Diagnostics最新文献

{"title":"Orbital Neurolymphomatosis in Patient with CNS Lymphoma.","authors":"Tara Shooshani, Michael Han, Jeremiah P Tao, Samuel J Spiegel, Maria Del Valle Estopinal","doi":"10.3390/diagnostics15060780","DOIUrl":"10.3390/diagnostics15060780","url":null,"abstract":"<p><p>Neurolymphomatosis (NL) is a rare manifestation of hematologic malignancies, characterized by a neoplastic infiltration of the peripheral nervous system and cranial nerves (CNs). Non-Hodgkin lymphomas (NHLs) account for 90% of NL cases, while acute leukemia represents 10% of the cases. NL can occur as the first manifestation of a malignancy (primary), or as a relapse or progression of a previously treated disease (secondary). Herein, we report a unique case of NL involving the left orbit and CNs in a 74-year-old female with primary central nervous system (CNS) diffuse large B-cell lymphoma (DLBCL). Our patient developed secondary neurolymphomatosis involving the left orbit and CNs II, III, V, and VI, supported by clinical, radiologic, and histologic findings. The lacrimal gland enhancement was histopathologically proven to be caused by the direct spread of CNS DLBCL to the lacrimal nerve, a branch of CN V, identifying NL as one of the conditions that can affect this organ. The lacrimal gland could be considered as a more accessible biopsy site when the involvement of CN V is suspected.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143728532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Zucker Diabetic Fatty Rat as a Model for Vascular Changes in Diabetic Kidney Disease: Characterising Hydronephrosis.","authors":"Amy McDermott, Nathalie Sarup Panduro, Iman Taghavi, Hans Martin Kjer, Stinne Byrholdt Søgaard, Michael Bachmann Nielsen, Jørgen Arendt Jensen, Charlotte Mehlin Sørensen","doi":"10.3390/diagnostics15060782","DOIUrl":"10.3390/diagnostics15060782","url":null,"abstract":"<p><p><b>Background/Objectives:</b> Diabetic kidney disease (DKD) is a significant concern for global healthcare, particularly in individuals with diabetes. The Zucker rat strain is a commonly used model of type 2 diabetes, despite awareness that this animal can develop hydronephrosis. In this study, we present novel imaging data evaluating the accuracy of this animal model in replicating the vascular aspects of human DKD while examining the impact of hydronephrosis on its validity as a disease model. <b>Methods:</b> This study reused data from a population of male Zucker Diabetic Fatty (ZDF; <i>n</i> = 22) rats and Zucker Lean (ZL) rats (<i>n</i> = 22) aged 12 to approximately 40 weeks. Vascular casting was performed to enable visualisation of the renal vasculature. Anatomical regional volumes and vascular density data were obtained from μCT scans using image thresholding and manual analysis. The effects of hydronephrosis were evaluated using renal functional parameters and histological examination. <b>Results:</b> A significantly lower cortical vascular density, as well as lower total renal vascular density, was seen in ZDF rats compared to ZL rats, independent of age. We identified that hydronephrosis affected 92% of ZDF rats and 69% of ZL rats. Hydronephrosis cavity size was significantly correlated with the degree of hyperglycaemia and rate of diuresis but had no other detected impact on renal function, vascularity, or tissue histological architecture. <b>Conclusions:</b> These findings support using the Zucker rat strain as a model for vascular changes in DKD. Despite identifying severe hydronephrosis in this population, it had minimal quantifiable impact on renal function or diabetes modelling.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143728935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Based Multi-Parameter Modeling for Prediction of Post-Inflammatory Lung Changes.","authors":"Gerlig Widmann, Anna Katharina Luger, Thomas Sonnweber, Christoph Schwabl, Katharina Cima, Anna Katharina Gerstner, Alex Pizzini, Sabina Sahanic, Anna Boehm, Maxmilian Coen, Ewald Wöll, Günter Weiss, Rudolf Kirchmair, Leonhard Gruber, Gudrun M Feuchtner, Ivan Tancevski, Judith Löffler-Ragg, Piotr Tymoszuk","doi":"10.3390/diagnostics15060783","DOIUrl":"10.3390/diagnostics15060783","url":null,"abstract":"<p><p><b>Objectives</b>: Prediction of lung function deficits following pulmonary infection is challenging and suffers from inaccuracy. We sought to develop machine-learning models for prediction of post-inflammatory lung changes based on COVID-19 recovery data. <b>Methods</b>: In the prospective CovILD study (<i>n</i> = 420 longitudinal observations from <i>n</i> = 140 COVID-19 survivors), data on lung function testing (LFT), chest CT including severity scoring by a human radiologist and density measurement by artificial intelligence, demography, and persistent symptoms were collected. This information was used to develop models of numeric readouts and abnormalities of LFT with four machine learning algorithms (Random Forest, gradient boosted machines, neural network, and support vector machines). <b>Results</b>: Reduced DLCO (diffusion capacity for carbon monoxide <80% of reference) was found in 94 (22%) observations. Those observations were modeled with a cross-validated accuracy of 82-85%, AUC of 0.87-0.9, and Cohen's κ of 0.45-0.5. No reliable models could be established for FEV1 or FVC. For DLCO as a continuous variable, three machine learning algorithms yielded meaningful models with cross-validated mean absolute errors of 11.6-12.5% and R² of 0.26-0.34. CT-derived features such as opacity, high opacity, and CT severity score were among the most influential predictors of DLCO impairment. <b>Conclusions</b>: Multi-parameter machine learning trained with demographic, clinical, and artificial intelligence chest CT data reliably and reproducibly predicts LFT deficits and outperforms single markers of lung pathology and human radiologist's assessment. It may improve diagnostic and foster personalized treatment.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Association of Heart Failure and Liver T1 Mapping in Cardiac Magnetic Resonance Imaging.","authors":"Adrian T Huber, Joanna Bartkowiak, Robin Seitz, Benedikt Bernhard, Martina Boscolo Berto, Giancarlo Spano, Benedikt Wagner, Verena C Obmann, Lukas Ebner, Inga A S Todorski, Michael P Brönnimann, Kady Fischer, Dominik P Guensch, Andreas Christe, Annalisa Berzigotti, Lorenz Räber, Tobias Reichlin, Thomas Pilgrim, Fabien Praz, Christoph Gräni, Nicholas Brugger, Alan A Peters","doi":"10.3390/diagnostics15060779","DOIUrl":"10.3390/diagnostics15060779","url":null,"abstract":"<p><p><b>Background/Objectives:</b> The objective of this study was to investigate the association between congestive heart failure (CHF) and T1 mapping in both liver lobes using cardiac MRI. <b>Methods:</b> This retrospective study included patients who underwent cardiac MRI with T1 mapping sequences on a 1.5 T scanner. The liver T1 values were measured in four hepatic regions, utilizing cardiac short axis and four-chamber views. Echocardiographic and laboratory data were collected within 90 days of the cardiac MRI. Comparisons of the liver T1 values and echocardiographic parameters between patients with and without elevated NT-proBNP levels (>125 pg/mL) were conducted using the Mann-Whitney U test. Logistic regression models were employed to adjust for confounding factors. <b>Results:</b> A total of 397 patients were included (with a median age of 56 years; 127 females), of whom 35% (<i>n</i> = 138) exhibited elevated NT-proBNP levels. The patients with elevated NT-proBNP levels showed a larger end-diastolic volume (EDV: 92 vs. 81 mL/m², <i>p</i> < 0.001) and a lower LVEF level (50% vs. 60%, <i>p</i> < 0.001). The liver T1 was significantly higher in the right liver lobe (670 vs. 596 ms, <i>p</i> < 0.001) and the caudate lobe (664 vs. 598 ms, <i>p</i> < 0.001), but not in the left lobe (571 vs. 568 ms, <i>p</i> = 0.068) or the dome (590 vs. 560 ms, <i>p</i> = 0.1). T1 mapping in the caudate (OR 1.013, 95% CI 1.004-1.023, <i>p</i> = 0.005) and right liver lobes (OR 1.012, 95% CI 1.003-1.021, <i>p</i> = 0.009) remained independently predictive in the logistic regression analysis. <b>Conclusions:</b> Elevated T1 values in the caudate and right liver lobes assessed by cardiac MRI were independently associated with CHF and outperformed T1 measurements in the left liver lobe in predicting disease.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Study of Machine Learning and Deep Learning Models for Automatic Parkinson's Disease Detection from Electroencephalogram Signals.","authors":"Sankhadip Bera, Zong Woo Geem, Young-Im Cho, Pawan Kumar Singh","doi":"10.3390/diagnostics15060773","DOIUrl":"10.3390/diagnostics15060773","url":null,"abstract":"<p><p><b>Background:</b> Parkinson's disease (PD) is one of the most prevalent, widespread, and intricate neurodegenerative disorders. According to the experts, at least 1% of people over the age of 60 are affected worldwide. In the present time, the early detection of PD remains difficult due to the absence of a clear consensus on its brain characterization. Therefore, there is an urgent need for a more reliable and efficient technique for early detection of PD. Using the potential of electroencephalogram (EEG) signals, this study introduces an innovative method for the detection or classification of PD patients through machine learning, as well as a more accurate deep learning approach. <b>Methods:</b> We propose an innovative EEG-based PD detection approach by integrating advanced spectral feature engineering with machine learning and deep learning models. Using (a) the UC San Diego Resting State EEG dataset and (b) IOWA dataset, we extract a standardized EEG feature from five key frequency bands-alpha, beta, theta, gamma, delta (α,β,θ,γ,δ) and employ an SVM (Support Vector Machine) classifier as a baseline, achieving a notable accuracy. Furthermore, we implement a deep learning classifier (CNN) with a complex multi-dimensional feature set by combining power values from all frequency bands, which gives superior performance in distinguishing PD patients (both with medication and without medication states) from healthy patients. <b>Results:</b> With the five-fold cross-validation on these two datasets, our approaches successfully achieve promising results in a subject dependent scenario. The SVM classifier achieves competitive accuracies of 82% and 94% in the UC San Diego Resting State EEG dataset (using gamma band) and IOWA dataset, respectively in distinguishing PD patients from non-PD patients in subject. With the CNN classifier, our model is able to capture major cross-frequency dependencies of EEG; therefore, the classification accuracies reach beyond 96% and 99% with those two datasets, respectively. We also perform our experiments in a subject independent environment, where the SVM generates 68.09% accuracy. <b>Conclusions:</b> Our findings, coupled with advanced feature extraction and deep learning, have the potential to provide a non-invasive, efficient, and reliable approach for diagnosing PD, with further work aimed at enhancing feature sets, inclusion of a large number of subjects, and improving model generalizability across more diverse environments.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contact Allergy in Atopic Dermatitis and Psoriasis: A Retrospective Study.","authors":"Domenico Bonamonte, Aurora De Marco, Giulia Ciccarese, Paolo Romita, Giulio Giancaspro, Francesca Ambrogio, Caterina Foti","doi":"10.3390/diagnostics15060766","DOIUrl":"10.3390/diagnostics15060766","url":null,"abstract":"<p><p><b>Background/Objectives</b>: The correlation between contact allergy (CA), atopic dermatitis (AD) and psoriasis is still debated. Therefore, the present study aims to retrospectively analyze the frequency of contact sensitization among patients with psoriasis and AD compared to controls, in order to further investigate the relationship between CA and the underlying immunological background. <b>Methods</b>: All data concerning patients who underwent patch testing from 2016 to 2022 in the dermatology clinic of a tertiary center in Southern Italy have been retrospectively collected. Only patients who underwent patch testing with the S.I.D.A.PA. standard series have been selected and divided into three groups: AD group, psoriasis group and control group. Acquired data were organized into database and underwent statistical examination. <b>Results</b>: A total of 2287 patients have been enrolled, including 377 AD patients, 127 psoriatic patients and 1783 controls. The most frequent allergens were nickel and balsam of Peru. Methylisothiazolinone (4.2% vs. 2.2%), paraben mix (0.3% vs. 0%) and neomycin (1.3% vs. 0.4%) significantly provided more positive reactions (PSR) in the AD group compared to the control one, and fragrance mix II displayed a higher rate of positivity in the atopic group compared to the psoriatic one (3.2% vs. 0%). <b>Conclusions</b>: Psoriasis turned out to be a possible protective factor for CA (odds ratio = 0.6), while AD seems to facilitate its development (odds ratio: 1.42). The limitations of this study mainly rely upon its retrospective nature which limited the acquisition of clinical relevance for PSR. Further studies are required to better investigate this topic.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940888/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evidence Report on the Safety of Gastrointestinal Endoscopy in Patients on Glucagon-like Peptide-1 Receptor Agonists: A Systematic Review and Meta-Analysis.","authors":"Zahid Ijaz Tarar, Umer Farooq, Ahtshamullah Chaudhry, Mustafa Gandhi, Abdallah El Alayli, Mark Ayoub, Baltej Singh, Ebubekir Daglilar, Nirav Thosani","doi":"10.3390/diagnostics15060770","DOIUrl":"10.3390/diagnostics15060770","url":null,"abstract":"<p><p><b>Background/Objectives</b>: Glucagon-like peptide-1 receptor agonists are increasingly used worldwide for weight and hyperglycemia management. There is an ongoing debate on the presence of increased gastric residue, leading to complications such as aspiration and overall safety in patients receiving upper gastrointestinal endoscopy. We aimed to study the effect of GLP-RAs on endoscopy outcomes. <b>Methods</b>: We conducted a detailed search of online databases to select the studies which provided details of the effects of GLP-RAs on patients undergoing endoscopy. The outcomes of interest were odds of retained gastric content (RGC), aspiration risk, and aborted and repeated procedures. A random effect model was used to calculate the pooled odds of outcomes with a 95% CI. We further calculated the pooled odds of predictive factors associated with an increased rate of retained gastric residues in the study population. <b>Results</b>: We included 12 studies with a total of 105,515 patients, of which 32,144 were on GLP-1 RAs and 73,273 were in the control group. A total of 234 (0.73%) aspiration events in GLP-RA users were noted compared to 257 (0.35%) events in the control group. No increased odds (1.26, 95% CI 0.86-1.87, I² 34%) of aspiration were found in GLP-1 users compared to the non-GLP-1 group. Patients on GLP-1 RA had increased RGC compared to the control group (OR 6.30, 95% CI 5.30-7.49, I² 0%). The pooled odds of aborted (OR 5.50, 95% CI 3.25-9.32, I² 0%) and repeated procedures (OR 2.19, 95% CI 1.42-3.38, I² 0%) were significantly higher in GLP-1 RA users. Patients taking Tirazepatide had the highest percentage of RGC (18.9%), while exenatide users had the lowest rate (6.2%) of food retention. Patients undergoing concomitant colonoscopy were found to have significantly low pooled odds of RGC (OR 0.26, 95% CI 0.04-0.48). GLP-1 RAs use was independently associated with increased odds of RGC (3.91, 95% CI 3.21-4.62, I² 0%). The results were homogenous and stayed consistent in the sensitivity analysis. <b>Conclusions</b>: Although the odds of RGC and aborted procedures are high in the GLP-1 RAs group compared to the control, no significant difference in the odds of aspiration was found between the two groups. Simple measures such as a clear liquid diet for 24 h, as routinely set for patients undergoing colonoscopy, may reduce the risk of retaining gastric residue in these patient populations.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143728845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiological Assessment of Charcot Neuro-Osteoarthropathy in Diabetic Foot: A Narrative Review.","authors":"Antonio Mascio, Chiara Comisi, Virginia Cinelli, Dario Pitocco, Tommaso Greco, Giulio Maccauro, Carlo Perisano","doi":"10.3390/diagnostics15060767","DOIUrl":"10.3390/diagnostics15060767","url":null,"abstract":"<p><p>Charcot Neuro-Osteoarthropathy (CNO) is a debilitating complication predominantly affecting individuals with diabetes and peripheral neuropathy. Radiological assessment plays a central role in the diagnosis, staging, and management of CNO. While plain radiographs remain the cornerstone of initial imaging, advanced modalities such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have significantly enhanced diagnostic accuracy. Nuclear imaging, including bone scintigraphy, radiolabeled leukocyte scans, and FDG-PET/CT, offers additional diagnostic precision in complex cases, especially when differentiating CNO from infections or evaluating patients with metal implants. This review underscores the importance of a multimodal imaging approach suited to the clinical stage and specific diagnostic challenges of CNO. It highlights the critical need for standardized imaging protocols and integrated diagnostic algorithms that combine radiological, clinical, and laboratory findings. Advances in imaging biomarkers and novel techniques such as diffusion-weighted MRI hold promise for improving early detection and monitoring treatment efficacy. In conclusion, the effective management of CNO in diabetic foot patients requires a multidisciplinary approach that integrates advanced imaging technologies with clinical expertise. Timely and accurate diagnosis not only prevents debilitating complications but also facilitates the development of personalized therapeutic strategies, ultimately improving patient outcomes.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Radiologist Experience on AI Annotation Quality in Chest Radiographs: A Comparative Analysis.","authors":"Malte Michel Multusch, Lasse Hansen, Mattias Paul Heinrich, Lennart Berkel, Axel Saalbach, Heinrich Schulz, Franz Wegner, Joerg Barkhausen, Malte Maria Sieren","doi":"10.3390/diagnostics15060777","DOIUrl":"10.3390/diagnostics15060777","url":null,"abstract":"<p><p><b>Background/Objectives</b>: In the burgeoning field of medical imaging and Artificial Intelligence (AI), high-quality annotations for training AI-models are crucial. However, there are still only a few large datasets, as segmentation is time-consuming, experts have limited time. This study investigates how the experience of radiologists affects the quality of annotations. <b>Methods</b>: We randomly collected 53 anonymized chest radiographs. Fifteen readers with varying levels of expertise annotated the anatomical structures of different complexity, pneumonic opacities and central venous catheters (CVC) as examples of pathologies and foreign material. The readers were divided into three groups of five. The groups consisted of medical students (MS), junior professionals (JP) with less than five years of working experience and senior professionals (SP) with more than five years of experience. Each annotation was compared to a gold standard consisting of a consensus annotation of three senior board-certified radiologists. We calculated the Dice coefficient (DSC) and Hausdorff distance (HD) to evaluate annotation quality. Inter- and intrareader variability and time dependencies were investigated using Intraclass Correlation Coefficient (ICC) and Ordinary Least Squares (OLS). <b>Results</b>: Senior professionals generally showed better performance, while medical students had higher variability in their annotations. Significant differences were noted, especially for complex structures (DSC Pneumonic Opacities as mean [standard deviation]: MS: 0.516 [0.246]; SP: 0.631 [0.211]). However, it should be noted that overall deviation and intraclass variance was higher for these structures even for seniors, highlighting the inherent limitations of conventional radiography. Experience showed a positive relationship with annotation quality for VCS and lung but was not a significant factor for other structures. <b>Conclusions</b>: Experience level significantly impacts annotation quality. Senior radiologists provided higher-quality annotations for complex structures, while less experienced readers could still annotate simpler structures with satisfying accuracy. We suggest a mixed-expertise approach, enabling the highly experienced to utilize their knowledge most effectively. With the increase in numbers of examinations, radiology will rely on AI support tools in the future. Therefore, economizing the process of data acquisition and AI-training; for example, by integrating less experienced radiologists, will help to meet the coming challenges.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictors of Diagnostic Inaccuracy of Detecting Coronary Artery Stenosis by Preprocedural CT Angiography in Patients Prior to Transcatheter Aortic Valve Implantation.","authors":"Matthias Renker, Steffen D Kriechbaum, Stefan Baumann, Christian Tesche, Grigorios Korosoglou, Efstratios I Charitos, Birgid Gonska, Tim Seidler, Yeong-Hoon Choi, Andreas Rolf, Won-Keun Kim, Samuel T Sossalla","doi":"10.3390/diagnostics15060771","DOIUrl":"10.3390/diagnostics15060771","url":null,"abstract":"<p><p><b>Background:</b> The diagnostic performance of preprocedural CT angiography in detecting coronary artery disease (CAD) in patients scheduled for transcatheter aortic valve implantation (TAVI) has been reported. However, data on predictors of diagnostic inaccuracy are sparse. We sought to investigate clinical characteristics and imaging criteria that predict the inaccurate assessment of coronary artery stenosis based on pre-TAVI-CT. <b>Methods:</b> The patient- and vessel-level analysis of all CT datasets from 192 patients (mean age 82.1 ± 4.8 years; 63.5% female) without known CAD or severe renal dysfunction was performed retrospectively in a blinded fashion. Significant CAD was defined as a CAD-RADS™ 2.0 category ≥ 4 by CT. Invasive coronary angiography (ICA) served as the reference standard for relevant CAD (≥70% luminal diameter stenosis or fractional flow reserve ≤ 0.80). Pertinent clinical characteristics and imaging criteria of all true-positive (<i>n</i> = 71), false-positive (<i>n</i> = 30), false-negative (<i>n</i> = 4), and true-negative patient-level CT diagnoses (<i>n</i> = 87) for relevant stenosis according to ICA were assessed. <b>Results:</b> In the univariate per-patient analysis, the following parameters yielded discriminative power (<i>p</i> < 0.10) regarding inaccurate CAD assessment by pre-TAVI-CT: age, atrial fibrillation, scanner generation, and image quality. Factors independently associated with CT diagnostic inaccuracy were determined using multivariable logistic regression analysis: a younger age (odds ratio [OR] 0.87; 95% confidence interval [CI] 0.80 to 0.94; <i>p</i> < 0.01) and insufficient CT image quality (OR 0.6; CI 0.41 to 0.89; <i>p</i> < 0.01). <b>Conclusions:</b> Our results demonstrate younger age and poor CT image quality to predict less accurate CAD assessments by pre-TAVI-CT in comparison with ICA. Knowledge of these predictors may aid in more efficient coronary artery interpretations based on pre-TAVI-CT.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}