for DE, 77% (95% CI 61–87%) for DTF, 74% (95% CI 40–93%) for P0.1, 69% (95% CI 13–97%) for Tdiei, 37% (95% CI 13–70%) for Tdiee, at fixed 80% specificity. Accuracy of DE and DTF to predict weaning success was significantly higher when compared to PImax (p = 0.04 and p < 0.01, respectively). Sensitivity and direct comparisons analyses showed that the accuracy of DTF to predict weaning success was significantly higher when compared to DE (p < 0.01).ConclusionsDTF and DE are superior to PImax and DTF seems to have the highest accuracy among all included respiratory muscle assessments for predicting weaning success. Further studies aiming at identifying the optimal threshold of DTF to predict weaning success are warranted.Trial registration: PROSPERO CRD42020209295, October 15, 2020.