Assessing the Predictive Accuracy of the aMAP Risk Score for Hepatocellular Carcinoma (HCC): Diagnostic Test Accuracy and Meta-analysis.

Purpose: We aimed to perform a meta-analysis with the intention of evaluating the reliability and test accuracy of the aMAP risk score in the identification of HCC. Methods: A systematic search was performed in PubMed, Scopus, Cochrane, Embase, and Web of Science databases from inception to September 2023, to identify studies measuring the aMAP score in patients for the purpose of predicting the occurrence or recurrence of HCC. The meta-analysis was performed using the meta package in R version 4.1.0. The diagnostic accuracy meta-analysis was conducted using Meta-DiSc software. Results: Thirty-five studies 102,959 participants were included in the review. The aMAP score was significantly higher in the HCC group than in the non-HCC group, with a mean difference of 6.15. When the aMAP score is at 50, the pooled sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio with 95% CI was 0.961 (95% CI 0.936, 0.976), 0.344 (95% CI 0.227, 0.483), 0.114 (95% CI 0.087, 0.15), and 1.464 (95% CI 1.22, 1.756), respectively. At a cutoff value of 60, the pooled sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio with 95% CI was 0.594 (95% CI 0.492, 0.689), 0.816 (95% CI 0.714, 0.888), 0.497 (95% CI 0.418, 0.591), and 3.235 (95% CI 2.284, 4.582), respectively. Conclusion: The aMAP score is a reliable, accurate, and easy-to-use tool for predicting HCC patients of all stages, including early-stage HCC. Therefore, the aMAP score can be a valuable tool for surveillance of HCC patients and can help to improve early detection and reduce mortality.

Evaluating the Performance of Different Criteria in Diagnosing AD and Preclinical AD with the Bayesian Latent Class Model.

BACKGROUND: The diagnostic criteria for Alzheimer's disease (AD) should be highly sensitive and specific. Clinicians have varying opinions on the different criteria, including the International Working Group-1 (IWG-1), International Working Group-2 (IWG-2), and AT(N) criteria. Few studies had evaluated the performance of these criteria in diagnosing AD and preclinical AD when the gold standard was absent. METHODS: We estimated and compared the performance of these criteria in diagnosing AD using data from 908 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Additionally, 622 subjects were selected to evaluate and compare the performance of IWG-2 and AT(N) criteria in diagnosing preclinical AD. A novel approach, Bayesian latent class models with fixed effect dependent, was utilized to estimate the diagnostic accuracy of these criteria in detecting different AD statuses simultaneously. RESULTS: The sensitivity of the IWG-1, IWG-2, and AT(N) criteria in diagnosing AD was 0.850, 0.836, and 0.665. The specificity of these criteria was 0.788, 0.746, and 0.747. The IWG-1 criteria had the highest Youden Index in detecting AD. When diagnosing preclinical AD, the sensitivity of the IWG-2 and AT(N) criteria was 0.797 and 0.955. The specificity of these criteria was 0.922 and 0.720. The IWG-2 criteria had the highest Youden Index. CONCLUSION: IWG-1 was more suitable than the IWG-2 and AT(N) criteria in detecting AD. IWG-2 criteria was more suitable than AT(N) criteria in detecting preclinical AD.

Diagnostic Accuracy of USG and MRI for the Detection of Rotator Cuff Injury.

INTRODUCTION: Rotator cuff disease frequently causes shoulder pain and is diagnosed using various radiological methods alongside history and physical examination. Arthrography has traditionally been employed for this purpose, but newer non-invasive techniques such as ultrasonography (USG) and magnetic resonance imaging (MRI) are increasingly used. However, no single method is universally agreed upon as the best diagnostic tool, each having its own limitations. OBJECTIVES: To evaluate how effectively ultrasound and MRI can diagnose rotator cuff tears. MATERIALS AND METHODS: Seventy patients suspected of having a rotator cuff tear underwent investigations at the Radiology Department of Krishna Vishwa Vidyapeeth (Deemed to be University), Karad. USG and MRI examinations were done on the same day, along with a detailed history. USG was conducted using a GE LOGIQ P9 machine with a high-frequency 3-12 MHz transducer. MRI was conducted using a 1.5T Siemens Magnetom Avanto scanner. RESULTS: Pain and stiffness are the most common complaints in rotator cuff tears. The predisposing factors include male predominance, increasing age, dominant hand use, and trauma history. The supraspinatus tendon is the most frequently injured, with partial tears, especially articular surface tears, being more common than full-thickness tears. Clinical examinations, USG, and MRI are valuable in diagnosing rotator cuff tears. CONCLUSION: Our findings indicate that USG may not be as reliable in detecting rotator cuff tears as once believed. A positive ultrasound result is more trustworthy than a negative one. In contrast, MRI demonstrates greater sensitivity and overall diagnostic accuracy compared to both ultrasonography and clinical assessment for detecting rotator cuff tears.

Prediction begins with diagnosis: Estimating seizure recurrence risk in the First Seizure Clinic.

OBJECTIVES: To assess the feasibility of using a seizure recurrence prediction tool in a First Seizure Clinic, considering (1) the accuracy of initial clinical diagnoses and (2) performance of automated computational models in predicting seizure recurrence after first unprovoked seizure (FUS). METHODS: To assess diagnostic accuracy, we analysed all sustained and revised diagnoses in patients seen at a First Seizure Clinic over 5 years with 6+ months follow-up ('accuracy cohort', n = 487). To estimate prediction of 12-month seizure recurrence after FUS, we used a logistic regression of clinical factors on a multicentre FUS cohort ('prediction cohort', n = 181), and compared performance to a recently published seizure recurrence model. RESULTS: Initial diagnosis was sustained over 6+ months follow-up in 69% of patients in the 'accuracy cohort'. Misdiagnosis occurred in 5%, and determination of unclassified diagnosis in 9%. Progression to epilepsy occurred in 17%, either following FUS or initial acute symptomatic seizure. Within the 'prediction cohort' with FUS, 12-month seizure recurrence rate was 41% (95% CI [33.8%, 48.5%]). Nocturnal seizure, focal seizure semiology and developmental disability were predictive factors. Our model yielded an Area under the Receiver Operating Characteristic curve (AUC) of 0.60 (95% CI [0.59, 0.64]). CONCLUSIONS: High clinical accuracy can be achieved at the initial visit to a First Seizure Clinic. This shows that diagnosis will not limit the application of seizure recurrence prediction tools in this context. However, based on the modest performance of currently available seizure recurrence prediction tools using clinical factors, we conclude that data beyond clinical factors alone will be needed to improve predictive performance.

Assessment of diagnostic value of unilateral systematic biopsy combined with targeted biopsy in detecting clinically significant prostate cancer.

Objectives: This retrospective study assessed the diagnostic accuracy of targeted biopsy (TB) and unilateral systematic biopsy in detecting clinically significant prostate cancer (csPCa) in 222 men with single magnetic resonance imaging (MRI) lesions (Prostate Imaging Reporting and Data System [PI-RADS] ≥ 3). Methods: Patients underwent multiparametric MRI and MRI/ultrasound fusion TB and 12-needle standard biopsy (SB) from September 2016 to June 2021. The study compared the diagnostic performance of TB + iSB (ipsilateral), TB + contralateral system biopsy (cSB) (contralateral), and TB alone for csPCa using the χ 2 test and analysis of variance. Results: Among 126 patients with csPCa (ISUP ≥ 2), detection rates for TB + iSB, TB + cSB, and TB were 100, 98.90, and 100% for lesions, respectively. TB + iSB showed the highest sensitivity and negative predictive value. No significant differences in accuracy were found between TB + iSB and the gold standard for type 3 lesions (P = 1). For types 4-5, detection accuracy was comparable across methods (P = 0.314, P = 0.314, P = 0.153). TB had the highest positive needle count rate, with TB + iSB being second for type 3 lesions (4.08% vs 6.57%, P = 0.127). Conclusion: TB + iSB improved csPCa detection rates and reduced biopsy numbers, making it a viable alternative to TB + SB for single MRI lesions.

Artificial intelligence-enhanced detection of subclinical coronary artery disease in athletes: diagnostic performance and limitations.

PURPOSE: This study evaluates the diagnostic performance of artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) for detecting coronary artery disease (CAD) and assessing fractional flow reserve (FFR) in asymptomatic male marathon runners. MATERIAL AND METHODS: We prospectively recruited 100 asymptomatic male marathon runners over the age of 45 for CAD screening. CCTA was analyzed using AI models (CorEx and Spimed-AI) on a local server. The models focused on detecting significant CAD (≥ 50% diameter stenosis, CAD-RADS 3, 4, or 5) and distinguishing hemodynamically significant stenosis (FFR ≤ 0.8) from non-significant stenosis (FFR > 0.8). Statistical analysis included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. RESULTS: The AI model demonstrated high sensitivity, with 91.2% for any CAD and 100% for significant CAD, and high NPV, with 92.7% for any CAD and 100% for significant CAD. The diagnostic accuracy was 73.4% for any CAD and 90.4% for significant CAD. However, the PPV was lower, particularly for significant CAD (25.0%), indicating a higher incidence of false positives. CONCLUSION: AI-enhanced CCTA is a valuable non-invasive tool for detecting CAD in asymptomatic, low-risk populations. The AI model exhibited high sensitivity and NPV, particularly for identifying significant stenosis, reinforcing its potential role in screening. However, limitations such as a lower PPV and overestimation of disease indicate that further refinement of AI algorithms is needed to improve specificity. Despite these challenges, AI-based CCTA offers significant promise when integrated with clinical expertise, enhancing diagnostic accuracy and guiding patient management in low-risk groups.

A Comparative Evaluation of Point-of-Care and Laboratory HbA1c Testing in Diabetes Care: An Indian Perspective.

AIM: This study aimed to assess the diagnostic performance of a point-of-care (POC) glycated hemoglobin (HbA1c) device in the Indian population against the standard laboratory (high-performance liquid chromatography {HPLC}) method for the effective management of diabetes in India. METHODS: This study on the diagnostic accuracy of a POC HbA1c device involved 121 participants. These participants were categorized into two groups according to their HbA1c levels - one group with HbA1c values below 6.5% and another group with HbA1c values equal to or greater than 6.5%. The HbA1c levels in enrolled participants were estimated using both the POC device (HemoCue HbA1c 501; Ängelholm, Sweden: HemoCue AB) and the standard HPLC-based method. The level of agreement and concordance between the two test results were assessed by the Bland-Altman plot and Lin's concordance correlation coefficient. Sensitivity, specificity, diagnostic accuracy, positive likelihood, and negative likelihood ratios of POC-HbA1c device were assessed with a 6.5% HbA1c cut-off. RESULTS:  The mean HbA1c values obtained by the two methods showed no statistically significant difference with a minimum effect size (Cohen's d = 0.035), indicating there was a negligible difference between these methods. The Bland-Altman plot revealed that most values were within acceptable limits (95% CI: -0.5 to 0.7) and Lin's concordance correlation coefficient showed strong agreement (p < 0.0001). The POC-HbA1c device demonstrated an area under the curve (AUC) of 0.991 (95% CI: 0.953-1.000) with sensitivity, specificity, diagnostic accuracy, positive likelihood, and negative likelihood ratios of 93.62%, 97.30%, 95.87%, 34.64 and 0.07, respectively, compared to the standard diagnostic assay. CONCLUSIONS: The diagnostic accuracy, sensitivity, and specificity demonstrated by the POC-HbA1c device to the standard HPLC method offers a viable and practical solution for diabetes management in India. Its ability to provide rapid and reliable results at the point of care can improve patient outcomes, reduce healthcare costs, and enhance access to diabetes care, especially in primary care, remote areas, and resource-limited settings of developing countries like India.

Diagnostic value of routine CT perfusion imaging for radiology residents.

To evaluate whether incorporating CT perfusion imaging can significantly enhance diagnostic CT accuracy in stroke detection. Two 3rd-year residents (3rd of 5 years of residency) reviewed CT scans of 200 patients with suspected stroke, consisting of 104 patients with a proven stroke and a control group with 96 patients. They analyzed each patient in a blinded and randomized manner in two runs. In one session, they had only non-contrast CT and CT angiography available for diagnosis; in the other session at a later time point, an additional CT perfusion imaging was available. The performance achieved by the two readers was determined in terms of AUC (area under the curve), accuracy, sensitivity, specificity, positive and negative predictive value and Cohen's Kappa. Reader 1 achieved an AUC of 87.64% with the basic stroke-protocol vs. an AUC of 97.4% with an additional CT-perfusion given. Based on the DeLong test, these values differ significantly (p-value: 0.00017). Reader 2 achieved an AUC of 91.23% in basic stroke-protocol vs. an AUC of 96.42% with an additional CT-perfusion. These values also differ significantly (p-value: 0.02612).. The performance gain achieved with CT-perfusion is most evident in the decrease in the number of false classified cases (Reader 1: 24 to 5; Reader 2: 18 or 14 to 7) and the significant increase in Cohen's kappa. Our study shows that additional CT-perfusion imaging in stroke diagnosis significantly improves the diagnostic reliability of residents. Therefore, it should be further investigated whether perfusion imaging should be a general standard of initial stroke diagnosis no matter of the onset.

Identification of Endosonographic Features that Compromise EUS-FNB Diagnostic Accuracy in Pancreatic Masses.

BACKGROUND: Endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) is highly accurate for diagnosing pancreatic mass. However, making diagnosis is challenging in 5-20% of patients. This study investigated the challenging features associated with reduced diagnostic performance in EUS-FNB and potential rescue methods that can improve the diagnostic rate. METHODS: This single-center retrospective study included patients with solid pancreatic tumors who underwent EUS-FNB between January 1, 2019, and December 12, 2021. Patients without a computed tomography (CT) scan or definite diagnosis were excluded. Challenging features were features that reduced diagnostic accuracy in EUS-FNB, as determined through multivariate analysis. Rescue methods were methods that assisted operators in assessing lesions in patients with challenging features. RESULTS: Of 332 enrolled patients, an accurate diagnosis obtained using EUS-FNB was achieved in 286 (86.1%). Univariable analysis revealed that the diagnostic accuracy was lower in cases of pancreatic tumors with isoattenuation in CT images (77.3% vs. 89.8%, odds ratio [OR]: 0.39, p = 0.003), an ill-defined margin on EUS (61.2% vs. 92.5%, OR: 0.13, p < 0.001), or tumor size < 20 mm (65.5% vs. 88.1%, OR: 0.26, p = 0.002). However, only ill-defined margins on EUS (OR: 0.14, p < 0.001) and tumor size < 20 mm (OR: 0.25, p = 0.005) were independent predictors of inconclusive EUS-FNB in the multivariate analysis. The use of contrast (OR: 4.46, p = 0.026) and a highly experienced endosonographer (> 5cases/month; OR: 3.25, p = 0.034) improved diagnostic performance in difficult cases. CONCLUSIONS: Pancreatic tumors with ill-defined tumor margins on EUS or size < 20 mm are challenging features in EUS-FNB. The use of contrast and a highly experienced endosonographer can improve diagnostic performance in difficult cases.

Evaluation of GFAP/UCH-L1 biomarkers for computed tomography exclusion in mild traumatic brain injury (mTBI).

INTRODUCTION: Mild traumatic brain injury (mTBI) represents a major public health concern and affects millions of people worldwide every year. Diagnosis mainly relies on clinical criteria and computed tomography (CT) scans. GFAP (glial fibrillary acidic protein) and UCH-L1 (ubiquitin carboxyl-terminal hydrolase-L1) have been recently studied as potential biomarkers of mTBI. This study retrospectively evaluated the possible use of these combined biomarkers as negative predictors for excluding brain injuries in patients with suspected mTBI in the emergency department. METHODS: Adult patients (n = 130) enrolled at Tor Vergata University Hospital (Rome, Italy), consecutively registered at the triage of the emergency department between October 2022 and January 2023, with non-penetrating TBI and Glasgow Coma Scale (GCS) score of 13-15, were considered. All eligible patients underwent intracranial CT scans and blood tests, within 12 h after trauma, for GFAP and UCH-L1 serum concentrations. RESULTS: Intracranial CT detected injuries in only seven patients (5%); GFAP and UCH-L1 tested positive in 96 patients and negative in 34 patients (74% vs. 26%). Combined biomarkers had a sensitivity equal to 1.00 (95% CI 0.64-1.00) and a negative predictive value (NPV) of 1.00 (0.99-1.00) in mTBI diagnosis with a negative CT. CONCLUSIONS: Combined laboratory tests for GFAP and UCH-L1 biomarkers might play a potential clinical role in avoiding unnecessary head CT scans after mTBI in emergency departments.

Estimates for diagnostic accuracy of rapid antigen tests for SARS-CoV-2 in systematic reviews are consistently similar despite poor methodological rigor: a methodological overview.

OBJECTIVES: To evaluate the design, conduct, and analysis of systematic reviews on the diagnostic accuracy of rapid antigen tests for SARS-CoV-2 during the COVID-19 pandemic. STUDY DESIGN AND SETTING: We did a methodological overview of systematic reviews on diagnostic test accuracy, exploring methodological rigor, risk of bias and potential factors of between-review variability. RESULTS: Of the 31 included reviews, 30 provided summary statistics for sensitivity and 29 for specificity. Summary sensitivities ranged from 56.2% to 91.1%, with a median of 71.5% (IQR 68.3%-76.6%) and a mean of 72.7% with a 7.2 SD. Reported summary specificity estimates were consistently high: median 99.5% (IQR 99%-99.9%) and a mean of 99.3% with a 0.9 SD. We found methodological shortcomings in the systematic reviews, with a majority showing critically low confidence in quality and a high risk of bias. CONCLUSION: Despite significant methodological flaws in the reviews, the diagnostic accuracy estimates for rapid antigen tests were characterized by a strong central tendency, highlighting the importance of large sample sizes and broad participant representation. This study suggests the need for further research in diagnostic test accuracy assessments of rigor and risk of bias in systematic reviews.

Diagnostic Accuracy of AI Algorithms in Aortic Stenosis Screening: A Systematic Review and Meta-Analysis.

Background: Aortic stenosis (AS) is frequently identified at an advanced stage after clinical symptoms appear. The aim of this systematic review and meta-analysis is to evaluate the diagnostic accuracy of artificial intelligence (AI) algorithms for AS screening.Methods: We conducted a thorough search of six databases. Several evaluation parameters, such as sensitivity, specificity, diagnostic odds ratio (DOR), negative likelihood ratio (NLR), positive likelihood ratio (PLR), and area under the curve (AUC) value were employed in the diagnostic meta-analysis of AI-based algorithms for AS screening. The AI algorithms utilized diverse data sources including electrocardiograms (ECG), chest radiographs, auscultation audio files, electronic stethoscope recordings, and cardio-mechanical signals from non-invasive wearable inertial sensors.Results: Of the 295 articles identified, 10 studies met the inclusion criteria. The pooled estimates for AI-based algorithms in diagnosing AS were as follows: sensitivity 0.83 (95% CI: 0.81-0.85), specificity 0.81 (95% CI: 0.79-0.84), PLR 4.78 (95% CI: 3.12-7.32), NLR 0.20 (95% CI: 0.13-0.28), and DOR 27.11 (95% CI: 14.40-51.05). The AUC value was 0.909 (95% CI: 0.889-0.929), indicating outstanding diagnostic accuracy. Subgroup and meta-regression analyses showed that continent, type of AS, data source, and type of AI-based method constituted sources of heterogeneity. Furthermore, we demonstrated proof of publication bias for DOR values analyzed using Egger's regression test (P = 0.002) and a funnel plot.Conclusion: Deep learning approaches represent highly sensitive, feasible, and scalable strategies to identify patients with moderate or severe AS.

Diagnostic accuracy of active pulmonary tuberculosis screening during detention admission: a systematic review.

Individuals entering incarceration are at high risk for infectious diseases, other ill conditions, and risky behavior. Typically, the status of active pulmonary tuberculosis (PTB) is not known at the time of admission. Early detection and treatment are essential for effective TB control. So far, no study has compared the diagnostic accuracy of various TB screening tools in detention using a network meta-analysis (NMA). We aimed to investigate the diagnostic accuracy of active PTB screening tests upon detention admission. We searched PubMed, Global Index Medicus, the Cochrane Library electronic databases, and grey literature for publications reporting detention TB entry screening in March 2022 and January 2024. Inclusion was non-restrictive regarding time, language, location, reference standards, or screening tests. Eligible study designs comprised comparative, observational, and diagnostic studies. Publications had to report TB screening of individuals entering confinement and provide data for diagnostic accuracy calculations. The QUADAS-2 tool was designed to assess the quality of primary diagnostic accuracy studies. This systematic review was registered with PROSPERO (CRD42022307863) and conducted without external funding. We screened a total of 2,455 records. Despite extensive searching, no studies met our inclusion criteria. However, we identified evidence revealing key differences in screening algorithm application. In conclusion, more diagnostic accuracy data on TB screening algorithms for detention admission worldwide needs to be collected. We recommend that global TB initiatives set up multi-site studies to investigate the diagnostic accuracy of TB screening on admission in low- and high-prevalence criminal justice systems. Further network meta-analyses of these studies could inform policymakers and public health experts to establish or fine-tune TB control in detention settings.

Diagnostic Test Accuracy of Contrast-Enhanced Ultrasound With Sonazoid for Assessment of Focal Liver Lesions: A Systematic Review and Meta-Analysis.

This meta-analysis examined the diagnostic accuracy of Sonazoid-enhanced ultrasonography (SZ-CEUS) in discriminating malignant from benign focal liver lesions (FLLs) and HCC from non-HCC FLLs. Finding relevant studies required a rigorous PubMed, EMBASE, and other database search. To distinguish malignant from benign FLLs, SZ-CEUS had a pooled sensitivity of 94% (95% CI: 0.91-0.95) and specificity of 84% (95%: 0.78-0.89). HCC distinction had 83% sensitivity and 96% specificity (95% CI: 0.80-0.85 and 0.95-0.97). SZ-CEUS accurately distinguishes malignant from benign FLLs and HCC from non-HCC lesions, especially smaller HCC lesions.

Comparison of AI-assisted cephalometric analysis and orthodontist-performed digital tracing analysis.

BACKGROUND: The aim of this study was to compare and evaluate three AI-assisted cephalometric analysis platforms-CephX, WeDoCeph, and WebCeph-with the traditional digital tracing method using NemoCeph software. MATERIAL AND METHOD: A total of 1500 lateral cephalometric films that met the inclusion criteria were classified as Class I, Class II, and Class III. Subsequently, 40 patients were randomly selected from each class. These selected films were uploaded to 3 AI-assisted cephalometric analysis platforms and analyzed without any manual intervention. The same films were also analyzed by an orthodontist using the NemoCeph program. RESULTS: The results revealed significant differences in key angular measurements (ANB, FMA, IMPA, and NLA) across Class I, II, and III patients when comparing the four cephalometric analysis methods (WebCeph, WeDoCeph, CephX, and NemoCeph). Notably, ANB (p < 0.05), FMA (p < 0.001), IMPA (p < 0.001), and NLA (p < 0.001) varied significantly. Linear measurements also differed, with significant differences in U1-NA (p = 0.002) and Co-A (p = 0.002) in certain classes. Repeated measurement analysis revealed variation in SNA (p = 0.011) and FMA (p = 0.030), particularly in the Class II NemoCeph group, suggesting method-dependent variability. CONCLUSION: AI-assisted cephalometric analysis platforms such as WebCeph, WeDoCeph, and CephX give rise to notable variation in accuracy and reliability compared to traditional manual digital tracing, specifically in terms of angular and linear measurements. These results emphasize the importance of meticulous selection and assessment of analysis methods in orthodontic diagnostics and treatment planning.

Diagnostic accuracy and added value of dynamic chest radiography in detecting pulmonary embolism: A retrospective study.

Purpose: This study aimed to assess the diagnostic performance of dynamic chest radiography (DCR) and investigate its added value to chest radiography (CR) in detecting pulmonary embolism (PE). Methods: Of 775 patients who underwent CR and DCR in our hospital between June 2020 and August 2022, individuals who also underwent contrast-enhanced CT (CECT) of the chest within 72 h were included in this study. PE or non-PE diagnosis was confirmed by CECT and the subsequent clinical course. The enrolled patients were randomized into two groups. Six observers, including two thoracic radiologists, two cardiologists, and two radiology residents, interpreted each chest radiograph with and without DCR using a crossover design with a washout period. Diagnostic performance was compared between CR with and without DCR in the standing and supine positions. Results: Sixty patients (15 PE, 45 non-PE) were retrospectively enrolled. The addition of DCR to CR significantly improved the sensitivity, specificity, accuracy, and area under the curve (AUC) in the standing (35.6-70.0 % [P < 0.0001], 84.8-93.3 % [P = 0.0010], 72.5-87.5 % [P < 0.0001], and 0.66-0.85 [P < 0.0001], respectively) and supine (33.3-65.6 % [P < 0.0001], 78.5-92.2 % [P < 0.0001], 67.2-85.6 % [P < 0.0001], and 0.62-0.80 [P = 0.0002], respectively) positions for PE detection. No significant differences were found between the AUC values of DCR with CR in the standing and supine positions (P = 0.11) or among radiologists, cardiologists, and radiology residents (P = 0.14-0.68). Conclusions: Incorporating DCR with CR demonstrated moderate sensitivity, high specificity, and high accuracy in detecting PE, all of which were significantly higher than those achieved with CR alone, regardless of scan position, observer expertise, or experience.

Diagnostic Accuracy of Neurocognitive and Executive Functions to Discriminate Women With and Without Fibromyalgia Syndrome: A Secondary Analysis.

Objective: The aim of the current study was to determine the capability of neurocognitive variables and executive functions to differentiate women with and without fibromyalgia syndrome (FMS). Methods: A secondary diagnostic accuracy analysis was conducted. A battery of neurocognitive and executive function tests (the D2 Attention test, the Rey-Osterrieth Complex Figure for visual perception, "Digits D/R/I" tests of the WAIS-IV battery for working memory, the 5-Digit test for mental inhibition, the Symbol Search for processing speed, and the Zoo Test for planning/decision-making) were collected in 129 women with FMS and 111 without FMS. The area under the receiver operating characteristic (ROC) curve, optimal cut-off point, sensitivity, specificity, and positive and negative likelihood ratios (LR) for each variable were calculated. Results: Between-group differences were identified in ROCF_Copy (p = 0.043), ROCF_Recall (p = 0.004), d2_TR (p = 0.019), d2_TA (p = 0.007), d2_TOT (p = 0.005), d2_CON (p = 0.004), d2_C (p = 0.042), Symbol Search (p = 0.008), Decoding _FDT (p = 0.001), Retrieving_FDT (p = 0.001), and Inhibiting_FDT (p = 0.024). The result showed that FDT-based outcomes (Retrieving_FDT: ROC 0.739, sensitivity 85.3%, specificity 48.6%; Decoding_FDT: ROC 0.724, sensitivity 50.4%, specificity 16.2%; Inhibiting_FDT: ROC 0.708, sensitivity 56.6%, specificity 22.5%) were the variables able to differentiate between women with and without FMS. Conclusions: Although women with FMS exhibited deficits in attention, long-term visual memory, processing speed, and mental inhibition when compared with women without FMS, only mental inhibition scores showed moderate diagnostic accuracy to discriminate between women with and without FMS. Future studies investigating these results in clinical settings are needed to identify the clinical relevance of these findings.

Assessing the Concordance of Clinical and Pathological Diagnoses in Basal Cell Carcinoma Among the Iranian Population: A Cross-Sectional Analysis of 229 Cases.

BACKGROUND: Nonmelanoma skin cancer (NMSC) is the most prevalent malignancy globally, with basal cell carcinoma (BCC) being the most common type. AIMS: This study aims to evaluate the concordance between clinical and pathological diagnoses of BCC, emphasizing the importance of early detection. METHODS AND RESULTS: In this cross-sectional study, we conducted a retrospective review of clinical and pathological records for 229 patients diagnosed with BCC between 2020 and 2024. The analysis focused on gender, age, lesion location, and diagnostic accuracy. Among the 229 patients, 193 were men (84.3%), and 131 (57.2%) had recorded clinical diagnoses. The mean age of diagnosed patients was 67.72 years. Lesions were primarily located on the scalp (29.5%), face (26.4%), and nose (13.9%). Of the pathological evaluations, 184 cases (80.3%) confirmed BCC, while 45 cases had alternative diagnoses. Notably, 94.6% of clinically diagnosed patients were suspected to have BCC by their physicians. A significant portion of cases (42%) lacked prior clinical diagnoses, reflecting a potential gap in education among nondermatologists regarding BCC recognition. CONCLUSION: The study found high concordance between clinical and pathological diagnoses of BCC, underscoring the need for improved clinical assessment skills among healthcare providers. Collaboration with dermatologists is essential for accurate diagnosis and improved patient outcomes. Enhanced training in recognizing BCC symptoms is recommended to address the identified gaps in clinical suspicion.

Clinical evaluation of a multiplex PCR-based test for joint infection: a prospective diagnostic accuracy study of forty-nine patients.

PURPOSE: The purpose of this study was to evaluate the diagnostic accuracy (sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)) of the PCR-based BioFire® Joint Infection Panel (BJI Panel) against microbiological culture growth for patients suspected of having a native or prosthetic joint infection. METHODS: Synovial fluid and tissue biopsies were prospectively collected from patients from June 2022 to June 2023. The results of the BJI Panel were compared with those of culture growth. RESULTS: 51 samples were included. Including all pathogens, the sensitivity was 69%, the specificity 89%, the PPV 73% and the NPV 86%. Including only pathogens in the BJI Panel, the sensitivity was 100%, the specificity 90%, the PPV 73% and the NPV 100%. CONCLUSION: The BJI Panel has a high accuracy for detecting the pathogens in its panel, but the absence of important common pathogens from the panel reduces its sensitivity and NPV. With a short turnaround time and precise pathogen detection, the BJI Panel has the potential to add value as a complementary diagnostic method.