Diagnostic Accuracy Research Articles

Management of acute chest pain in the Emergency Department: benefits of coronary computed tomography angiography.

Acute chest pain (ACP) is a frequent cause of Emergency Department (ED) admissions, with millions of cases reported globally each year. Timely and accurate diagnosis is crucial due to the wide range of underlying causes, such as acute coronary syndrome (ACS), pulmonary embolism (PE), aortic dissection, and others. Misdiagnosis can lead to missed life-saving interventions or, alternatively, unnecessary hospitalizations, escalating healthcare costs. While risk stratification tools like the HEART, GRACE, and TIMI scores are useful, additional imaging is often required to achieve diagnostic precision. Coronary computed tomography angiography (CCTA) has shown significant potential in enhancing diagnostic accuracy and improving patient outcomes. In this review, we explore the challenges physicians encounter when evaluating ACP in the ED, emphasizing the utility of CCTA as a key diagnostic tool. Additionally, we present a clinical case that illustrates how CT scan effectively aids in diagnosing patients with ambiguous symptoms, with CT imaging playing a pivotal role in identifying the underlying pathology.

National Institute of Health and Care Excellence Clinical Criteria for the Diagnosis of Knee Osteoarthritis: A Prospective Diagnostic Accuracy Study in Individuals with Type 2 Diabetes.

The National Institute of Health and Care Excellence (NICE) criteria for osteoarthritis (OA) obviate the need for physical exam or imaging, and their use may improve timely diagnosis of OA. However, they have not been validated. Within a larger study of individuals with type 2 diabetes, participants with and without self-reported knee pain underwent assessment of the NICE criteria for knee OA by questionnaire (index test), and clinical evaluation for established or possible knee OA by a rheumatologist (reference standard). We calculated the sensitivity, specificity, likelihood ratio positive (LR+) and likelihood ratio negative (LR-) of the NICE criteria and modified NICE criteria without the stiffness criterion. Our study included 96 participants: mean age 65.4 (SD 8.3) years and 52% were women. Individuals who fulfilled NICE criteria for knee OA (55.2%) included a spectrum of pain severity on a 11-point pain numeric rating scale with a median score of 5 (range: 1-9). Rheumatologist assessment identified 56 (58.3%) participants with symptomatic knee OA. The sensitivity, specificity, LR+, and LR- of NICE criteria for symptomatic knee OA were 0.84 (95% CI 0.74, 0.94), 0.85 (95% CI 0.74, 0.96), 5.6 and 0.19, respectively. For modified NICE criteria, these were 0.89 (95% CI 0.82, 0.97), 0.85 (95% CI 0.74, 0.96), 5.93 and 0.13. The NICE criteria have high sensitivity and specificity for detecting symptomatic knee OA in a population with type 2 diabetes. We found that a modified version, omitting the stiffness criterion, performed similarly. These criteria should be validated in other settings and populations.

Acute Coronary Syndrome After Aneurysmal Subarachnoid Hemorrhage: Incidence, Risk Factors and Impact on the Outcome

Background and Objectives: Development of acute coronary syndrome (ACS) after aneurysmal subarachnoid hemorrhage (aSAH) strongly affects further neuro-intensive care management. We aimed to analyze the incidence, risk factors and clinical impact of ACS in aSAH patients. Materials and Methods: This retrospective analysis included 855 aSAH cases treated between 01/2003 and 06/2016. The occurrence of ACS during 3 weeks of aSAH was documented. Patients’ demographic, clinical, radiographic and laboratory characteristics at admission were collected as potential ACS predictors. The association between ACS and the aSAH outcome was analyzed as the occurrence of cerebral infarcts in the computed tomography scans and unfavorable outcome (modified Rankin scale > 3) at 6 months after aSAH. Univariable and multivariable analyses were performed. Results: ACS was documented in 28 cases (3.3%) in the final cohort (mean age: 54.9 years; 67.8% females). In the multivariable analysis, there was a significant association between ACS, an unfavorable outcome (adjusted odds ratio [aOR] = 3.43, p = 0.027) and a borderline significance with cerebral infarcts (aOR = 2.5, p = 0.066). The final prediction model for ACS occurrence included five independent predictors (age > 55 years [1 point], serum sodium < 142 mmol/L [3 points], blood sugar ≥ 170 mg/dL [2 points], serum creatine kinase ≥ 255 U/L [3 points] and gamma-glutamyl transferase ≥ 36 U/L [1 point]) and showed high diagnostic accuracy for ACS prediction (AUC = 0.879). Depending on the cumulative score value, the risk of ACS in the cohort varied between 0% (0 points) and 66.7% (10 points). Conclusions: ACS is a rare, but clinically very relevant, complication of aSAH. The development of ACS can reliably be predicted by the presented prediction model, which enables the early identification of aSAH individuals at high risk for ACS. External validation of the prediction model is mandatory.

Diagnostic Performance of Quantitative Flow Ratio for the Assessment of Non-Culprit Lesions in Myocardial Infarction (QFR-OUTSMART): Systematic Review and Meta-Analysis.

Quantitative flow ratio (QFR) analysis is a simple and non-invasive coronary physiological assessment method with evidence for evaluating stable coronary artery disease with correlation to fractional flow reserve (FFR). However, there is no evidence to recommend its use in non-culprit lesions (NCLs) in myocardial infarction (MI). We performed a systematic review and meta-analysis using the PRISMA and PROSPERO statements. The study's primary objective was to assess the diagnostic accuracy of QFR in identifying functionally significant NCLs after MI based on invasive FFR and non-hyperemic pressure ratios as references. We obtained values of the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We performed a leave-one-out sensitivity analysis for each study's impact on the overall effect. We included eight studies, with 713 patients and 920 vessels evaluated with QFR. The overall AUC was 0.941 (I2 = 0.559, p < 0.002), with a sensitivity of 87.3%, a specificity of 89.4%, a PPV of 86.6%, and an NPV of 90.1%. Compared to FFR, we found an AUC of 0.957 (I2 = 0.331, p < 0.194), a sensitivity of 89.6%, a specificity of 89.8%, a PPV of 88.3%, and an NPV of 91%. The sensitivity analysis showed a similar diagnostic performance in both studies. QFR is effective in analyzing NCLs with a significant diagnostic yield compared to FFR, with an excellent AUC in MI patients. Performing prospective multicenter studies to characterize this population and reproduce our results is essential.

Artificial intelligence in fracture detection on radiographs: a literature review.

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for patients, and artificial intelligence that uses algorithms to imitate human intelligence to aid or enhance human performs is a promising solution to address this issue. In the last few years, numerous commercially available algorithms have been developed to enhance radiology practice and a large number of studies apply artificial intelligence to fracture detection. Recent contributions in literature have described numerous advantages showing how artificial intelligence performs better than doctors who have less experience in interpreting musculoskeletal X-rays, and assisting radiologists increases diagnostic accuracy and sensitivity, improves efficiency, and reduces interpretation time. Furthermore, algorithms perform better when they are trained with big data on a wide range of fracture patterns and variants and can provide standardized fracture identification across different radiologist, thanks to the structured report. In this review article, we discuss the use of artificial intelligence in fracture identification and its benefits and disadvantages. We also discuss its current potential impact on the field of radiology and radiomics.

Clinical utility of administering the WISC-V as standard practice in paediatric attention-deficit/hyperactivity disorder (ADHD) assessments in a public hospital and health system: a rapid review to inform clinical practice

ABSTRACT Objective The purpose of this rapid review is to appraise the current evidence-base to inform the development of a new assessment and diagnostic pathway for ADHD presentations in a public health paediatric department in Brisbane. We posited that the standardised WISC-V administration in all ADHD presentations regardless of cognitive indication was low-value, inefficient and burdensome for clinicians, children and their families. We wanted to find out if the literature supports this position. Method This review used PRISMA systematic methods to search two relevant databases (PUBMED, and Elsevier (ClinicalKey)). Search terms were used: (attention deficit hyperactivity disorder OR ADHD) AND (children) AND (assessment OR diagnosis) AND (Australia) AND (cognitive proficiency) OR (WISC). Results Search returned 286 full text journal articles, published between 2018 and 2023. Two hundred and sixty-two were removed to irrelevance, adult population, duplications, or focus on other paediatric mental health disorders. Following the application of exclusion criteria, 10 were reviewed at full text and four were ultimately included for contribution to this rapid review. Conclusions Neuropsychological testing if no cognitive query is indicated may confound the findings. This review provided the opportunity to improve clinical efficiency without compromising diagnostic accuracy.

MULTICENTER EXTERNAL VALIDATION OF THE ACCURACY OF COMPUTED TOMOGRAPHY CRITERIA FOR DETECTING THORACOLUMBAR POSTERIOR LIGAMENTOUS COMPLEX INJURY

Background and Objective Recent studies have proposed computed tomography (CT) criteria for posterior ligamentous complex (PLC) injury: disrupted if ? 2 CT findings, indeterminate if single finding, and intact if 0 CT findings. The study aims to validate the CT criteria for PLC injury externally. Methods Three level 1 trauma centers enrolled 614 consecutive patients with acute thoracolumbar fractures (T1- L5) who received Computed tomography (CT) and magnetic resonance imaging (MRI). Three reviewers from each center were the patients from the respective center for sessed CT for facet joint malalignment, horizontal laminar fracture, spinous process fracture, and interspinous widening and MRI for disrupted PLC. The primary outcome is the diagnostic accuracy of CT criteria (0,1, ? 2 findings) in detecting disrupted PLC on MRI using all CT readings. Subgroup analysis for each participating center and reviewer was done. The inter-reader agreement on PLC status on MRI and CT criteria was assessed using Fleiss Kappa (k). Results The positive predictive value (PPV) for PLC injury was 0 findings, 3%; single positive CT, 43%; ? 2 CT findings, 94%, and was consistent among different centers and reviewers. The AUC for ? 1 CT findings in detecting PLC injury ranged from 90% to 97%, indicating excellent discrimination for all centers. The inter-reader k on PLC status by MRI and CT criteria was substantial (k &gt;0.60). Conclusions This study externally validates the previously proposed CT criteria for PLC injury. ? 2 positive CT findings or 0 CT findings can be used as criteria for a disrupted PLC (B-type injury) or intact PLC (A-type injuries), respectively, without added MRI. A single CT finding implies indeterminate PLC status and the need for further MRI assessment. The CT criteria will potentially guide MRI indications and treatment decisions for burst fractures in patients without neurological impairment.

Antemortem diagnostic tests for the detection of Aspergillus infection in birds: a systematic review.

Aspergillosis remains a common and life-threatening disease in captive and wild birds all over the world. Diagnosis is currently based on clinical signs or lesions, diagnostic imaging and a variety of biological tests. This systematic review aimed to compare the accuracy of antemortem diagnostic tests for Aspergillus infection in birds. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a search was conducted on PubMed, Scopus, Web of Science, CAB until January 2024. The methodological quality was assessed with QUADAS 2 risk of bias tool. The thirteen studies, selected for the review, included results from a wide variety of birds (mainly Spheniciformes but also Falconiformes, Psittaciformes, and Galliformes) from wildlife rehabilitation centers, zoological parks or veterinary practices. Aspergillus infection was mainly confirmed by fungal culture and/or histopathology. Serum markers included Aspergillus components (galactomannan, ß-D-glucan, mannoproteins and gliotoxin), anti-Aspergillus antibodies, 3-hydroxybutyrate as well as protein electrophoresis and acute phase molecules. Sensitivity and specificity displayed a large amount of variation despite threshold arrangement. Disparities in the number of individuals per study did not allow for reliable comparison. Platelia Ag Assay (Bio-Rad), the most commonly used test in the studies, demonstrated moderate specificity and low sensitivity. Overall, non-specific tests demonstrated more consistent performance, whereas specific tests showed greater variability. Based on current knowledge, none of these tests provide sufficient accuracy to reliably detect Aspergillus infection in birds in clinical practice.

Emerging and re-emerging viral exanthems among children: what a physician should know.

Viral exanthems can present with diverse morphologies of rash, including macular, maculopapular, papular, urticarial and vesicular, or sometimes a combination of these. There has been an increasing trend towards emerging and re-emerging viral exanthems in recent years, the cause of which is multifactorial, including changing environmental conditions and altered host-vector-agent interaction. The significant temperature variations brought on by climate change and ever-increasing international travel has modified the host-agent interactions, and many re-emerging viral illnesses are now presenting with atypical presentations, including an increased frequency of affliction across broader age groups and heightened manifestations often posing as 'great imitators' mimicking a myriad of other dermatoses. Although final diagnosis often relies on serological and molecular tests, certain cutaneous clues can help arrive at a probable clinical diagnosis and help the clinicians order specific and relevant investigations, especially in resource-poor settings where access to laboratory diagnostic tests is likely to be limited. In this review we explore the changing disease dynamics of common viral infections, especially in resource-poor settings, including coronavirus disease 2019, chikungunya, hand-foot-and-mouth disease and some newly emerging ones like mpox (previously referred to as monkeypox), and highlight recent developments in our understanding of the clinical variations seen in their presentations.

Abstract A024: Assessing Mitondiral Priming in Leukemia Using BH3 Complex Specific Antibodies (PRIMABS): Prediciting Patient Response to Apoptosis Inducing Drugs

Abstract Diagnostic tools for cancer therapy that enable oncologists to inform patients about the likely benefits of their treatment versus toxicity will empower patients to make evidence-based decisions about the management of their disease, and in some cases, their last phase of life. Here we provide a novel approach that measures key drug response features of individual cancer cells with the potential for providing prognostic/predictive diagnostic utility for practicing precision medicine. Most cancer treatments work though activation of the cell suicide program of apoptosis. This process is tightly regulated by the BCL-2 family of proteins whose members either prevent or promote cell death. The relative levels of, and the specific interactions between the pro-survival and pro-apoptotic family members that dictates the fate of all cells and determines their propensity to undergo apoptosis. Cells that more readily die in response to various stimuli are referred to as “primed”. Hence, the degree of priming of a tumor sample can be predictive of responses to anti-cancer drugs and can be established using a technique called “BH3 profiling”. The relevance of this method has been demonstrated but it has had only limited application. To address this limitation a unique panel of antibody-based reagents that are mitochondrial PRIMing state detecting Antibodies (PRIMABS), have been developed by our lab, as a novel approach to implementing personalized medicine and developing predicative diagnostic tests. These antibodies enable the degree of tumor cell priming to be determined directly, unlike BH3 profiling which is an indirect measure. The key innovation lies in the approach taken to determine the presence (or absence) of the critical pro-survival : pro-apoptotic protein complexes , as these are the principal determinants of cell priming.. Such PRIMAB reagents have hitherto not been available. Here we provide data outlining the methodology development as well as evidence demonstrating feasibility of PRIMABTM Dx platform for predicting AML and CLL patient response in retrospective studies. Our platform provides a PD biomarker that will become a clinical predictor of cancer cell response to treatments tat are intended to induce apopstosis in tumor cells. The PRIMABTM -DX platform will enable heretofore hard to achieve clinical applicability of priming measurements. This will be significant due to the important prognostic implications of PRIMAB-DxTM readouts and will provide insights into drug resistance and sensitivity mechanisms and lead to new treatment options for cancer patients. Citation Format: Michael Cardone. Assessing Mitondiral Priming in Leukemia Using BH3 Complex Specific Antibodies (PRIMABS): Prediciting Patient Response to Apoptosis Inducing Drugs [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr A024.

Abstract PR013: Highly accurate detection of early-stage colorectal cancer using tumor and immune extracellular vesicles biomarkers

Abstract Introduction: Colorectal cancer (CRC) is one of the most common cancers worldwide, and early detection is critical for successful treatment and improved survival rates. However, precancerous and early-stage CRC present significant diagnostic challenges due to the small size of lesions and the very low expression of tumor-specific biomarkers in the bloodstream. Current genomics-based diagnostic methods struggle to detect these lesions, making it imperative to develop more sensitive and specific approaches. Circulating extracellular vesicles (EVs) are emerging as a promising solution for early-stage CRC detection, since EVs are produced by tumor cells as well as the tumor microenviroment and host immune cells. Thus, EVs may offer the means to detect and monitor small early-stage tumors through both direct detection of tumor- associated biomarkers as well through tumor specific host response and tumor microenvironment biomarkers. Methods: To identify novel early-stage CRC specific biomarkers, we performed proteomics analysis on EVs purified from patient plasma using size exclusion chromatography and a proprietary buffer system that enhances EV and corona protein recovery. TrueDiscovery™ Data-independent acquisition (DIA) mass spectrometry (MS) analysis was conducted on 24 pre- cancer/stage 0 and 25 stage 1 CRC patients and 75 normal patient plasma samples. An in-house developed machine learning pipeline was used to identify differentially expressed proteins and model candidate multiplexes to identify those with extremely high diagnostic accuracy (&amp;gt; 0.99). Results: An average of ∼2,500 proteins were identified per sample and included in bioinformatics analysis. Comparative analysis between control patient EVs and either pre- cancerous/Stage 0, or Stage 1 colon cancer EVs using an in-house Machine Learning pipeline identified 336 and 493 differentially expressed proteins for each group (FDR adjusted p-value &amp;lt; 0.001). Of these, the best 17 pre/stage 0 proteins and 53 stage 1 proteins were trained and tested using Support Vector Machine to assess all potential 3-plexes in order to identify those with mean diagnostic accuracy &amp;gt; 99%. This yielded 5 3-plexes in precancer/stage 0 and 34 3-plexes in stage 1 with near perfect diagnostic accuracy. These plexes are currently being assessed in single analyte and multiplex immunoassays with the goal of translating candidate 3-plexes to the clinical. Conclusions: Key biomarkers from CRC patient EVs indicate the potential to detect and diagnose early-stage and low tumor burden cancers using our methods. Interestingly, the most accurate 3-plexes consist of proteins from immune, inflammatory and metabolic processes, suggesting that for the detection of early stage cancer it may be critical to include both tumor and host specific biomarkers. Citation Format: Poorva Mudgal, Cheryl Bandoski, Zachary Opheim, Martin Mehnert, Valesca Anschau, Issa Isaac, Alan Ezrin, Todd Hembrough. Highly accurate detection of early-stage colorectal cancer using tumor and immune extracellular vesicles biomarkers [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr PR013.

Abstract A004: Full spectrum isolation technique-based dual circulating rare cells analysis to maximize early cancer diagnosis

Abstract Liquid biopsy (LB) is essential in precision medicine, enabling real-time tumor monitoring through repeated sampling. However, its diagnostic sensitivity and accuracy are limited by the low detection rate of circulating tumor cells (CTCs). For example, in breast cancer, the CTC detection rate exhibited only 20%, significantly limiting its clinical utility. This limitation arises since existing technologies selectively isolate specific phenotypes of CTCs, failing to capture others, such as mesenchymal CTCs, which are associated with poor prognosis. These mesenchymal CTCs often evade detection due to their lack of EpCAM expression and high deformability, making difficult to isolate them with traditional filter or EpCAM based methods. Additionally, conventional LB methods fail to provide critical insights into the tumor microenvironment (TME). To overcome these limitations, we developed a novel approach that improves LB diagnostic sensitivity and accuracy by (1) creating a technology to isolate all types of CTCs, regardless of marker expression or size, and (2) simultaneously analyzing CTCs and circulating cancer-associated fibroblasts (cCAFs). This dual approach addresses the challenges of low sensitivity and accuracy by enabling the isolation of a broader range of CTCs, including mesenchymal types, and by incorporating cCAF analysis. cCAFs, derived from the TME and found in the bloodstream, provide valuable insights into the TME that are hard to obtain through traditional methods. Analyzing cCAFs alongside CTCs offers a more comprehensive understanding of tumor biology and its microenvironment, leading to improved diagnostic accuracy, especially in early-stage cancer. In our study involving 55 breast cancer patients, we demonstrated that while CTCs were undetectable in 42.9% of early-stage patients, cCAFs were detected in all cases and were found to be 10 times more abundant than CTCs. Importantly, for the first time, this study uncovers that the diverse CAF subtypes previously identified in tissue samples also exist in the bloodstream, highlighting their critical role in providing TME-related insights. In addition, cCAF subtypes vary with hormonal receptor expression, and their marker analysis allows for 80.9% accurate classification of breast cancer subtypes. We applied this approach to early-stage breast cancer patients, for whom diagnosis through liquid biopsy is particularly difficult, and found that the combined analysis of CTCs and cCAFs achieved a 18% improvement in accuracy, with the AUC increasing from 0.6817 to 0.8009 compared to CTC analysis alone. This innovative approach significantly enhances LB by simultaneously isolating all CTC subtypes and incorporating cCAF analysis, thereby offering a more sensitive and accurate diagnostic tool. Beyond improving breast cancer diagnostics, this method has the potential to be applied to other tumor types and could also prove valuable for prognosis prediction and therapeutic response monitoring, ultimately leading to better patient outcomes and more effective cancer treatment strategies. Citation Format: Hyeongjung Woo, Simon A Joosse, Frederike Harten, Felix Hilpert, Klaus Pantel, Minseok S. Kim. Full spectrum isolation technique-based dual circulating rare cells analysis to maximize early cancer diagnosis [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A004.

Skin Cancer Diagnosis by Lesion, Physician, and Examination Type: A Systematic Review and Meta-Analysis.

Skin cancer is the most common cancer in the US; accurate detection can minimize morbidity and mortality. To assess the accuracy of skin cancer diagnosis by lesion type, physician specialty and experience, and physical examination method. PubMed, Embase, and Web of Science. Cross-sectional and case-control studies, randomized clinical trials, and nonrandomized controlled trials that used dermatologists or primary care physicians (PCPs) to examine keratinocytic and/or melanocytic skin lesions were included. Search terms, study objectives, and protocol methods were defined before study initiation. Data extraction was performed by a reviewer, with verification by a second reviewer. A mixed-effects model was used in the data analysis. Data analyses were performed from May 2022 to December 2023. Meta-analysis of diagnostic accuracy comprised sensitivity and specificity by physician type (primary care physician or dermatologist; experienced or inexperienced) and examination method (in-person clinical examination and/or clinical images vs dermoscopy and/or dermoscopic images). In all, 100 studies were included in the analysis. With experienced dermatologists using clinical examination and clinical images, the sensitivity and specificity for diagnosing keratinocytic carcinomas were 79.0% and 89.1%, respectively; using dermoscopy and dermoscopic images, sensitivity and specificity were 83.7% and 87.4%, and for PCPs, 81.4% and 80.1%. Experienced dermatologists had 2.5-fold higher odds of accurate diagnosis of keratinocytic carcinomas using in-person dermoscopy and dermoscopic images compared with in-person clinical examination and images. When examining for melanoma using clinical examination and images, sensitivity and specificity were 76.9% and 89.1% for experienced dermatologists, 78.3% and 66.2% for inexperienced dermatologists, and 37.5% and 84.6% for PCPs, respectively; whereas when using dermoscopy and dermoscopic images, sensitivity and specificity were 85.7% and 81.3%, 78.0% and 69.5%, and 49.5% and 91.3%, respectively. Experienced dermatologists had 5.7-fold higher odds of accurate diagnosis of melanoma using dermoscopy compared with clinical examination. Compared with PCPs, experienced dermatologists had 13.3-fold higher odds of accurate diagnosis of melanoma using dermoscopic images. The findings of this systematic review and meta-analysis indicate that there are significant differences in diagnostic accuracy for skin cancer when comparing physician specialty and experience, and examination methods. These summary metrics of clinician diagnostic accuracy could be useful benchmarks for clinical trials, practitioner training, and the performance of emerging technologies.

Trop2-Targeted Molecular Imaging in Solid Tumors: Current Advances and Future Outlook.

Trophoblast cell surface antigen 2 (Trop2), a transmembrane glycoprotein, plays a dual role in physiological and pathological processes. In healthy tissues, Trop2 facilitates development and orchestrates intracellular calcium signaling. However, its overexpression in numerous solid tumors shifts its function toward driving cell proliferation and metastasis, thus leading to a poor prognosis. The clinical relevance of Trop2 is underscored by its utility as both a biomarker for diagnostic imaging and a target for therapy. Notably, the U.S. Food and Drug Administration (FDA) has approved sacituzumab govitecan (SG), a novel Trop2-targeted agent, for treating triple-negative breast cancer (TNBC) and refractory urothelial cancer, highlighting the significance of Trop2 in clinical oncology. Molecular imaging, a powerful tool for visualizing and quantifying biological phenomena at the molecular and cellular levels, has emerged as a critical technique for studying Trop2. This approach encompasses various modalities, including optical imaging, positron emission tomography (PET), single photon emission computed tomography (SPECT), and targeted antibodies labeled with radioactive isotopes. Incorporating Trop2-targeted molecular imaging into clinical practice is vital for the early detection, prognostic assessment, and treatment planning of a broad spectrum of solid tumors. Our review captures the latest progress in Trop2-targeted molecular imaging, focusing on both diagnostic and therapeutic applications across diverse tumor types, including lung, breast, gastric, pancreatic, prostate, and cervical cancers, as well as salivary gland carcinomas. We critically evaluate the current state by examining the relevant applications, diagnostic accuracy, therapeutic efficacy, and inherent limitations. Finally, we analyze the challenges impeding widespread clinical application and offer insights into strategies for advancing the field, thereby guiding future research endeavors.

Artificial Intelligence-Driven Wearable Electronics and Smart Nanodevices for Continuous Cancer Monitoring and Enhanced Diagnostic Accuracy

Artificial Intelligence (AI)-driven wearable electronics and smart nanodevices are transforming cancer diagnostics by offering continuous monitoring and enhanced diagnostic accuracy. Traditional cancer diagnostic methods often suffer from delays in detection and limited real-time data, which can hinder timely treatment. In contrast, AI integration in wearable technologies and nanodevices allows for the continuous tracking of physiological biomarkers, enabling earlier detection of cancer and more precise monitoring of disease progression. This review explores the advancements in AI-powered wearable electronics and smart nanodevices for cancer diagnostics, focusing on their applications, benefits, and challenges. AI-driven wearable devices, such as smartwatches and biosensors, are equipped with machine learning algorithms capable of real-time data analysis, facilitating personalized and proactive healthcare solutions. Similarly, smart nanodevices, which leverage nanotechnology combined with AI, offer unprecedented precision in identifying and monitoring cancer cells, allowing for more accurate diagnostic outcomes. Case studies are presented to demonstrate the effectiveness of these technologies in improving diagnostic accuracy and patient outcomes. The review also highlights the challenges these technologies face, including data privacy, ethical concerns, and technical limitations. Additionally, the paper discusses future directions in AI-driven cancer diagnostics, emphasizing the potential for these innovations to revolutionize early cancer detection, improve treatment strategies, and enhance long-term patient outcomes. Ultimately, this review provides a comprehensive understanding of how AI and advanced technologies are reshaping the landscape of cancer diagnostics and monitoring, offering significant implications for clinical practice and research in the medical field.

Combined [18F]-FDG PET-MR Imaging: A promising tool for diagnostics of small bowel Crohn's disease.

Introduction Diagnostics of small bowel Crohn's disease (CD) can be difficult. Combined positron emission tomography-magnetic resonance enterography (PET-MRE) can be used to evaluate intestinal metabolism, but clinical use has been limited due to accessibility, costs, absence of standardized methods and diagnostic thresholds. Our aim was to show that combined PET-MRE can be used to diagnose active small bowel CD. Methods We performed a fusion PET-MRE-scan with [18F]-FDG-tracer to 30 patients with suspected small bowel CD in colonoscopy. Standardized uptake values (SUV) were measured from small bowel. The diagnosis was confirmed with small bowel capsule endoscopy. Clinicians chose appropriate medication to each patient blinded from SUV-results. Endoscopic, laboratory and MRE-findings were investigated in relation to SUV. Results Fusion PET-MRE outperformed MRE in diagnostic accuracy. Patients diagnosed with CD (N=24) had higher SUV than patients not diagnosed with CD (N=6) (3.34 vs. 1.84, p=0.022). A diagnostic cut-off at SUV at 2.5. could be used (AUROC=0.81). A higher SUV predicts need for immunosuppressive medication (p=0.0026) and biologics (p=0.0005). SUV correlates positively with SES-CD-score (Simple Endoscopic Score for Crohn's Disease), fecal calprotectin and CRP and negatively with Hb and serum albumin. Conclusion In future, [18F]-FDG PET-MRE can be used in diagnostics of small bowel CD as a safe alternative for capsule endoscopy. High SUV can predict a more progressive disease course and need for more advanced therapies.

Diagnostic Accuracy of Four-Dimensional Computed Tomography in Preoperative Localization of Primary Hyperparathyroidism After Negative or Inconclusive Sestamibi: A Systematic Review and Meta-analysis

Objective The aim of the study is to assess the diagnostic accuracy of 4-dimensional computed tomography (4D-CT) scans for patients with primary hyperparathyroidism (pHPT) after negative or inconclusive Technetium-99m sestamibi single-photon emission computed tomography scan. Methods A literature search of several databases was conducted from inception to August 2023. Eligible studies reported adult patients (&gt;18 years old) who underwent 4D-CT after negative or inconclusive sestamibi results. The pooled proportions were analyzed using a random-effects model. This review was registered in PROSPERO (CRD42023446285). Results From 208 initial studies screened, 10 met the eligibility criteria, with a total of 335 patients with a diagnosis of pHPT who underwent 4D-CT scans and subsequent surgical exploration. Nine of the studies reported a pooled sensitivity of localization of 71% (0.71; 95% confidence interval [CI]: 0.610, 0.811, I 2 = 72%). The pooled specificity of localization across 5 studies was calculated at 47% (0.47; 95% CI: −0.111, 1.059, I 2 = 99%). Seven of the included studies reported a pooled positive predictive value of 81% (0.81; 95% CI: 0.708, 0.917, I 2 = 70%), and 6 of the studies reported a negative predictive value of 28% (0.28; 95% CI: −0.114, 0.683, I 2 = 98%). Conclusions For pHPT patients with inconclusive sestamibi scans, 4D-CT demonstrates promising results with good diagnostic accuracy for the localization of pHPT. Additional studies evaluating larger groups of patients may provide further support for the use of 4D-CT in this population.

An historical overview of artificial intelligence for diagnosis of major depressive disorder

The Artificial Intelligence (AI) technology holds immense potential in the realm of automated diagnosis for Major Depressive Disorder (MDD), yet it is not without potential shortcomings. This paper systematically reviews the research progresses of integrating AI technology with depression diagnosis and provides a comprehensive analysis of existing research findings. In this context, we observe that the knowledge-driven first-generation of depression diagnosis methods could only address deterministic issues in structured information, with the selection of depression-related features directly influencing identification outcomes. The data-driven second-generation of depression diagnosis methods achieved automatic learning of features but required substantial high-quality clinical data, and the results were often obtained solely from the black-box models which lack sufficient explainability. In an effort to overcome the limitations of the preceding approaches, the third-generation of depression diagnosis methods combined the strengths of knowledge-driven and data-driven approaches. Through the fusion of information, the diagnostic accuracy is greatly enhanced, but the interpretability remains relatively weak. In order to enhance interpretability and introduce diagnostic criteria, this paper offers a new approach using Large Language Models (LLMs) as AI agents for assisting the depression diagnosis. Finally, we also discuss the potential advantages and challenges associated with this approach. This newly proposed innovative approach has the potential to offer new perspectives and solutions in the diagnosis of depression.

Bayesian accuracy estimates for diagnostic tests to detect tuberculosis in captive sun bears (Helarctos malayanus) and Asiatic black bears (Ursus thibetanus) in Cambodia and Vietnam.

Effective control of tuberculosis (TB) depends on early diagnosis of disease, yet available tests are unable to perfectly detect infected individuals. In novel hosts diagnostic testing methods for TB are extrapolated from other species, with unknown accuracy. The primary challenge to evaluating the accuracy of TB tests is the lack of a perfect reference test. Here we use a Bayesian latent class analysis approach to evaluate five tests available for ante-mortem detection of pulmonary TB in captive sun bears and Asiatic black bears in Southeast Asia. Using retrospective results from screening of 344 bears at three rescue centres, we estimate accuracy parameters for thoracic radiography, a serological assay (DPP VetTB), and three microbiological tests (microscopy, PCR (Xpert MTB/RIF, Xpert MTB/RIF Ultra), mycobacterial culture) performed on bronchoalveolar lavage samples. While confirming the high specificities (≥ 0.99) of the three microbiological tests, our model demonstrated their sub-optimal sensitivities (<0.7). Thoracic radiography was the only diagnostic method with sensitivity (0.95, 95% BCI: 0.76, 0.998) and specificity (0.95, 95% BCI: 0.91, 0.98) estimated above 0.9. We recommend caution when interpreting DPP VetTB results, with the increased sensitivity resulting from treatment of weakly visible reactions as positive accompanied by a drop in specificity, and we illustrate how the diagnostic value of weak DPP VetTB reactions is particularly reduced if disease prevalence and/or clinical suspicion is low. Conversely, the reduced utility of negative microbiological tests on bronchoalveolar lavage fluid samples when prevalence and/or clinical suspicion is high is demonstrated. Taken together our results suggest multiple tests should be applied and accompanied by consideration of the testing context, to minimise the consequences of misclassification of disease status of bears at risk of TB in sanctuary settings.

Cost analysis of adding hypertension and diabetes management into routine HIV care in Mbarara and Ibanda districts, Uganda.

In 2016, Uganda introduced services for hypertension and diabetes in selected HIV clinics. We evaluated the costs associated with scaling up these services in HIV clinics in Mbarara and Ibanda districts, Uganda. We estimated the annual costs of providing hypertension and diabetes services using an activity-based costing approach from the health system perspective in ten randomly selected HIV clinics in Mbarara and Ibanda districts. Cost inputs included 2023 data on costs of medications, health provider time, salaries, training costs, and monitoring costs. We determined the average annual cost and medication costs for hypertension and diabetes treatment per enrolled adult patient, stratified by type of health facility. The total annual cost of hypertension and diabetes management services in ten selected HIV clinics was estimated to be $413,850 (range: $8,386 - 186,973). The annual average clinic-level cost per enrolled patient was estimated at $14 (range: $7 - 31). Of the total annual cost, the cost of provider time for initial and follow-up visits represented the largest cost component in 5/10 clinics (mean: 37%, range [13-58%]). In 4/10 clinics, the major cost components were the costs of medication, diagnostic tests, and related supplies (mean: 37%, range [10-75%]). The average cost per enrolled adult patient was $11 at public facilities and $21 in private not-for-profit facilities. The average medication cost per patient for hypertension was $24 (range: $7 - 97) annually; $13 at public facilities and $50 at private not-for-profit facilities. For diabetes treatment, the average annual medication cost per patient was estimated at $14 (range: $6 - 35); $11 at public facilities and $22 at private not-for-profit facilities. Adding hypertension and diabetes management to routine HIV care might be feasible based on the estimated annual cost per patient. Hypertension and diabetes treatment was more costly in private not-for-profit facility-based clinics than at public facilities. This variation was primarily driven by higher medication procurement prices at private facilities, revealing a potential area for optimizing costs through improved procurement practices.