CT-Based Radiomics and Deep Learning for Preoperative Thyroid Nodule Classification: A Systematic Review, Meta-analysis, and Radiologist Comparison.

To assess the diagnostic value of serum interleukin-8 (IL-8) in the detection of colorectal cancer (CRC). An original study was conducted to explore the potential value of IL-8 in CRC diagnosis. Receiver operating characteristic (ROC) analysis was performed and the area under the curve (AUC) value was calculated. PUBMED and EMBASE were searched (to October, 2013), supplemented with manual screening for relevant publications. Meta-analysis methods were applied to pool sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratios and to construct a summary receiver-operating characteristic (sROC) curve. Heterogeneity across studies was checked by the I(2) test and Deek's funnel plot method was applied to test publication bias. In our original study, serum IL-8 yielded an AUC of 0.742 [95%CI: 0.635-0.849]. The sensitivity and specificity were 85.42% and 54.05%, respectively, at a cut-off value of 5.39. In this meta-analysis, we included five studies with 668 CRC patients and 374 controls and one study in our own center with 48 CRC patients and 37 controls. The pooled sensitivity and specificity of IL-8 were 0.69 (95%CI: 0.42-0.87) and 0.85 (95%CI: 0.68-0.94) for CRC detection. Besides, the area under the sROC curve was 0.86 (95%CI: 0.82-0.88). Subgroup analysis suggested that there was no heterogeneity in the Asian subgroup but some in the European subgroup. In addition, no publication bias was found according to the Deek's funnel plot asymmetry test. Serum IL-8 is a promising biomarker for CRC detection and may become a clinically useful tool to identify high-risk patients.

Backgrounds and Purpose: Currently, circulating microRNAs (miRNAs) are considered to be non-invasive diagnostic biomarkers in a broad range of tumors. Nevertheless, so far, miRNAs have not been fully applied to the clinic for routine screening in glioma patients. Thus, our goal is to evaluate the diagnostic performance of circulating miRNAs for gliomas via a meta-analysis. The present study is registered on the PROSPERO website, with the number CRD42020195883.Methods: Literature retrieval was implemented in the PubMed, Embase, and Web of Science databases using the established search strategy. We pooled the sensitivity, specificity, and its 95% confidence intervals (CIs) for the included studies using the Stata 14.0 software. In addition, the heterogeneity between studies was assessed via the Q statistics and I2 values calculated by a Chi-square test. A bivariate random effects model was selected due to significant heterogeneity. Specifically, for exploring the factors influencing the heterogeneity, we implemented subgroup and meta-regression analyses. Ultimately, a Deek's funnel plot asymmetry test was used to estimate the potential publication bias.Results: A total of 18 articles covering 24 studies were included, containing 2,170 glioma patients and 1,456 healthy participants. The overall pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were 0.84 (95%CI: 0.79–0.87), 0.84 (95%CI: 0.80–0.88), 5.3 (95%CI: 4.1–6.8), 0.19 (95%CI: 0.15–0.25), 27 (95%CI: 18–41), and 0.91 (95%CI: 0.88–0.93), respectively. Additionally, the findings revealed that serum miRNAs and miRNA panels presented superior diagnostic performance.Conclusion: Thus, circulating miRNAs have the potential to serve as diagnostic biomarkers for gliomas, but need to be verified via a large pool of prospective studies. Additionally, specific miRNAs still need to be elucidated in the diagnosis of a glioma, especially in the early screening stage. The findings may provide diagnostic and therapeutic strategies for the glioma population.

Diagnostic accuracy of HNF1β, Napsin A and P504S/Alpha-Methylacyl-CoA Racemase (AMACR) as markers of endometrial clear cell carcinoma

The present study aims to construct a random forest (RF) model based on ultrasound radiomics and elastography, offering a new approach for the differentiation of thyroid nodules (TNs). We retrospectively analyzed 152 TNs from 127 patients and developed four machine learning models. The examination was performed using the Resona 9Pro equipped with a 15-4 MHz linear array probe. The region of interest (ROI) was delineated with 3D Slicer. Using the RF algorithm, four models were developed based on sound touch elastography (STE) parameters, strain elastography (SE) parameters, and the selected radiomic features: the STE model, SE model, radiomics model, and the combined model. Decision Curve Analysis (DCA) is employed to assess the clinical benefit of each model. The DeLong test is used to determine whether the area under the curves (AUC) values of different models are statistically significant. A total of 1396 radiomic features were extracted using the Pyradiomics package. After screening, a total of 7 radiomic features were ultimately included in the construction of the model. In STE, SE, radiomics model, and combined model, the AUCs are 0.699 (95% CI: 0.570-0.828), 0.812 (95% CI: 0.683-0.941), 0.851 (95% CI: 0.739-0.964) and 0.911 (95% CI: 0.806-1.000), respectively. In these models, the combined model and the radiomics model exhibited outstanding performance. The combined model, integrating elastography and radiomics, demonstrates superior predictive accuracy compared to single models, offering a promising approach for the diagnosis of TNs.

Radiomics methods are increasingly used to identify benign and malignant lung nodules, and early monitoring is essential in prognosis and treatment strategy formulation. To evaluate the diagnostic performance of computed tomography (CT)-based radiomics for distinguishing between benign and malignant lung nodules by performing a meta-analysis. Between January 2000 and December 2021, we searched the PubMed and Embase electronic databases for studies in English. Studies were included if they demonstrated the sensitivity and specificity of CT-based radiomics for diagnosing benign and malignant lung nodules. The studies were evaluated using the QUADAS-2 and radiomics quality scores (RQS). The inhomogeneity of the data and publishing bias were also evaluated. Some subgroup analyses were performed to investigate the impact of diagnostic efficiency. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Guidelines were followed for this meta-analysis. A total of 20 studies involving 3793 patients were included. The combined sensitivity, specificity, diagnostic odds ratio, and area under the summary receiver operating characteristic curve based on CT radiomics diagnosis of benign and malignant lung nodules were 0.81, 0.86, 27.00, and 0.91, respectively. Deek's funnel plot asymmetry test confirmed no significant publication bias in all studies. Fagan nomograms showed a 40% increase in post-test probability among pretest-positive patients. Current evidence shows that CT-based radiomics has high accuracy in the diagnosis of benign and malignant lung nodules.

Objective To evaluate the diagnostic accuracy of miniprobe endoscopic ultrasonography(EUS)in assessment of tumor invasion depth in early gastric cancer. Methods We searched on the PubMed, Cochrane Library, Embase, CNKI, WanFang data, VIP data for relevant studies published.Stata 14.2 software was used to perform the Meta-analysis for sensitivity, specificity, positive likelihood ratio(LR), and negative LR, diagnostic odds ratio and 95% CI.A summary receiver-operating characteristic(SROC)curve was constructed, the area under the curve(AUC)was calculated, and the diagnostic value was evaluated.Furthermore, to explore the potential sources of heterogeneity, we used meta-regression to estimate the effect of the characteristics of the studies on the diagnostic accuracy of miniprobe EUS, and a subgroup analysis was performed.We assessed the publication bias using Deeks′Funnel plot. Results Sixteen studies with a total of 3 168 tumors were included in the analysis.The pooled sensitivity, specificity, positive likelihood ratio(LR), negative LR, diagnostic odds ratio and 95% CI and area under curve(AUC)were 0.85(95% CI: 0.78-0.90), 0.73(95% CI: 0.65-0.80), 3.2(95% CI: 2.4-4.1), 0.20(95% CI: 0.14-0.30), 16(95% CI: 9-26). The AUC of SROC curve was 0.85.No significant publication bias was found with Deeks′Funnel plot. Conclusion Miniprobe EUS can not accurately diagnose or exclude the M/SM1 layer invasion in early gastric cancer, but it still can greatly improve the diagnostic accuracy of invasion depth in early gastric cancer. Key words: Miniprobe endoscopic ultrasonography; Early gastric cancer; Invasion depth; Meta-analysis

We aimed to design a radiomics model for differential diagnosis of thyroid carcinoma based on dynamic ultrasound video, and compare its diagnostic performance with that of radiomics model based on static ultrasound images. Between January 2019 and May 2021, 890 patients with 1015 thyroid nodules (775 for training, 240 for validation) were prospectively enrolled. In total 890 patients underwent thyroidectomy within 1 month, and ultrasound dynamic video and static images were both acquired. Two deep learning (DL) models, namely DL-video and DL-image models, were proposed to diagnose thyroid nodules by analyzing ultrasound video and static images respectively. The performance of models was assessed by areas under the receiver operating characteristic curve (AUC). The DL model on ultrasound cines was re-visualized to help radiologists understand its potential working mechanism. The AUC of DL-video were 0.947 (95% CI: 0.931-0.963) and 0.923 (95% CI: 0.892-0.955) in training and validation cohorts, respectively. For DL-image model, the AUC were 0.928 (95% CI: 0.910-0.945) and 0.864 (95% CI: 0.819-0.910), respectively. The diagnosis performance of the DL-video was superior to that of DL-image, and there was significant difference between the AUC of DL-video and DL-image model in validation cohort (P=.028). The visualization demonstrated certain important ultrasound features that could be recognized by human eyes. The proposed DL radiomics model based on dynamic ultrasound video can accurately and individually classified thyroid nodules. The constructed DL-video model combining ultrasound video holds good potential for benefiting the management of patients with thyroid nodules.

CT-based radiomics predicts CD38 expression and indirectly reflects clinical prognosis in epithelial ovarian cancer

We evaluate the diagnostic value of bladder tumour antigen (BTA stat) tests compared with urine cytology test in detecting bladder cancer. We searched public databases including PubMed, MEDLINE Springer, Elsevier Science Direct, Cochrane Library and Google Scholar before December 2012. To collect relevant data of BTA stat tests and urine cytology tests in patients with bladder cancer, we studied meta-analyses of sensitivity, specificity, positive likelihood ratio (LR), negative LR and diagnostic odds ratios (DOR) of BTA stat tests and cytology tests from published studies. We applied the software of Rev. Man 5.1 and Stata 11.0 to the meta-analysis. A total of 13 separate studies consisting of 3462 patients with bladder cancer were considered in the meta-analysis. We found that the BTA stat test had a higher sensitivity than the urine cytology test (0.67, 95% confidence interval [CI] 0.64 to 0.69 vs. 0.43, 95% CI 0.40 to 0.46), but the specificity, positive LR, negative LR, DOR, the area under the curve (AUC) and Q index of the BTA stat test were lower compared with the urine cytology test. The results of the Egger's linear regression test showed no publication bias (p > 0.05). Specificity, positive LR, negative LR, DOR, the AUC and the Q index of the urine cytology test may be superior to the BTA stat test, but the BTA stat test has greater sensitivity than the urine cytology test.

Early fetal echocardiography is becoming increasing common during the nuchal translucency scan period. The aim of this meta-analysis was to assess the accuracy of first-trimester fetal echocardiography in diagnosing congenital heart defects (CHDs). The databases of PubMed, Embase, the Cochrane Library, and the Cumulative Index to Nursing and Allied Health Literature were systematically searched for the candidate articles, and the references of included studies were also examined. We recorded the characteristics of the included studies and assessed the quality of each study by the Quality Assessment of Diagnostic Accuracy Studies tool. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated with Meta-Disc version 1.4 software (Ramón y Cajal Hospital, Madrid, Spain). We also evaluated the publication bias by using Stata version 12.0 software (StataCorp, College Station, TX). This meta-analysis included 18 studies with 26,201 fetal hearts. The overall pooled sensitivity, specificity, PLR, and NLR were 0.750, 0.999, 392.95, and 0.277, respectively. The DOR and AUC were 1736.0 and 0.9331. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC for major CHDs were 0.838, 1.000, 725.69, 0.203, 5084.8, and 0.9617. First-trimester fetal echocardiography had high value in diagnosing CHDs, especially major CHDs.

Predictive value of T2-weighted imaging and dynamic contrast-enhanced MRI for assessing cervical invasion in patients with endometrial cancer: a meta-analysis

ObjectiveThe aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images.MethodsDatabases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated.ResultsOverall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95–0.96), 0.95 (95% CI, 0.94–0.95), 10.76 (95% CI, 7.33–15.79), 0.07 (95% CI, 0.04–0.11), and 173.93 (95% CI, 81.79–369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91–0.96), 0.90 (95% CI, 0.88–0.92), 6.14 (95% CI, 2.06–18.30), 0.07 (95% CI, 0.04–0.11), and 69.13 (95% CI, 14.73–324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87–0.88), 0.88 (95% CI, 0.87–0.88), 7.20 (95% CI, 4.32–12.00), 0.14 (95% CI, 0.09–0.23), and 48.77 (95% CI, 24.98–95.19), respectively.ConclusionsOn the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC.Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).

BackgroundSuperb microvascular imaging (SMI) is an advanced form of Doppler flow imaging which has advantages in tiny vessels and low-speed flow. This study aimed to evaluate the diagnostic performance of combining greyscale ultrasound (US) with SMI in differentiating between benign and malignant thyroid nodules.MethodsA search was conducted in PubMed, Embase, Cochrane Library, Scopus, and Web of Science for relevant studies published till 25 October 2023 that investigated the combined use of greyscale US and SMI to differentiate between benign and malignant thyroid nodules. A subgroup analysis was performed on the basis of different SMI diagnostic criteria for malignant thyroid nodules. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used for quality assessment. The summary sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−), diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curve were used for evaluation of diagnostic performance.ResultsA total of 10 original studies, encompassing 1,160 thyroid nodules, 556 of which were malignant, were included in the analysis. The area under the curve (AUC) of greyscale US combined with SMI in diagnosing malignant thyroid nodules was 0.92 [95% confidence interval (CI): 0.89–0.94]. The summary sensitivity and specificity were 0.90 (95% CI: 0.77–0.96) and 0.74 (95% CI: 0.54–0.88) for greyscale US combined with rich vascularity on SMI, 0.86 (95% CI: 0.76–0.92) and 0.83 (95% CI: 0.71–0.91) for greyscale US combined with vascular distribution on SMI, 0.87 (95% CI: 0.80–0.92) and 0.88 (95% CI: 0.81–0.93) for greyscale US combined with the penetrating vessel on SMI, respectively. Meta-regression analysis revealed that variations in sample size could be a source of heterogeneity.ConclusionsAlthough the SMI diagnostic criteria for malignant nodules varied among the studies, the combination of greyscale US with SMI demonstrates great diagnostic performance for the differentiation of benign and malignant thyroid nodules. However, more studies are still needed on the standardized SMI diagnostic criteria for thyroid nodules.

Diagnostic accuracy of HIK1083 and MUC6 as immunohistochemical markers of endocervical gastric-type adenocarcinoma: A systematic review and meta-analysis.

Diagnostic Value of Clinical Tests for Infraspinatus Tendon Tears