Accuracy Studies Research Articles

A-241 Verification and Validation Study for a Host Response Assay Based on TRAIL, IP-10, and CRP: Application to Pediatric Emergency Department

Abstract Background Acute febrile illnesses (AFIs) represent a significant disease burden globally; however, the lack of reliable, rapid point-of-care testing makes diagnosis difficult. A simple tool for distinguishing bacterial versus non-bacterial infections would radically improve patient management and reduce indiscriminate antibiotic use. MeMed BV® (MMBV) is a blood test to reliably differentiate between viral and bacterial infections. Three host proteins (TRAIL, IP-10, and CRP) are measured quantitatively and a combinational score (0-100) is computed that indicates the likelihood of bacterial infection (or co-infection) versus viral infection (BV Score). MMBV time to result is 15 min on the MeMed Key® analyzer that conducts chemiluminescence-based immunoassays to measure protein concentrations and calculates the test score, using 100 µL of serum. Methods The verification study design was a comparison of known results to on-site testing for contrived samples. The validation study design was a prospective, single-center, observational, blinded diagnostic accuracy study conducted between 8/3/22 to 5/30/23. The population was pediatric patients with suspected serious infections presenting to Geisinger’s quaternary care Pediatric Emergency Dept. in Danville, PA. The comparator was a clinical chart review and adjudication of over 60 clinical and laboratory variables related to systemic infections. The data review team consisted of one pediatric emergency department physician, one pediatric infectious disease physician, and one microbiology laboratory director. If clinical decisions differed among the three, the team adjudicated with a full chart review including knowledge of the eventual outcome, which served as the final reference standard result. The study aims were to assess the analytical and clinical accuracy of MMBV testing and to assess the test’s potential impact to further inform the provider’s decision to admit or discharge the patient. Results Of 60 contrived samples there was three failed tests (5% failure rate) and 2 discordant results. Analytical accuracy was 55/57 (96.5%) for 28 bacterial, 26 viral, and 1 equivocal control. Of 50 pediatric patients recruited, 8 were excluded because they were trauma patients. Among 42 patients tested, 22/42 (52.4%) were interpreted as bacterial or co-infection, 16/42 (38.1%) as viral or non-infectious, and 4/42 (9.5%) as equivocal. For 37/38 (97.3%) of patients with bacterial/viral MMBV results, the result was concordant with expert adjudication. Of the 42 patients for which potential utility was assessed, all results were in alignment with the clinical decisions made for admission or discharge. Conclusions MMBV performed accurately in pediatric ED patients with symptoms of severe infection, both clinically and analytically. A larger sample size will be tested to assess the utility in ED admission and discharge decisions and antimicrobial stewardship. The prevalence of viral and bacterial or coinfection cases within the sample population is representative of the pediatric population samples.

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

PurposeThe 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.MethodsSynovial 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.Results51 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%.ConclusionThe 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.

B-099 Validation and Clinical Implementation of Organ Procurement Serology Testing: Our Analytical and Regulatory Experience in Implementing a New Donor Testing Program

Abstract Background The Mount Sinai Hospital recently implemented a comprehensive organ donor testing program to service the clinical needs of transplant services in the New York City metro area. Conventional serology testing is not approved by the FDA for donor testing. To support this program, the MSH Chemistry Laboratory implemented and validated a comprehensive donor testing panel on the Abbott Alinity S donor testing platform. We validated donor serology testing for the following assays: HIV-1/2 (HIV), Anti-Hepatitis B Core (Anti-HBc), Hepatitis B Surface Antigen (HBsAg), Human T-lymphotropic virus I and II (HTLV-I/II), Anti-Hepatitis C (Anti-HCV), and Trypanosoma cruzi antibodies (T.Cruzi). The Alinity S System is an automated immunoassay-based analyzer FDA-approved for donor testing. The analyzer consists of two distinct sample ‘paths’ and is effectively two instruments combined in one analyzer. The analyzer is approved for use in living donor serum and plasma and in cadaveric serum. Prior to going live, we found that there was limited guidance on laboratory quality assurance programs related to donor testing. In addition to confirming assay analytical performance, we designed and implemented a comprehensive quality assurance program prior to satisfy compliance with regulatory guidelines, addressing proficiency compliance, sample TAT, and control ranges, amongst other quality indicators. Methods Inter and Intraday precision was assessed through replicate analysis of positive and negative controls, where 10 replicates of both controls were assayed on a single day, and one replicate of each control was assayed daily for 7 distinct days, results were represented as %CV. Result accuracy was assessed through split sample analysis of known serologically positive (n=20) and negative samples (n=40). Each sample was assayed on two distinct lots of reagents, each with its own unique calibration curve. Accuracy was confirmed in both living donor (n=30), and cadaveric samples (n=10). In addition, split sample analysis was performed between the Alinity S and FDA-approved donor testing assays in a reference laboratory (N=7). A carryover study was performed through the analysis of triplicate replicates of low, high, and then low controls. These analyses were performed for all 6 assays. Results Intraday precision using assay general controls afforded the following %CV: HIV: 3.1%, 6.5%; Anti-HBc: 1.3%, 2.6%; Anti-HCV: 3.3%, 11.7%; HTLV-I,II: 2.5%, 5.9%; T.Cruzi: 2.8%, 0%; HBsAg: 3.2%, 11.6%. Interday precision yielded the following in %CV: HIV: 2.9%, 10.0%; Anti-HBc: 3.0%, 5.6%; Anti-HCV: 3.3%, 3.4%; HTLV-I,II: 2.8%, 4.6%; T.Cruzi: 2.4%, 0%; HBsAg: 4.7%, 7.8%. Accuracy studies yielded 100% positive and negative correlation for all 6 assays in both living donor and cadaveric samples. In addition, there was a 100% serological correlation for samples compared to reference laboratories. Carryover studies showed no evidence of sample carryover. Conclusions The Abbott Alinity S shows acceptable assay precision on all 6 validated assays. The assays are serologically accurate in both cadaveric and living donor samples. As such, the assays and platform are acceptable for use in donor screening. We designed a unique quality assurance program including a comprehensive self-proficiency and instrument crosscheck program to ensure analyzer accuracy, as well as adherence to regulatory guidelines.

Bullous Pemphigoid Severity and Levels of Antibodies to BP180 and BP230

The correlation between serum levels of autoantibodies against bullous pemphigoid (BP) antigens 180 (BP180) and 230 (BP230) with BP disease severity is unclear. To investigate the correlation of anti-BP180 and anti-BP230 immunoglobulin G (IgG) antibody levels with BP disease severity. A search was performed of the Cochrane Central Register of Controlled Trials, Embase, and PubMed databases from their respective inception to April 11, 2024. Studies evaluating the correlation between serum levels of anti-BP180 or anti-BP230 IgG measured using enzyme-linked immunosorbent assay (ELISA) and disease severity assessed per the Autoimmune Bullous Skin Disorder Intensity Score (ABSIS) or BP Disease Area Index (BPDAI) were included. No language or geographic restrictions were imposed. Nearly 0.4% of initially identified studies met the selection criteria. One researcher extracted data and another researcher confirmed data. The risk of bias was independently assessed by these researchers using the Quality Assessment of Diagnostic Accuracy Studies 2 tool, with discrepancies resolved by discussion with a third researcher. A random-effects model meta-analysis and a subgroup analysis were conducted based on the ELISA kit manufacturers. Pooled correlation coefficients of antibody levels with ABSIS and BPDAI. In all, 14 studies with 1226 participants were analyzed. The risk of bias of included studies was generally low. The meta-analysis found anti-BP180 autoantibody levels showed moderate correlation with objective BPDAI (r = 0.56; 95% CI, 0.46-0.64) at baseline, strong correlation (r = 0.63; 95% CI, 0.39-0.79) at 3-month follow-up, and moderate correlation (r = 0.53; 95% CI, 0.25-0.72) at 6-month follow-up. Anti-BP180 autoantibody levels also showed moderate correlation (r = 0.52; 95% CI, 0.39-0.62) with ABSIS at baseline, strong correlation (r = 0.62; 95% CI, 0.39-0.79) at 3-month follow-up, and moderate correlation (r = 0.53; 95% CI, 0.25-0.72) at 6-month follow-up. By contrast, anti-BP230 autoantibody levels showed no association with objective BPDAI and ABSIS at diagnosis and follow-up. The subgroup analysis found similar results when using different ELISA kits. The findings of this systematic review and meta-analysis indicated that anti-BP180 autoantibody levels may serve as an adjunctive tool for monitoring BP disease severity and guiding clinical care for patients with BP.

A-272 Evaluation of a Real-Time Multiplex Polymerase Chain Reaction Assay for Simultaneous Detection of Respiratory Pathogens in Nasopharyngeal Specimens

Abstract Background Respiratory infections present a significant challenge due to the diverse range of pathogens involved and the possibility of concurrent infections, emphasizing the importance of rapid and accurate detection for effective treatment. Advancements in molecular methods have provided cost-effective and reliable solutions for the identification of respiratory pathogens. In this study, we aimed to assess performance characteristics of the Seegene Allplex™ 2019-nCoV Assay and Seegene Novaplex™ Respiratory Panel Assays for simultaneous amplification and detection of 22 respiratory pathogens using real-time multiplex polymerase chain reaction (PCR). Methods Nucleic acid extraction was performed using the MagMAX™ Viral/Pathogen Nucleic Acid Isolation Kit (Thermo Fisher Scientific, MA) on the CyBio® FeliX liquid handler (Analytik Jena, Germany). PCR amplification and detection were performed using the Seegene Novaplex™ Respiratory Panels 1A, 2, 3, PneumoBacter Assay, and Allplex™ 2019-nCoV Assay (Seegene Inc, South Korea) on the CFX96™ System (Bio-Rad Laboratories, CA). The assays utilize Seegene MuDT™ technology that allows for the detection of multiple targets in a single channel without the need for melt curve analysis. For method validation, precision studies were performed within-run and over 4 days. The limit of detection was verified by using plasmids from Seegene containing the target sequence for each pathogen at known concentrations. Method comparison studies were carried out by extracting and analyzing a range of 41 to 79 split samples for each pathogen against the BioFire® Respiratory 2.1 Panel (bioMérieux, France), cobas® Influenza A/B & RSV Assay on the cobas® liat system (Roche Diagnostics, IN), and Lyra® SARS-CoV-2 Assay (Quidel, CA) on the CFX96™ system. Additional accuracy studies were performed by spiking negative patient samples with standards from ZeptoMetrix® (Antylia Scientific, IL) containing known concentrations of the targeted respiratory pathogens. Results Precision studies demonstrated consistent and reproducible results for all pathogens. The limit of detection was verified at 1000 copies/reaction for human parainfluenza virus 4 and human metapneumovirus, and at 100 copies/reaction for all other pathogens. Results of the spike-and-recovery studies were consistent with expected results for all targets. Patient correlation studies showed 100% agreement with expected pathogen identification for 19 pathogens, and greater than 97.9% agreement for influenza B virus, respiratory syncytial virus, human parainfluenza virus 4, and human coronavirus 229E. Conclusions The Seegene Allplex™ 2019-nCoV Assay and Novaplex™ Respiratory Panel Assays offer a rapid and reliable method for detecting respiratory pathogens in nasopharyngeal specimens. The capability of the assays to detect multiple targets in a single well enhances efficiency, cost-effectiveness, and turnaround time.

Meta-Analysis of Diagnostic Accuracy Studies With Multiple Thresholds: Comparison of Approaches in a Simulation Study.

The development of methods for the meta-analysis of diagnostic test accuracy (DTA) studies is still an active area of research. While methods for the standard case where each study reports a single pair of sensitivity and specificity are nearly routinely applied nowadays, methods to meta-analyze receiver operating characteristic (ROC) curves are not widely used. This situation is more complex, as each primary DTA study may report on several pairs of sensitivity and specificity, each corresponding to a different threshold. In a case study published earlier, we applied a number of methods for meta-analyzing DTA studies with multiple thresholds to a real-world data example (Zapf etal., Biometrical Journal. 2021; 63(4): 699-711). To date, no simulation study exists that systematically compares different approaches with respect to their performance in various scenarios when the truth is known. In this article, we aim to fill this gap and present the results of a simulation study that compares three frequentist approaches for the meta-analysis of ROC curves. We performed a systematic simulation study, motivated by an example from medical research. In the simulations, all three approaches worked partially well. The approach by Hoyer and colleagues was slightly superior in most scenarios and is recommended in practice.

A prospective blinded diagnostic accuracy study to assess the accuracy of fetal weight estimation at term by clinical method using Leopold’s maneuvers and ultrasonography in predicting the actual birth weight

ABSTRACT Context: Over-medicalization of normal pregnancy is associated with unnecessary inductions of labor leading to operative deliveries. In a busy outpatient department (OPD) or low-resource setup where ultrasound availability, experience, and cost will be a concern, routine ultrasound at term can be avoided if the clinical estimation of fetal weight is more or less equal to the actual birth weight. Aims: To assess the reliability and validity of fetal weight estimation at term by ultrasound and clinical estimation. Settings and Design: A prospective study of diagnostic accuracy was done over a period of 1.5 years in a tertiary hospital, which included 100 women with term singleton pregnancies admitted in obstetric wards or labor room. Methods and Material: To find out the agreement between actual birthweight and ultrasound fetal weight interclass correlation coefficient (ICC) was used. Mean difference plots (Bland–Altman plots) were done. The mean percentage error was compared with the consultant estimate and registrar estimate across groups. The sensitivity, specificity, and predictive values by clinical and ultrasound estimate were calculated for actual birth weight ≤ 2.5 kg. Results: Both methods of fetal weight estimation have moderate reliability in predicting the actual birth weight. The sensitivity of identifying birthweight < 2.5 kg babies by ultrasound was slightly higher. For birth weight less than 2.5 kg, there was an overestimation of fetal weight by clinical estimation done by postgraduate with mean percentage error being statistically significant as compared with ultrasound estimation. Conclusions: In a developing country like ours where ultrasound is not readily available in all healthcare setups, the clinical method is an easy, cost-effective, simple one that can be used by all medical professionals after adequate training.

Accuracy and Computational Cost Comparison of Numerical Flight Dynamics Reduced-Order Models

This paper compares several approaches to model the unsteady and nonlinear longitudinal aerodynamics of a generic unmanned combat air vehicle configuration based on computational fluid dynamics (CFD). Three different reduced-order models (ROMs) are implemented through unsteady simulations to predict the evolution of forces and moments during prescribed trajectories carried out at M=0.15. The first model investigates a linear quasi-steady model based on a first-order Taylor series expansion of the aerodynamic coefficients. The second one picks up the mathematical formulation of the first model but adds a dynamic coefficient in order to take into account the unsteady effect of rotation rate variations. Finally, the third model is built on indicial functions computed from positive step changes in the angle of attack or pitch rate. The unknown parameters of the first two models and the unknown indicial functions of the third one are obtained, respectively, from the study of forced oscillations and step motions. These prescribed trajectories are performed using unsteady Reynolds-averaged Navier–Stokes simulations coupled with a grid displacement tool. Two different methods are tested and compared to carry out the CFD mesh motions: an overset method and a full moving grid (FMG) method. The indicial functions obtained using the FMG method give results equivalent to those found in the literature in half the time of the overset method. To reduce the computational costs to set up the ROMs, the FMG method is preferred to the overset method. ROMs are then applied, and their predictions are compared with CFD simulations for prescribed trajectories with different angular rates and angles of attack. A study of instantaneous and overall accuracy associated with the implementation cost of the ROMs has allowed to show their strengths and their weaknesses. Similar results are observed at low angles of attack for the ROMs studied, while a major modeling advantage of the indicial method has been identified for higher angles of attack in the nonlinear domain.

Diagnostic value of a magnetic resonance imaging (MRI)-based vertebral bone quality score for bone mineral density assessment: an updated systematic review and meta-analysis.

Many studies have explored the application of the magnetic resonance imaging (MRI)-based vertebral bone quality (VBQ) score as a tool for opportunistic osteoporosis screening before spine surgery. We aimed to conduct a systematic review and meta-analysis of studies to determine the diagnostic value of the VBQ score for bone mineral density (BMD) assessment. The PubMed, EBSCO, Ovid, Web of Science, Cochrane Library, Wanfang, China National Knowledge Infrastructure, Chinese Science and Technology Periodical databases were searched to retrieve original studies on VBQ and BMD published from inception to 31 December 2023 with no restrictions on language and the reference standard of BMD. The quality of the included articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Summary accuracy metrics were obtained by bivariate random-effects meta-analysis. The area under the curve (AUC) of the summary receiver operating characteristic (SROC) analysis was used to evaluate the performance of the VBQ score. Meta-regression and subgroup analyses were performed to examine the source of heterogeneity. In total, 23 studies, comprising 2981 patients, from 2022 to 2023, of high- or medium-to-high scientific quality were included in the meta-analysis. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of the VBQ score in the assessment of BMD were 0.77 [95% confidence interval (CI), 0.73-0.81; P<0.001], 0.65 (95% CI, 0.59-0.71; P<0.001), and 6.49 (95% CI, 4.82-8.73; P<0.001), respectively, with an area under the SROC curve of 0.78 (95% CI, 0.74-0.82; P<0.001). The presence of heterogeneity was found to have a significant association with factors such as the reference standard, sex, mean age, region of publication, VBQ method, and magnet field strength. The MRI-based VBQ score has some diagnostic value in detecting osteoporosis. The VBQ score could be used as a tool for opportunistic osteoporosis screening before spine surgery.

Workflow improvement and financial gain after integration of high-throughput sample processing system with flow cytometer in a high-volume pathology laboratory: Results from a prospective comparative study using Lean principles

BackgroundHighly efficient clinical laboratories are essential for monitoring many human illnesses. Ampath Laboratory Services, the largest pathology lab in South Africa, analyzes large numbers of peripheral blood samples for CD4 levels yearly. ObjectiveTo assess productivity and quality of a newer integrated automated solution, the BD FACSDuet™ Sample Preparation System/BD FACSLyric™ Flow Cytometer using conventional assessment methods and Lean concepts. Materials and methodsThis prospective study compared the performance of the BD FACS™ Sample Preparation Assistant [SPA] III and BD FACSCanto™ II Flow Cytometer with the newly introduced integrated system (BD FACSDuet™/BD FACSLyric™). They were validated for accuracy, precision, and external quality assessment. Process mapping and Lean assessment helped identify steps leading to waste. An economic model was developed to characterize workflow and economic impact associated with total daily hands-on time, processing time, and reworks. ResultsStrong linear correlation was present between both systems. Precision and accuracy studies revealed that all coefficient of variation (CV)% values were below 20 % of allowable limits. External proficiency assessments were within limits. The fully automated workflow of BD FACSDuet™/BD FACSLyric™ permitted better consistency with significantly shorter processing time and batch processing and reduced operator interventions. Lean assessment identified defects with motion, over-processing, waiting, and non-utilized talent. Significant reductions in hands-on and total daily processing time that could increase daily specimen testing efficiency and fewer reworks were associated with the BD FACSDuet™/BD FACSLyric™. Lean improvements translated to significant economic savings associated with operator costs and unnecessary reagent consumption. ConclusionBD FACSDuet™/BD FACSLyric™ is an accurate, reliable, and cost-effective fully automated system for high-volume flow cytometry labs that perform T-cell enumeration using a single-platform and single-tube approach.

Examining Historical Data to Inform Future Preparedness: Systematic Review of the Effects of Prone Positioning on Oxygen Levels in Nonintubated COVID-19 Patients

Abstract Over time, there has been a significant increase in the utilization of prone positioning (PP) as a strategy to improve oxygen levels in patients requiring mechanical ventilation for the management of acute respiratory distress syndrome. PP has emerged as a potential intervention for enhancing oxygenation in nonintubated patients with COVID-19. The current systematic review aims to assess existing evidence regarding the impact of PP on oxygen parameters in this patient population. Understanding the effectiveness and possible advantages of PP in optimizing oxygenation could contribute to enhanced management strategies for COVID-19 cases, particularly those that do not require invasive mechanical ventilation. Comprehensive search was conducted using electronic databases to identify the relevant studies, covering the period of 2019–2023. A two-part tool was employed to address five specific domains alongside other potential biases. Quality assessment of diagnostic accuracy studies was undertaken using a risk of bias. In a present systematic review, 15 studies were included. The findings suggest that PP had a notable influence on oxygen parameters. The PP was a beneficial intervention for nonintubated patients of COVID-19 in terms of improving oxygen parameters. The evidence suggests that PP can contribute to enhanced oxygenation in patients. Further research is necessary for a better understanding of optimal techniques, patient selection criteria, and long-term outcomes linked with PP.

Cholangioscopy-based convoluted neuronal network vs. confocal laser endomicroscopy in identification of neoplastic biliary strictures.

Background and study aims Artificial intelligence (AI) models have demonstrated high diagnostic performance identifying neoplasia during digital single-operator cholangioscopy (DSOC). To date, there are no studies directly comparing AI vs. DSOC-guided probe-base confocal laser endomicroscopy (DSOC-pCLE). Thus, we aimed to compare the diagnostic accuracy of a DSOC-based AI model with DSOC-pCLE for identifying neoplasia in patients with indeterminate biliary strictures. Patients and methods This retrospective cohort-based diagnostic accuracy study included patients ≥ 18 years old who underwent DSOC and DSOC-pCLE (June 2014 to May 2022). Four methods were used to diagnose each patient's biliary structure, including DSOC direct visualization, DSOC-pCLE, an offline DSOC-based AI model analysis performed in DSOC recordings, and DSOC/pCLE-guided biopsies. The reference standard for neoplasia was a diagnosis based on further clinical evolution, imaging, or surgical specimen findings during a 12-month follow-up period. Results A total of 90 patients were included in the study. Eighty-six of 90 (95.5%) had neoplastic lesions including cholangiocarcinoma (98.8%) and tubulopapillary adenoma (1.2%). Four cases were inflammatory including two cases with chronic inflammation and two cases of primary sclerosing cholangitis. Compared with DSOC-AI, which obtained an area under the receiver operator curve (AUC) of 0.79, DSOC direct visualization had an AUC of 0.74 ( P = 0.763), DSOC-pCLE had an AUC of 0.72 ( P = 0.634), and DSOC- and pCLE-guided biopsy had an AUC of 0.83 ( P = 0.809). Conclusions The DSOC-AI model demonstrated an offline diagnostic performance similar to that of DSOC-pCLE, DSOC alone, and DSOC/pCLE-guided biopsies. Larger multicenter, prospective, head-to-head trials with a proportional sample among neoplastic and nonneoplastic cases are advisable to confirm the obtained results.

Development and evaluation of machine-learning methods in whole-body magnetic resonance imaging with diffusion weighted imaging for staging of patients with cancer: the MALIBO diagnostic test accuracy study

Background Whole-body magnetic resonance imaging is accurate, efficient and cost-effective for cancer staging. Machine learning may support radiologists reading whole-body magnetic resonance imaging. Objectives To develop a machine-learning algorithm to detect normal organs and cancer lesions. To compare diagnostic accuracy, time and agreement of radiology reads to detect metastases using whole-body magnetic resonance imaging with concurrent machine learning (whole-body magnetic resonance imaging + machine learning) against standard whole-body magnetic resonance imaging (whole-body magnetic resonance imaging + standard deviation). Design and participants Retrospective analysis of (1) prospective single-centre study in healthy volunteers &gt; 18 years (n = 51) and (2) prospective multicentre STREAMLINE study patient data (n = 438). Tests Index: whole-body magnetic resonance imaging + machine learning. Comparator: whole-body magnetic resonance imaging + standard deviation. Reference standard Previously established expert panel consensus reference at 12 months from diagnosis. Outcome measures Primary: difference in per-patient specificity between whole-body magnetic resonance imaging + machine learning and whole-body magnetic resonance imaging + standard deviation. Secondary: per-patient sensitivity, per-lesion sensitivity and specificity, read time and agreement. Methods Phase 1: classification forests, convolutional neural networks, and a multi-atlas approaches for organ segmentation. Phase 2/3: whole-body magnetic resonance imaging scans were allocated to Phase 2 (training = 226, validation = 45) and Phase 3 (testing = 193). Disease sites were manually labelled. The final algorithm was applied to 193 Phase 3 cases, generating probability heatmaps. Twenty-five radiologists (18 experienced, 7 inexperienced in whole-body magnetic resonance imaging) were randomly allocated whole-body magnetic resonance imaging + machine learning or whole-body magnetic resonance imaging + standard deviation over two or three rounds in a National Health Service setting. Read time was independently recorded. Results Phases 1 and 2: convolutional neural network had best Dice similarity coefficient, recall and precision measurements for healthy organ segmentation. Final algorithm used a ‘two-stage’ initial organ identification followed by lesion detection. Phase 3: evaluable scans (188/193, of which 50 had metastases from 117 colon, 71 lung cancer cases) were read between November 2019 and March 2020. For experienced readers, per-patient specificity for detection of metastases was 86.2% (whole-body magnetic resonance imaging + machine learning) and 87.7% (whole-body magnetic resonance imaging + standard deviation), (difference −1.5%, 95% confidence interval −6.4% to 3.5%; p = 0.387); per-patient sensitivity was 66.0% (whole-body magnetic resonance imaging + machine learning) and 70.0% (whole-body magnetic resonance imaging + standard deviation) (difference −4.0%, 95% confidence interval −13.5% to 5.5%; p = 0.344). For inexperienced readers (53 reads, 15 with metastases), per-patient specificity was 76.3% in both groups with sensitivities of 73.3% (whole-body magnetic resonance imaging + machine learning) and 60.0% (whole-body magnetic resonance imaging + standard deviation). Per-site specificity remained high within all sites; above 95% (experienced) or 90% (inexperienced). Per-site sensitivity was highly variable due to low number of lesions in each site. Reading time lowered under machine learning by 6.2% (95% confidence interval −22.8% to 10.0%). Read time was primarily influenced by read round with round 2 read times reduced by 32% (95% confidence interval 20.8% to 42.8%) overall with subsequent regression analysis showing a significant effect (p = 0.0281) by using machine learning in round 2 estimated as 286 seconds (or 11%) quicker. Interobserver variance for experienced readers suggests moderate agreement, Cohen’s κ = 0.64, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + machine learning) and Cohen’s κ = 0.66, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + standard deviation). Limitations Patient whole-body magnetic resonance imaging data were heterogeneous with relatively few metastatic lesions in a wide variety of locations, making training and testing difficult and hampering evaluation of sensitivity. Conclusions There was no difference in diagnostic accuracy for whole-body magnetic resonance imaging radiology reads with or without machine-learning support, although radiology read time may be slightly shortened using whole-body magnetic resonance imaging + machine learning. Future work Failure-case analysis to improve model training, automate lesion segmentation and transfer of machine-learning techniques to other tumour types and imaging modalities. Study registration This study is registered as ISRCTN23068310. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/122/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 15. See the NIHR Funding and Awards website for further award information.

Using the GoogLeNet deep-learning model to distinguish between benign and malignant breast masses based on conventional ultrasound: a systematic review and meta-analysis.

Breast cancer is one of the most common malignancies in women worldwide, and early and accurate diagnosis is crucial for improving treatment outcomes. Conventional ultrasound (CUS) is a widely used screening method for breast cancer; however, the subjective nature of interpreting the results can lead to diagnostic errors. The current study sought to estimate the effectiveness of using a GoogLeNet deep-learning convolutional neural network (CNN) model to identify benign and malignant breast masses based on CUS. A literature search was conducted of the Embase, PubMed, Web of Science, Wanfang, China National Knowledge Infrastructure (CNKI), and other databases to retrieve studies related to GoogLeNet deep-learning CUS-based models published before July 15, 2023. The diagnostic performance of the GoogLeNet models was evaluated using several metrics, including pooled sensitivity (PSEN), pooled specificity (PSPE), the positive likelihood ratio (PLR), the negative likelihood ratio (NLR), the diagnostic odds ratio (DOR), and the area under the curve (AUC). The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies Scale (QUADAS). The eligibility of the included literature were independently searched and assessed by two authors. All of the 12 studies that used pathological findings as the gold standard were included in the meta-analysis. The overall average estimation of sensitivity and specificity was 0.85 [95% confidence interval (CI): 0.80-0.89] and 0.86 (95% CI: 0.78-0.92), respectively. The PLR and NLR were 6.2 (95% CI: 3.9-9.9) and 0.17 (95% CI: 0.12-0.23), respectively. The DOR was 37.06 (95% CI: 20.78-66.10). The AUC was 0.92 (95% CI: 0.89-0.94). No obvious publication bias was detected. The GoogLeNet deep-learning model, which uses a CNN, achieved good diagnostic results in distinguishing between benign and malignant breast masses in CUS-based images.

Patellar tendinopathy: a systematic review of diagnostic accuracy studies

Patellar tendinopathy: a systematic review of diagnostic accuracy studies

Ultrasound is an accurate imaging modality for diagnosing hip luxation in dogs presenting with hind limb lameness.

To determine the accuracy of ultrasound for diagnosing hip luxation in dogs presenting for hind limb lameness. 24 client-owned dogs presenting with pelvic limb lameness and concern for hip luxation were enrolled in this prospective diagnostic accuracy study from April 1, 2021, to July 1, 2022. An experienced ultrasonographer and a novice ultrasonographer, both masked to the diagnoses, performed hip ultrasonography using a point-of-care ultrasound machine. The experienced evaluator also performed a more comprehensive ultrasonography with a high-end ultrasound machine to characterize concurrent pelvis and hip disease. Pelvic radiographs served as the criterion standard for the diagnosis of hip luxation. Ultrasonographic diagnosis of hip luxation demonstrated high accuracy, ranging from 84.2% to 100% for detecting the presence of luxation and 80.6% to 98.1% for diagnosing the direction of luxation for the novice and experienced evaluators, respectively, with substantial interevaluator agreement (κ = 0.722). Compared to radiographs, ultrasound accurately diagnosed the presence of osteoarthrosis with almost perfect agreement (κ = 0.913) and the presence of fractures of the femoral head/neck and the nonaxial portions of the pelvis with substantial intermodality agreement (κ = 0.775). In 36% of patients, ultrasound identified injury to soft tissue structures that contribute to hip stability, including the gluteal muscles, gemelli, and joint capsule. Ultrasound can be utilized to accurately diagnose the presence of hip luxation and document concurrent hip and pelvic disease. Applications include the use of ultrasonography as part of a routine point-of-care protocol to catalogue injuries in patients with acute trauma and in mobile practice or remote settings where radiography is not readily available.

The American Cancer Society National Lung Cancer Roundtable strategic plan: Optimizing strategies for lung nodule evaluation and management.

Lung nodules are frequently detected on low-dose computed tomography scans performed for lung cancer screening and incidentally detected on imaging performed for other reasons. There is wide variability in how lung nodules are managed by general practitioners and subspecialists, with high rates of guideline-discordant care. This may be due in part to the level of evidence underlying current practice guideline recommendations (primarily based on findings from uncontrolled studies of diagnostic accuracy). The primary aims of lung nodule management are to minimize harms of diagnostic evaluations while expediting the evaluation, diagnosis, and treatment of lung cancer. Potentially useful tools such as lung cancer probability calculators, automated methods to identify patients with nodules in the electronic health record, and multidisciplinary team evaluation are often underused due to limited availability, accessibility, and/or provider knowledge. Finally, relatively little attention has been paid to identifying and reducing disparities among individuals with screening-detected or incidentally detected lung nodules. This contribution to the American Cancer Society National Lung Cancer Roundtable Strategic Plan aims to identify and describe these knowledge gaps in lung nodule management and propose recommendations to advance clinical practice and research. Major themes that are addressed include improving the quality of evidence supporting lung nodule evaluation guidelines, strategically leveraging information technology, and placing emphasis on equitable approaches to nodule management. The recommendations outlined in this strategic plan, when carried out through interdisciplinary efforts with a focus on health equity, ultimately aim to improve early detection and reduce the morbidity and mortality of lung cancer. PLAIN LANGUAGE SUMMARY: Lung nodules may be identified on chest scans of individuals who undergo lung cancer screening (screening-detected nodules) or among patients for whom a scan was performed for another reason (incidental nodules). Although the vast majority of lung nodules are not lung cancer, it is important to have evidence-based, standardized approaches to the evaluation and management of a lung nodule. The primary aims of lung nodule management are to diagnose lung cancer while it is still in an early stage and to avoid unnecessary procedures and other harms.

Risk-prediction models in postmenopausal patients with symptoms of suspected ovarian cancer in the UK (ROCkeTS): a multicentre, prospective diagnostic accuracy study

Multiple risk-prediction models are used in clinical practice to triage patients as being at low risk or high risk of ovarian cancer. In the ROCkeTS study, we aimed to identify the best diagnostic test for ovarian cancer in symptomatic patients, through head-to-head comparisons of risk-prediction models, in a real-world setting. Here, we report the results for the postmenopausal cohort. In this multicentre, prospective diagnostic accuracy study, we recruited newly presenting female patients aged 16-90 years with non-specific symptoms and raised CA125 or abnormal ultrasound results (or both) who had been referred via rapid access, elective clinics, or emergency presentations from 23 hospitals in the UK. Patients with normal CA125 and simple ovarian cysts of smaller than 5 cm in diameter, active non-ovarian malignancy, or previous ovarian malignancy, or those who were pregnant or declined a transvaginal scan, were ineligible. In this analysis, only postmenopausal participants were included. Participants completed a symptom questionnaire, gave a blood sample, and had transabdominal and transvaginal ultrasounds performed by International Ovarian Tumour Analysis consortium (IOTA)-certified sonographers. Index tests were Risk of Malignancy 1 (RMI1) at a threshold of 200, Risk of Malignancy Algorithm (ROMA) at multiple thresholds, IOTA Assessment of Different Neoplasias in the Adnexa (ADNEX) at thresholds of 3% and 10%, IOTA SRRisk model at thresholds of 3% and 10%, IOTA Simple Rules (malignant vs benign, or inconclusive), and CA125 at 35 IU/mL. In a post-hoc analysis, the Ovarian Adnexal and Reporting Data System (ORADS) at 10% was derived from IOTA ultrasound variables using established methods since ORADS was described after completion of recruitment. Index tests were conducted by study staff masked to the results of the reference standard. The comparator was RMI1 at the 250 threshold (the current UK National Health Service standard of care). The reference standard was surgical or biopsy tissue histology or cytology within 3 months, or a self-reported diagnosis of ovarian cancer at 12 month follow-up. The primary outcome was diagnostic accuracy at predicting primary invasive ovarian cancer versus benign or normal histology, assessed by analysing the sensitivity, specificity, C-index, area under receiver operating characteristic curve, positive and negative predictive values, and calibration plots in participants with conclusive reference standard results and available index test data. This study is registered with the International Standard Randomised Controlled Trial Number registry (ISRCTN17160843). Between July 13, 2015, and Nov 30, 2018, 1242 postmenopausal patients were recruited, of whom 215 (17%) had primary ovarian cancer. 166 participants had missing, inconclusive, or other reference standard results; therefore, data from a maximum of 1076 participants were used to assess the index tests for the primary outcome. Compared with RMI1 at 250 (sensitivity 82·9% [95% CI 76·7 to 88·0], specificity 87·4% [84·9 to 89·6]), IOTA ADNEX at 10% was more sensitive (difference of -13·9% [-20·2 to -7·6], p<0·0001) but less specific (difference of 28·5% [24·7 to 32·3], p<0·0001). ROMA at 29·9 had similar sensitivity (difference of -3·6% [-9·1 to 1·9], p=0·24) but lower specificity (difference of 5·2% [2·5 to 8·0], p=0·0001). RMI1 at 200 had similar sensitivity (difference of -2·1% [-4·7 to 0·5], p=0·13) but lower specificity (difference of 3·0% [1·7 to 4·3], p<0·0001). IOTA SRRisk model at 10% had similar sensitivity (difference of -4·3% [-11·0 to -2·3], p=0·23) but lower specificity (difference of 16·2% [12·6 to 19·8], p<0·0001). IOTA Simple Rules had similar sensitivity (difference of -1·6% [-9·3 to 6·2], p=0·82) and specificity (difference of -2·2% [-5·1 to 0·6], p=0·14). CA125 at 35 IU/mL had similar sensitivity (difference of -2·1% [-6·6 to 2·3], p=0·42) but higher specificity (difference of 6·7% [4·3 to 9·1], p<0·0001). In a post-hoc analysis, when compared with RMI1 at 250, ORADS achieved similar sensitivity (difference of -2·1%, 95% CI -8·6 to 4·3, p=0·60) and lower specificity (difference of 10·2%, 95% CI 6·8 to 13·6, p<0·0001). In view of its higher sensitivity than RMI1 at 250, despite some loss in specificity, we recommend that IOTA ADNEX at 10% should be considered as the new standard-of-care diagnostic in ovarian cancer for postmenopausal patients. UK National Institute of Heath Research.

Magnetic Resonance Imaging-Based Vertebral Bone Quality Score for Prediction of Cage Subsidence and Screw Loosening in Patients Undergoing Degenerative Lumbar Surgery: A Meta-analysis.

Poor bone quality is a risk factor for postoperative complications after degenerative lumbar fusion surgery. The magnetic resonance imaging-based vertebral bone quality (VBQ) score is a good tool for assessing bone quality, and this is the first meta-analysis performed to summarize the predictive value of the VBQ score for cage subsidence and screw loosening in patients undergoing degenerative lumbar surgery. Studies were comprehensively searched in electronic databases. The quality of the studies was assessed. The pooled sensitivity, specificity and summary receiver operating characteristic curve were calculated. Publication bias was assessed and meta-regression was conducted. We ultimately included 9 studies with a total of 1,404 patients with a mean age of 60.4 years and a percentage of females of 57.0%. According to the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to assess methodological quality, the quality of the included studies was relatively low and risks of bias might exist. Results showed that a high VBQ was significantly associated with cage subsidence and screw loosening, and risk factor analysis revealed that the merged odds ratio was 5.37 for cage subsidence and 3.87 for screw loosening. With a VBQ cutoff value of 3.34±0.45, the pooled sensitivity and specificity for the diagnosis of postoperative complications were 0.75 and 0.75, respectively, and the area under the curve was 0.82 (95% confidence interval, 0.78-0.85). A high VBQ was associated with a high risk of cage subsidence and screw loosening in patients who underwent degenerative lumbar surgery. The VBQ score could be considered for identifying high-risk patients for further evaluation.

White blood cell Indices to predict first trimester missed miscarriage.

To identify the relationship between white blood cell indices in missed miscarriage compared to ongoing pregnancy in the first trimester. The diagnostic accuracy study was conducted at the Department of Maternity, Al-Yarmouk Hospital, Baghdad, Iraq, from January to October 2021,and comprised healthy first-trimester pregnant women in group A and those with missed miscarriage after 7-13 weeks of gestation in group B. Ultrasound findings and the last menstrual period were noted for all the subjects. Total and differential leukocyte counts, neutrophil-to-lymphocyte ratio and monocyte-to-lymphocyte ratio were compared between the groups. Data was analysed using SPSS 22. Of the 167 women, 81(48.5%) were in group A and 86(51.5%) were in group B. Demographic and obstetrics data was not significantly different between the groups (p>0.05). Mean white blood cell, neutrophil and monocyte counts showed no significant difference between the groups (p>0.05), while lymphocyte count, neutrophil-tolymphocyte ratio and monocyte-to-lymphocyte ratio were significantly different between the groups (p<0.05). With respect to gestational age, neutrophil-to-lymphocyte ratio and monocyte-to-lymphocyte ratio had a fair ability to predict missed miscarriage at various time points within the first trimester (p<0.05). There was no significant correlation between WBC count and gestational age. Reduced MLR and NLR levels can be utilised to forecast miscarriages in a cost-effective manner.