Diagnostic performance of AI-based models versus physicians among patients with hepatocellular carcinoma: a systematic review and meta-analysis.

Background: Hepatocellular carcinoma (HCC) is a common primary liver cancer that requires early diagnosis due to its poor prognosis. Recent advances in artificial intelligence (AI) have facilitated hepatocellular carcinoma detection using multiple AI models; however, their performance is still uncertain. Aim: This meta-analysis aimed to compare the diagnostic performance of different AI models with that of clinicians in the detection of hepatocellular carcinoma. Methods: We searched the PubMed, Scopus, Cochrane Library, and Web of Science databases for eligible studies. The R package was used to synthesize the results. The outcomes of various studies were aggregated using fixed-effect and random-effects models. Statistical heterogeneity was evaluated using I-squared (I2) and chi-square statistics. Results: We included seven studies in our meta-analysis;. Both physicians and AI-based models scored an average sensitivity of 93%. Great variation in sensitivity, accuracy, and specificity was observed depending on the model and diagnostic technique used. The region-based convolutional neural network (RCNN) model showed high sensitivity (96%). Physicians had the highest specificity in diagnosing hepatocellular carcinoma(100%); furthermore, models-based convolutional neural networks achieved high sensitivity. Models based on AI-assisted Contrast-enhanced ultrasound (CEUS) showed poor accuracy (69.9%) compared to physicians and other models. The leave-one-out sensitivity revealed high heterogeneity among studies, which represented true differences among the studies. Conclusion: Models based on Faster R-CNN excel in image classification and data extraction, while both CNN-based models and models combining contrast-enhanced ultrasound (CEUS) with artificial intelligence (AI) had good sensitivity. Although AI models outperform physicians in diagnosing HCC, they should be utilized as supportive tools to help make more accurate and timely decisions.

Artificial intelligence can help detecting incidental intracranial aneurysm on routine brain MRI using TOF MRA data sets and improve the time required for analysis of these images.

PURPOSE: The aim of our study was to assess the diagnostic performance of commercially available AI software for intracranial aneurysm detection and to determine if the AI system enhances the radiologist's accuracy in identifying aneurysms and reduces image analysis time. METHODS: TOF-MRA clinical brain examinations were analyzed using commercially available software and by an consultant neuroradiologist for the presence of intracranial aneurysms. The results were compared with the reference standard, to measure the sensitivity and specificity of the software and the consultant neuroradiologist. Furthermore, we examined the time required for the neuroradiologist to analyze the TOF-MRA image set, both with and without use of the AI software. RESULTS: In 500 TOF-MRI brain studies, 106 aneurysms were detected in 85 examinations by combining AI software with neuroradiologist readings. The neuroradiologist identified 98 aneurysms (92.5% sensitivity), while AI detected 77 aneurysms (72.6% sensitivity). Specificity and sensitivity were calculated from the combined effort as reference. Combining AI and neuroradiologist readings significantly improves detection reliability. Additionally, AI integration reduced TOF-MRA analysis time by 19 s (23% reduction). CONCLUSIONS: Our findings indicate that the AI-based software can support neuroradiologists in interpreting brain TOF-MRA. A combined reading of the AI-based software and the neuroradiologist demonstrated higher reliability in identifying intracranial aneurysms as compared to reading by either neuroradiologist or software, thus improving diagnostic accuracy of the neuroradiologist. Simultaneously, reading time for the neuroradiologist was reduced by approximately one quarter.

Clinical significance of direct fast scarlet staining on the diagnosis of eosinophilic colitis: A comparative study focusing on the eosinophil degranulation in colonic mucosal tissue.

This study aimed to validate the DFS (direct fast scarlet) staining in the diagnosis of EC (eosinophilic colitis). The study included 50 patients with EC and 60 with control colons. Among the 60 control samples, 39 and 21 were collected from the ascending and descending colons, respectively. We compared the median number of eosinophils and frequency of eosinophil degranulation by HE (hematoxylin and eosin) and DFS staining between the EC and control groups. In the right hemi-colon, eosinophil count by HE was useful in distinguishing between EC and control (41.5 vs. 26.0 cells/HPF, p < 0.001), but the ideal cutoff value is 27.5 cells/HPF (high-power field). However, this method is not useful in the left hemi-colon (12.5 vs. 13.0 cells/HPF, p = 0.990). The presence of degranulation by DFS allows us to distinguish between the groups even in the left hemi-colon (58% vs. 5%, p < 0.001). DFS staining also enabled a more accurate determination of degranulation than HE. According to the current standard to diagnose EC (count by HE staining ≥20 cells/HPF), mucosal sampling from left hemi-colon is problematic since the number of eosinophils could not be increased even in EC. Determination of degranulated eosinophils by DFS may potentiate the diagnostic performance even in such conditions.

Performance of metagenomic Next-Generation Sequencing and metagenomic Nanopore Sequencing for the diagnosis of tuberculosis in HIV-positive patients.

Background: Patients who were infected by the Human Immunodeficiency Virus (HIV) could have weakened immunity that is complicated by opportunistic infections, especially for Mycobacterium tuberculosis (MTB). Notably, the HIV-MTB co-infection will accelerate the course of disease progress and greatly increase the mortality of patients. Since the traditional diagnostic methods are time-consuming and have low sensitivity, we aim to investigate the performance of mNGS (metagenomic Next-Generation Sequencing) and mNPS (metagenomic NanoPore Sequencing) for the rapid diagnosis of tuberculosis in HIV-infected patients. Methods: The 122 HIV-infected patients were enrolled for the retrospective analysis. All of the patients underwent traditional microbiological tests, mNGS, and (or) mNPS tests. The clinical comprehensive diagnosis was used as the reference standard to compare the diagnostic performance of culture, mNGS, and mNPS on tuberculosis. We also investigate the diagnostic value of mNGS and mNPS on mixed-infection. Furthermore, the treatment adjustment directed by mNGS and mNPS was analyzed. Results: Compared with the composite reference standard, the culture showed 42.6% clinical sensitivity and 100% specificity, and the OMT(other microbiological testing) had 38.9% sensitivity and 100% specificity. The mNGS had 58.6% clinical sensitivity and 96.8% specificity, and the mNPS had 68.0% clinical sensitivity and 100% specificity. The proportion of mixed-infection cases (88.9%) in the TB group was higher than those in the non-TB group (54.8%) and the mNGS and mNPS are more competitive on mixed-infection diagnosis compared with the traditional methods. Furthermore, there are 63 patients (69.2%) and 36 patients (63.2%) achieved effective treatment after receiving the detection of mNPS and mNGS, respectively. Conclusion: Our study indicated that mNPS and mNGS have high sensitivity and specificity for TB diagnosis compared with the traditional methods, and mNPS seems to have better diagnostic performance than mNGS. Moreover, mNGS and mNPS showed apparent advantages in detecting mixed infection. The mNPS and mNGS-directed medication adjustment have effective treatment outcomes for HIV-infected patients who have lower immunity.

Enhancing Breast Cancer Detection through Advanced AI-Driven Ultrasound Technology: A Comprehensive Evaluation of Vis-BUS.

This study aims to enhance breast cancer detection accuracy through an AI-driven ultrasound tool, Vis-BUS, developed by Barreleye Inc., Seoul, South Korea. Vis-BUS incorporates Lesion Detection AI (LD-AI) and Lesion Analysis AI (LA-AI), along with a Cancer Probability Score (CPS), to differentiate between benign and malignant breast lesions. A retrospective analysis was conducted on 258 breast ultrasound examinations to evaluate Vis-BUS's performance. The primary methods included the application of LD-AI and LA-AI to b-mode ultrasound images and the generation of CPS for each lesion. Diagnostic accuracy was assessed using metrics such as the Area Under the Receiver Operating Characteristic curve (AUROC) and the Area Under the Precision-Recall curve (AUPRC). The study found that Vis-BUS achieved high diagnostic accuracy, with an AUROC of 0.964 and an AUPRC of 0.967, indicating its effectiveness in distinguishing between benign and malignant lesions. Logistic regression analysis identified that 'Fatty' lesion density had an extremely high odds ratio (OR) of 27.7781, suggesting potential convergence issues. The 'Unknown' density category had an OR of 0.3185, indicating a lower likelihood of correct classification. Medium and large lesion sizes were associated with lower likelihoods of correct classification, with ORs of 0.7891 and 0.8014, respectively. The presence of microcalcifications showed an OR of 1.360. Among Breast Imaging-Reporting and Data System categories, category C5 had a significantly higher OR of 10.173, reflecting a higher likelihood of correct classification. Vis-BUS significantly improves diagnostic precision and supports clinical decision-making in breast cancer screening. However, further refinement is needed in areas like lesion density characterization and calcification detection to optimize its performance.

Diagnostic Performance of a Deep Learning-Powered Application for Aortic Dissection Triage Prioritization and Classification.

This multicenter retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from six manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The diagnostic performance of the device was compared with the ground truth established by the majority agreement of three U.S. board-certified radiologists. Furthermore, the DL algorithm's time to notification was evaluated to demonstrate clinical effectiveness. The study included 1303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8-97.5%] and a specificity of 97.3% [95% CI: 96.2-98.1%]. The application classified positive cases by the AD type with an accuracy of 99.5% [95% CI: 98.9-99.8%] for type A and 97.5 [95% CI: 96.4-98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 s. This deep learning-based application demonstrated a strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.

A Systematic Review of Diagnostic Performance of Circulating MicroRNAs in Colorectal Cancer Detection with a Focus on Early-Onset Colorectal Cancer.

The rising incidence and mortality of early-onset colorectal cancer (EOCRC) emphasize the urgent need for effective non-invasive screening. Circulating microRNAs (miRNAs) have emerged as promising biomarkers for cancer detection. This systematic review aims to evaluate the diagnostic performance of circulating miRNAs in detecting colorectal cancer (CRC). A literature search was conducted in PubMed and Scopus. Studies that report sensitivity, specificity, or area under the curve (AUC) for CRC detection by miRNA were included. The miRNA miR-21 was the most frequently studied biomarker, with a varying range of AUC from 0.55 to 0.973 attributed to differences in study populations and methodologies. The miRNAs miR-210 and miR-1246 showed potential diagnostic capacity with miR-1246 achieving an AUC of 0.924, 100% sensitivity, and 80% specificity. The miRNA panels offer improved diagnostic performance compared to individual miRNA. The best performing panel for CRC patients below 50 is miR-211 + miR-25 + TGF-ß1 with AUC 0.99 and 100 specificity and 97 sensitivity. Circulating miRNAs hold significant promise as non-invasive biomarkers for CRC screening. However, the variability in diagnostic performance highlights the need for a standardized method and robust validation studies. Future research should focus on large-scale, ethnically diverse cohorts to establish clinically relevant miRNA biomarkers for CRC, particularly in younger populations.

Performance of artificial intelligence in detecting the chronic total occlusive lesions of coronary artery based on coronary computed tomographic angiography.

Background: Coronary chronic total occlusion (CTO) increases the risk of developing major adverse cardiovascular events (MACE) and cardiogenic shock. Coronary computed tomography angiography (CCTA) is a safe, noninvasive method to diagnose CTO lesions. With the development of artificial intelligence (AI), AI has been broadly applied in cardiovascular images, but AI-based detection of CTO lesions from CCTA images is difficult. We aim to evaluate the performance of AI in detecting the CTO lesions of coronary arteries based on CCTA images. Methods: We retrospectively and consecutively enrolled patients with 50% stenosis, 50-99% stenosis, and CTO lesions who received CCTA scans between June 2021 and June 2022 in Beijing Anzhen Hospital. Four-fifths of them were randomly assigned to the training dataset, while the rest (1/5) were randomly assigned to the testing dataset. Performance of the AI-assisted CCTA (CCTA-AI) in detecting the CTO lesions was evaluated through sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic analysis. With invasive coronary angiography as the reference, the diagnostic performance of AI method and manual method was compared. Results: A total of 537 patients with 1,569 stenotic lesions (including 672 lesions with <50% stenosis, 493 lesions with 50-99% stenosis, and 404 CTO lesions) were enrolled in our study. CCTA-AI saved 75% of the time in post-processing and interpreting the CCTA images when compared to the manual method (116±15 vs. 472±45 seconds). In the testing dataset, the accuracy of CCTA-AI in detecting CTO lesions was 86.2% (79.0%, 90.3%), with the area under the curve of 0.874. No significant difference was found in detecting CTO lesions between AI and manual methods (P=0.53). Conclusions: AI can automatically detect CTO lesions based on CCTA images, with high diagnostic accuracy and efficiency.

The STOP-Bang questionnaire: A narrative review on its utilization in different populations and settings.

STUDY RATIONALE: Although the STOP-Bang questionnaire has been validated for its efficacy and diagnostic performance in various settings, there is no review that summarizes the pertinent evidence of the STOP-Bang questionnaire in the different populations. We aimed to review the evidence of the diagnostic performance of the STOP-Bang questionnaire, correlation between STOP-Bang scores and the probability of obstructive sleep apnea (OSA), and its clinical application in various populations. STUDY IMPACT: This review guides healthcare providers in the sleep medicine and perioperative medicine disciplines to be better informed when using the STOP-Bang questionnaire in the different populations. It provides a greater understanding for both patients and clinicians when making decisions regarding OSA screening for each population.

Added value of artificial intelligence solutions for arterial stenosis detection on head and neck CT angiography: A randomized crossover multi-reader multi-case study.

PURPOSE: The purpose of this study was to investigate the added value of artificial intelligence (AI) solutions for the detection of arterial stenosis (AS) on head and neck CT angiography (CTA). MATERIALS AND METHODS: Patients who underwent head and neck CTA examinations at two hospitals were retrospectively included. CTA examinations were randomized into group 1 (without AI-washout-with AI) and group 2 (with AI-washout-without AI), and six readers (two radiology residents, two non-neuroradiologists, and two neuroradiologists) independently interpreted each CTA examination without and with AI solutions. Additionally, reading time was recorded for each patient. Digital subtraction angiography was used as the standard of reference. The diagnostic performance for AS at lesion and patient levels with four AS thresholds (30 %, 50 %, 70 %, and 100 %) was assessed by calculating sensitivity, false-positive lesions index (FPLI), specificity, and accuracy. RESULTS: A total of 268 patients (169 men, 63.1 %) with a median age of 65 years (first quartile, 57; third quartile, 72; age range: 28-88 years) were included. At the lesion level, AI improved the sensitivity of all readers by 5.2 % for detecting AS ≥ 30 % (P < 0.001). Concurrently, AI reduced the FPLI of all readers and specifically neuroradiologists for detecting non-occlusive AS (all P < 0.05). At the patient level, AI improved the accuracy of all readers by 4.1 % (73.9 % [1189/1608] without AI vs. 78.0 % [1254/1608] with AI) (P < 0.001). Sensitivity for AS ≥ 30 % and the specificity for AS ≥ 70 % increased for all readers with AI assistance (P = 0.01). The median reading time for all readers was reduced from 268 s without AI to 241 s with AI (P< 0.001). CONCLUSION: AI-assisted diagnosis improves the performance of radiologists in detecting head and neck AS, and shortens reading time.

Detection and Monitoring of Highly Pathogenic Influenza A Virus 2.3.4.4b Outbreak in Dairy Cattle in the United States.

The emergence and spread of highly pathogenic avian influenza virus A subtype H5N1 (HP H5N1-IAV), particularly clade H5N1 2.3.4.4b, pose a severe global health threat, affecting various species, including mammals. Historically, cattle have been considered less susceptible to IAV, but recent outbreaks of H5N1-IAV 2.3.4.4b in dairy farms suggest a shift in host tropism, underscoring the urgency of expanded surveillance and the need for adaptable diagnostic tools in outbreak management. This study investigated the presence of anti-nucleoprotein (NP) antibodies in serum and milk and viral RNA in milk on dairy farms affected by outbreaks in Texas, Kansas, and Michigan using a multi-species IAV ELISA and RT-qPCR. The analysis of ELISA results from a Michigan dairy farm outbreak demonstrated a positive correlation between paired serum and milk sample results, confirming the reliability of both specimen types. Our findings also revealed high diagnostic performance during the convalescent phase (up to 96%), further improving sensitivity through serial sampling. Additionally, the evaluation of diagnostic specificity using serum and milk samples from IAV-free farms showed an excellent performance (99.6%). This study underscores the efficacy of the IAV NP-blocking ELISA for detecting and monitoring H5N1-IAV 2.3.4.4b circulation in dairy farms, whose recent emergence raises significant animal welfare and zoonotic concerns, necessitating expanded surveillance efforts.

Diagnostic Accuracy of ChatGPT for Patients' Triage; a Systematic Review and Meta-Analysis.

Introduction: Artificial intelligence (AI), particularly ChatGPT developed by OpenAI, has shown the potential to improve diagnostic accuracy and efficiency in emergency department (ED) triage. This study aims to evaluate the diagnostic performance and safety of ChatGPT in prioritizing patients based on urgency in ED settings. Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines. Comprehensive literature searches were performed in Scopus, Web of Science, PubMed, and Embase. Studies evaluating ChatGPT's diagnostic performance in ED triage were included. Quality assessment was conducted using the QUADAS-2 tool. Pooled accuracy estimates were calculated using a random-effects model, and heterogeneity was assessed with the I² statistic. Results: Fourteen studies with a total of 1,412 patients or scenarios were included. ChatGPT 4.0 demonstrated a pooled accuracy of 0.86 (95% CI: 0.64-0.98) with substantial heterogeneity (I² = 93%). ChatGPT 3.5 showed a pooled accuracy of 0.63 (95% CI: 0.43-0.81) with significant heterogeneity (I² = 84%). Funnel plots indicated potential publication bias, particularly for ChatGPT 3.5. Quality assessments revealed varying levels of risk of bias and applicability concerns. Conclusion: ChatGPT, especially version 4.0, shows promise in improving ED triage accuracy. However, significant variability and potential biases highlight the need for further evaluation and enhancement.

Whole-Body HER2 Heterogeneity Identified on HER2 PET in HER2-Negative, -Low, and -Positive Metastatic Breast Cancer.

Understanding which patients with human epidermal growth factor receptor 2 (HER2)-negative or -low metastatic breast cancer (MBC) benefit from HER2-targeted strategies is urgently needed. We assessed the whole-body heterogeneity of HER2 expression on 89Zr-trastuzumab PET (HER2 PET) and the diagnostic performance of HER2 PET in a large series of patients, including HER2-negative and -low MBC. Methods: In the IMPACT-MBC study, patients with newly diagnosed and nonrapidly progressive MBC of all subtypes were included. Metastasis HER2 status was determined by immunohistochemistry and in situ hybridization.89Zr-trastuzumab uptake was quantified as SUVmax and SUVmean HER2 immunohistochemistry was related to the quantitative 89Zr-trastuzumab uptake of all metastases and corresponding biopsied metastasis, uptake heterogeneity, and qualitative scan evaluation. A prediction algorithm for HER2 immunohistochemistry positivity based on uptake was developed. Results: In 200 patients, 89Zr-trastuzumab uptake was quantified in 5,163 metastases, including 186 biopsied metastases. With increasing HER2 immunohistochemistry status, uptake was higher (geometric mean SUVmax of 7.0, 7.6, 7.3, and 17.4 for a HER2 immunohistochemistry score of 0, 1, 2, or 3+, respectively; P < 0.001). High uptake exceeding 14.6 (90th percentile) was observed in one third of patients with a HER2-negative or -low metastasis biopsy. The algorithm performed best when lesion site and size were incorporated (area under the curve, 0.86; 95% CI, 0.79-0.93). Conclusion: HER2 PET had good diagnostic performance in MBC, showing considerable whole-body HER2 heterogeneity and uptake above background in HER2-negative and -low MBC. This provides novel insights into HER2-negative and -low MBC compared with standard HER2 immunohistochemistry on a single biopsy.

Exploring the clinical and diagnostic value of metagenomic next-generation sequencing for urinary tract infection: a systematic review and meta-analysis.

BACKGROUND: A new pathogen detection tool, metagenomic next-generation sequencing (mNGS), has been widely used for infection diagnosis, but the clinical and diagnostic value of mNGS in urinary tract infection (UTI) remains inconclusive. This systematic review with meta-analysis aimed to investigate the efficacy of mNGS in treating UTIs. METHODS: A comprehensive literature search was performed in PubMed, Web of Science, Embase, and the Cochrane Library, and eligible studies were selected based on the predetermined criteria. The quality of the included studies was assessed via the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, and the certainty of evidence (CoE) was measured by the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) score. Then, the positive detection rate (PDR), pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve of the summary receiver operating characteristic curve (AUROC) was estimated in Review Manager, Stata, and MetaDisc. Subgroup analysis, meta-regression, and sensitivity analysis were performed to reveal the potential factors that influence internal heterogeneity. RESULTS: A total of 17 studies were selected for further analysis. The PDR of mNGS was markedly greater than that of culture (odds ratio (OR) = 2.87, 95% confidence interval [CI]: 1.72-4.81, p < 0.001, I2 = 90%). The GRADE score presented a very low CoE. Then, the pooled sensitivity was 0.89 (95% CI: 0.86-0.91, I2 = 39.65%, p = 0.06), and the pooled specificity was 0.75 (95% CI: 0.51-0.90, I2 = 88.64%, p < 0.001). The AUROC of the studies analyzed was 0.89 (95% CI: 0.86-0.92). The GRADE score indicated a low CoE. CONCLUSION: The current evidence shows that mNGS has favorable diagnostic performance for UTIs. More high-quality prospective randomized controlled trials (RCTs) are expected to verify these findings and provide more information about mNGS in UTI treatment and prognosis.

Diagnostic performance of [18 F]FDOPA PET/CT and other tracers in pheochromocytoma: A meta-analysis.

RATIONALE AND OBJECTIVES: PET-CT is extensively used in the diagnosis of pheochromocytoma (PHEO). However, various PET-CT tracers are recommended for the diagnosis of PHEO. Therefore, this study evaluated the diagnostic performance of all tracers currently used in the PET-CT detection of PHEO. METHODS: Studies were retrieved from PubMed, Web of Science, Embase, and Cochrane Library from inception to Feb. 7, 2024. The studies were screened according to the eligibility criteria and the data were extracted. Quality of the included studies was evaluated by the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic (sROC) curve (AUC) were pooled in Stata 15, and diagnostic accuracy was pooled in R 4.3.3. RESULTS: Sixteen studies were included in the meta-analysis. The sensitivity and specificity of [18 F]FDOPA PET/CT for initial PHEO diagnosis were 97% (95% CI: 91%-99%, I2 = 46.14%, p > 0.01) and 94% (95% CI: 86%-98%, I2 = 87.90%, p < 0.01), respectively. The AUC was 0.99 (95% CI: 0.98-1.00). The diagnostic accuracy of [18 F]FDOPA PET/CT was 98.9% (95% CI: 95%-100%) for PHEO patients and 89.7% (95% CI: 85.4%-92.8%) for PHEO lesions. [68Ga]DOTATATE PET/CT had a diagnostic accuracy of 86.9% (95% CI: 78.2%-93.9%) for PHEO and 87.5% (95% CI: 70.3%-95.4%) for PHEO lesions. FDG PET/CT had a diagnostic accuracy of 85.2% (95% CI: 73.6%-94.1%) for PHEO and 86.8% (95% CI: 73%-94.2%) for PHEO lesions. [68Ga]DOTANOC PET/CT had a diagnostic accuracy of 79.3% (95% CI: 49.2%-98.3%) for PHEO. CONCLUSIONS: In general, PET/CT demonstrates superior performance in the diagnosis of PHEO. In addition, [18 F]FDOPA PET/CT has the best diagnostic performance in PHEO compared with other tracers. Given the limited research on other PET/CT tracers and the potential constraints on their widespread use, additional multicenter and multiregional studies are warranted to further evaluate their diagnostic performance and provide recommendations for clinical use.

Circulating Exosomal miRNA Profiles in Non-Small Cell Lung Cancers.

A growing number of studies have shown that microRNAs (miRNAs) can exert oncogenic or tumor suppressor activities in a variety of cancers, including lung cancer. Given their presence in exosome preparations, microRNA molecules may in fact participate in exosomal intercellular transfers and signaling. In the present study, we examined the profile of 25 circulating exosomal microRNAs in ostensibly healthy controls compared to patients with squamous cell lung cancers (SQCLC) or lung adenocarcinomas (LUAD). Eight miRNAs, namely, miR-21-5p, miR-126-3p, miR-210-3p, miR-221-3p, Let-7b-5p, miR-146a-5p, miR-222-3p, and miR-9-5p, were highly enriched in the cohort and selected for further analyses. All miRNAs were readily detected in non-small cell lung cancer (NSCLC) patients of both sexes at all cancer stages, and their levels in exosomes correlated with the clinicopathological characteristics of tumors. Thus, the presence of these miRNAs in circulating exosomes may contribute to the regulation of oncogenic activity in patients with NSCLC.

Diagnostic Performance of Serum Leucine-Rich Alpha-2-Glycoprotein 1 in Pediatric Acute Appendicitis: A Prospective Validation Study.

Introduction: Leucine-rich alpha-2-glycoprotein 1(LRG-1) is a human protein that has shown potential usefulness as a biomarker for diagnosing pediatric acute appendicitis (PAA). This study aims to validate the diagnostic performance of serum LRG-1 in PAA. Material and Methods: This work is a subgroup analysis from BIDIAP (BIomarkers for DIagnosing Appendicitis in Pediatrics), a prospective single-center observational cohort, to validate serum LRG-1 as a diagnostic tool in PAA. This analysis included 200 patients, divided into three groups: (1) healthy patients undergoing major outpatient surgery (n = 56), (2) patients with non-surgical abdominal pain (n = 52), and (3) patients with a confirmed diagnosis of PAA (n = 92). Patients in group 3 were divided into complicated and uncomplicated PAA. In all patients, a serum sample was obtained during recruitment, and LRG-1 concentration was determined by Enzyme-Linked ImmunoSorbent Assay (ELISA). Comparative statistical analyses were performed using the Mann-Whitney U, Kruskal-Wallis, and Fisher's exact tests. The area under the receiver operating characteristic curves (AUC) was calculated for all pertinent analyses. Results: Serum LRG-1 values, expressed as median (interquartile range) were 23,145 (18,246-27,453) ng/mL in group 1, 27,655 (21,151-38,795) ng/mL in group 2 and 40,409 (32,631-53,655) ng/mL in group 3 (p < 0.0001). Concerning the type of appendicitis, the serum LRG-1 values obtained were 38,686 (31,804-48,816) ng/mL in the uncomplicated PAA group and 51,857 (34,013-64,202) ng/mL in the complicated PAA group (p = 0.02). The area under the curve (AUC) obtained (group 2 vs. 3) was 0.75 (95% CI 0.67-0.84). For the discrimination between complicated and uncomplicated PAA, the AUC obtained was 0.66 (95% CI 0.52-0.79). Conclusions: This work establishes normative health ranges for serum LRG-1 values in the pediatric population and shows that serum LRG-1 could be a potentially helpful tool for diagnosing PAA in the future. Future prospective multicenter studies, with the parallel evaluation of urinary and salivary LRG-1, are necessary to assess the implementability of this molecule in actual clinical practice.

Development and validation of reverse-transcription cross-priming amplification-based lateral flow assay for the detection of infectious hematopoietic necrosis virus.

Infectious hematopoietic necrosis virus (IHNV) severely and lethally infects salmonid fish, including Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss) worldwide. Rapid and accurate viral detection is crucial for preventing pathogen spread and minimizing damage. Although several IHNV detection assays have been developed, their analytical and diagnostic performances have not been evaluated and field usability assessments have not been completely validated. Here, we developed a reverse-transcription cross-priming amplification-based lateral flow assay (RT-CPA-LFA) and validated its diagnostic performance. To detect the IHNV, primers were designed based on the consensus sequence of the nucleocapsid (N) gene. Notably, when combined with a lateral flow dipstick, it could visualize the IHNV amplification products within 5 min and the detection limit of the developed RT-CPA-LFA was 3.28×105 copies/µL. The diagnostic sensitivity and specificity in fish samples (n=140) were 98.88 % and 96.08 %, respectively. Moreover, the IHNV detection rate by RT-CPA-LFA in dead rainbow trout artificially injected with the virus was 100 %, consistent with to the results obtained from second conventional and real-time PCR, indicating its applicability for rapid IHNV detection and presumptive IHN diagnosis during the endemic period.

Diagnostic Performance of Preoperative Calcitonin and Procalcitonin Tests for Differential Diagnosis of Medullary Thyroid Cancer.

Medullary thyroid cancer (MTC) shows a relatively poor prognosis among thyroid cancers. Though calcitonin has been used as a diagnostic marker for MTC, it has disadvantages including poor sample stability and discrepancies among results by assay. This study aimed to compare the usefulness of preoperative calcitonin and procalcitonin (PCT) in the diagnosis of MTC. Serum calcitonin and PCT levels were measured before thyroidectomy from MTC (n = 23) and other types of thyroid cancers in patients (n = 1308). Diagnostic performances of calcitonin and PCT for discerning MTC were estimated. In a multivariate analysis, preoperative calcitonin level was independently associated with the diagnosis of MTC, whereas PCT was not. Calcitonin and PCT, respectively, exhibited area under the curve values of 0.997 and 0.979 for the diagnosis of MTC, without significant differences. For calcitonin, the sensitivity, specificity, and positive and negative predictive values were 0.957, 0.992, 0.688, and 0.999, respectively, at a cut-off of 7.2 pg/mL. The corresponding values for PCT were 0.913, 0.995, 0.778, and 0.998 at a cut-off of 0.19 ng/mL. Preoperative calcitonin and PCT showed similar diagnostic utility for MTC. Depending on the patient's clinical status and laboratory environment, these tests can be used as complementary methods for detecting MTC.