ROC Curve Research Articles

Utilization and validation of dried blood spot-based metabolomics in plasma-derived diagnostic models

Metabolomics has rapidly advanced in life sciences, enhancing our understanding of physiological conditions. Plasma stands out as a predominant sample type in metabolomics studies. Dried blood spot (DBS) has been recognized as a valuable strategy due to its unique characteristics, such as minimal invasiveness, small sample volume, and easy transport. Most large cohort studies construct the diagnostic model using plasma-based metabolomics rather than DBS. However, the comparability of metabolite measurements between DBS and plasma samples, as well as the integration of DBS-based metabolomics into established plasma-derived diagnostic models, remains unclear. We collected plasma and DBS samples from the same 12 healthy controls under fasting and non-fasting conditions and performed ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS)-based metabolomics. We identified 277 metabolites present in both plasma and DBS samples, with 229 (82.7 %) showing a strong correlation between the two sample types. Furthermore, pre-processing datasets from plasma and DBS samples before combining them effectively diminished inconsistencies between plasma- and DBS-based metabolomics without compromising the inherent classification within the data. For the clinical application, the plasma samples were obtained from the gastric cancer patients (n = 204) and the healthy controls (n = 128) to serve as the training cohorts. Additionally, DBS samples were collected from different participants of gastric cancer (n = 19) and healthy controls (n = 12) to validate the diagnostic model. Finally, DBS-based metabolomics were integrated into established plasma-derived gastric cancer diagnostic models, yielding an area under the ROC curve (AUC) of 0.934. Overall, incorporating DBS-based metabolomics into an established plasma-derived diagnostic model holds promise, offering valuable opportunities and insights for diagnostic research and clinical practice.

Integrating Multisource Data and Machine Learning for Supraglacial Lake Detection: Implications for Environmental Management and Sustainable Development Goals in High Mountainous Regions

The accurate detection and monitoring of supraglacial lakes in high mountainous regions are crucial for understanding their dynamic nature and implications for environmental management and sustainable development goals. In this study, we propose a novel approach that integrates multisource data and machine learning techniques for supra-glacial lake detection in the Passu Batura glacier of the Hunza Basin, Pakistan. We extract pertinent features or parameters by leveraging multisource datasets such as radar backscatter intensity VH and VV parameters from Sentinel-1 Ground Range Detected (GRD) data, near-infrared (NIR), NDWI_green, NDWI_blue parameters from Sentinel-2 Multi-spectral Instrument(MSI) data, and surface slope, aspect, and elevation parameters from topographic data. The entire dataset is partitioned into training and testing sets, with machine learning models including the artificial neural network (ANN), the support vector machine (SVM), logistic regression (LR), random forest (RF), and K-nearest neighbour (KNN) trained on the training data (70%). Accuracy assessment employs testing data and involves the evaluation of metrics such as ROC curves and confusion matrices. The best-performing model, ANN, is validated against manually digitized lake polygons derived from Sentinel-2 and Google Earth Pro imagery. Furthermore, the digitized lake polygons are used to analyze glacial lake dynamics from 2016 to 2022. Key findings of this research presented that the NDWI_green, Sigma0_VH, and elevation are the most significant predictors in detecting supra-glacial lakes. Among the various trained and evaluated models, the Artificial Neural Network (ANN) achieved the highest performance (accuracy: 95%, AUC: 0.99) and accurately mapped supra-glacial lakes regardless of their small size. The findings have significant implications for understanding glacial lake behavior in the context of climate change and informing future research and monitoring efforts.

A Multimodal Deep Learning Nomogram for the Identification of Clinically Significant Prostate Cancer in Patients with Gray-Zone PSA Levels: Comparison with Clinical and Radiomics Models

ObjectiveTo establish a multimodal deep learning nomogram for predicting clinically significant prostate cancer in patients with grey-zone PSA levels. MethodsThis retrospective study enrolled 303 patients with pathological results between January 2018 and December 2022. Clinical variables and the PI-RADS v2.1 score were used to construct a clinical model. Radiomics and deep learning features from bp-MRI were used to develop a radiomics model with SVM and a deep learning model, respectively. A hybrid fusion approach was used to integrate the multimodal data and construct combined models (Comb.Rad.model and Comb.DL.model). The robustness of the radiomics model with XGBoost was validated and compared. Model efficacy was assessed through ROC curve and decision curve analysis. A nomogram was developed based on the best-performing model. ResultsThe clinical model had AUCs of 0.845 and 0.779 in the training and testing set. The radiomics model with SVM and the deep learning model achieved AUCs of 0.825 and 0.933 in the training set and 0.811 and 0.907 in the testing set, respectively. The diagnostic performance of the combined models was significantly improved, with Comb.DL.model having a higher AUC than Comb.Rad.model in both the training (0.986 vs. 0.924, P = 0.008) and testing (0.965 vs. 0.859, P = 0.005) set. The diagnostic efficiency of both the radiomics model and Comb.Rad.model with XGBoost were comparable to that of SVM, confirming the robustness of the established model. ConclusionThe integrated nomogram combining deep learning features, PI-RADS score, and clinical variables significantly outperformed the traditional radiomics and clinical models.

АБИЛИТАЦИОННЫЙ ПРОГНОЗ НЕДОНОШЕННЫХ НОВОРОЖДЕННЫХ ДЕТЕЙ

Background. Expert rehabilitation assessment of premature infants in the neonatal period and the first months of life presents great difficulties; from this standpoint, it is advisable to introduce the term “habilitation prognosis” (HP) for the category of premature infants. The purpose of the study is to substantiate and develop indicators of habilitation prognosis and habilitation potential of premature infants. Materials and methods. A prospective study was performed. 212 premature newborns were included (n=212), whose data were statistically processed. Two groups were formed: 1st group of medical habilitation MH(+) n=89 patients for whom MH measures were carried out in full in accordance with the main directions of the developed methodology. The other part of the children made up the 2nd group of MH(-) n=123 patients for whom MH was not performed in accordance with the developed methodology: due to parental disagreement, or the impossibility of systematic work, or MH was not performed in full. The post-conceptual age of children at which medical habilitation of premature infants began ranged from 32 weeks to 3 months. Results. A quantitative integrated indicator of habilitation potential for premature infants HPtP (in the range of values from 7 to 35 points) was developed based on the qualitative characteristics of the rehabilitation prognosis (favorable, doubtful, unfavorable). ROC analysis was performed. It has been established that the optimal separation points are: 11 (area under the ROC curve (AUC) AUC=0.8613 95CI [0.8101-0.9124]) and, accordingly, the boundary for establishing a favorable HPtP from 7 to 11 (out of 35 maximum possible) , p<0.001, and 16 (AUC=0.998 0.8613; 95% CI [0.8101-0.9124]) with sensitivity indicators of 0.9716312 (97.2%), specificity 0.9859155 (98.6%), and, accordingly, the limit for establishing unfavorable HPtP from 17 up to 35 points, p<0.001. Conclusion. A quantitative indicator of habilitation potential for premature infants (HPtP) has been developed. For the first time, the term “habilitation prognosis for premature infants” (HPrP), referring to the possibility of realizing the habilitation potential of premature infants under the full application of medical habilitation methods in a certain premature infant was introduced.

Machine Learning Model for Early Prediction of Survival in Gallbladder Adenocarcinoma: A comparison Study

The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification and improve clinical decision-making, this study set out to create and validate a machine learning model that could accurately predict early survival outcomes in GBAC patients. Five models—RSF, Cox regression, GBM, XGBoost, and Deepsurv—were compared using data from the SEER database (2010–2020). The dataset was divided into training (70%) and validation (30%) sets, and the C-index, ROC curves, calibration curves, and decision curve analysis (DCA) were used to assess the model's performance. At 1, 2, and 3-year survival intervals, the RSF model performed better than the others in terms of calibration, discrimination, and clinical net benefit. The most important predictor of survival, according to SHAP analysis, is AJCC stage. Patients were divided into high, medium, and low-risk groups according to RSF-derived risk scores, which revealed notable variations in survival results. These results demonstrate the RSF model's potential as an early survival prediction tool for GBAC patients, which could enhance individualized treatment and decision-making.

Diagnostic Accuracy of Combined Measurement of Serum Homocysteine, C-Reactive Protein, and Serum Ferritin in Mild Cognitive Impairments and Alzheimer's Disease.

Evaluation of the diagnostic accuracy of combined detection of serum homocysteine (Hcy), C-reactive protein (CRP), and serum ferritin (SF) levels in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Descriptive study. Place and Duration of the Study: Clinical Laboratory Section, Affiliated Hospital of Hebei University, Baoding, Heibei, China, from May 2022 to June 2023. Data of 120 patients with memory decline were retrospectively collected. They were divided into an MCI group and an AD group. A further 50 healthy participants were used as a normal control (NC) group. Differences in the Hcy, CRP, and SF levels between the three groups were evaluated. The specificity, accuracy, and sensitivity of these three indices in the combined or single diagnosis of AD and MCI were compared. Their associations with the severity of AD and MCI were also compared. The AD group had the highest levels of Hcy, CRP, and SF (18.79 ± 4.50, 6.35 ± 2.04, and 355.69 ± 120.36), followed by MCI (16.75 ± 3.06, 4.58 ± 2.31, and 203.48 ± 12.76), and NC group (14.32 ± 2.06, 2.06 ± 0.76, and 98.46 ± 5.06), with statistically significant differences (all p <0.001). The diagnostic efficacy of AD for CRP was 98.50%, sensitivity was 96.00%, and specificity was 94.00%, which was higher than Hcy and SF. Tested together, the area under the ROC curve was 99.90%, specificity was 98.00%, and sensitivity was 98.00%. The diagnostic efficacy of SF for MCI had sensitivity of 100.00%, and specificity of 100.00%, which was higher than that of Hcy and CRP. When the three were combined for detection, the area under the curve of SF was 100.00%, sensitivity of 100.00%, and specificity of 100.00%. The levels of Hcy, CRP, and SF were positively correlated with the severity of AD (p <0.01), while negatively correlated with the mini-mental state examination (MMSE) score (p <0.01). The combined detection of Hcy, CRP, and SF improved the diagnostic accuracy of comorbid AD and MCI. Homocysteine, C-reactive protein, Cognitive impairment, Serum ferritin, Alzheimer's disease.

A Predictive Nomogram for the Occurrence of Gastroesophageal Reflux Disease After Sleeve Gastrectomy: A Study Based on Preoperative HERM.

Gastroesophageal reflux disease (GERD) is a common complication after laparoscopic sleeve gastrectomy (LSG); This study aimed to construct a model that can predict the incidence of GERD after LSG by exploring the correlation between the results of high-resolution esophageal manometry (HREM) and the incidence of GERD after LSG. We collected the clinical data of patients who had undergone HREM before bariatric surgery from September 2013 to September 2019 at the bariatric center of our hospital. The Gerd-Q scores during the postoperative follow-up were collected to determine the incidence of GERD. A logistic regression analysis was performed to explore the correlation of the HREM results and general clinical data with the incidence of GERD after LSG. The percentage of synchronous contractions, lower esophageal sphincter (LES) resting pressure, and history of smoking were correlated with the development of GERD after LSG, with the history of smoking and percentage of synchronous contractions as risk factors and LES resting pressure as a protective factor. The training set showed an area under the ROC curve (AUC) of the nomogram model of 0.847. The validation set showed an AUC of 0.761. The decision and clinical impact curves showed a high clinical value for the prediction model. The HREM results correlated with the development of GERD after LSG, with the percentage of synchronous contractions and LES resting pressure showing predictive value. Combined with the history of smoking, the predictive model showed a high confidence and clinical value.

Diagnostic Accuracy of Combined Measurement of Serum Homocysteine, C-Reactive Protein, and Serum Ferritin in Mild Cognitive Impairments and Alzheimer's Disease.

Evaluation of the diagnostic accuracy of combined detection of serum homocysteine (Hcy), C-reactive protein (CRP), and serum ferritin (SF) levels in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Descriptive study. Place and Duration of the Study: Clinical Laboratory Section, Affiliated Hospital of Hebei University, Baoding, Heibei, China, from May 2022 to June 2023. Data of 120 patients with memory decline were retrospectively collected. They were divided into an MCI group and an AD group. A further 50 healthy participants were used as a normal control (NC) group. Differences in the Hcy, CRP, and SF levels between the three groups were evaluated. The specificity, accuracy, and sensitivity of these three indices in the combined or single diagnosis of AD and MCI were compared. Their associations with the severity of AD and MCI were also compared. The AD group had the highest levels of Hcy, CRP, and SF (18.79 ± 4.50, 6.35 ± 2.04, and 355.69 ± 120.36), followed by MCI (16.75 ± 3.06, 4.58 ± 2.31, and 203.48 ± 12.76), and NC group (14.32 ± 2.06, 2.06 ± 0.76, and 98.46 ± 5.06), with statistically significant differences (all p <0.001). The diagnostic efficacy of AD for CRP was 98.50%, sensitivity was 96.00%, and specificity was 94.00%, which was higher than Hcy and SF. Tested together, the area under the ROC curve was 99.90%, specificity was 98.00%, and sensitivity was 98.00%. The diagnostic efficacy of SF for MCI had sensitivity of 100.00%, and specificity of 100.00%, which was higher than that of Hcy and CRP. When the three were combined for detection, the area under the curve of SF was 100.00%, sensitivity of 100.00%, and specificity of 100.00%. The levels of Hcy, CRP, and SF were positively correlated with the severity of AD (p <0.01), while negatively correlated with the mini-mental state examination (MMSE) score (p <0.01). The combined detection of Hcy, CRP, and SF improved the diagnostic accuracy of comorbid AD and MCI. Homocysteine, C-reactive protein, Cognitive impairment, Serum ferritin, Alzheimer's disease.

Prognostic Value of Preoperative Systemic Immune-Inflammation Index in Non-Metastatic Paediatric Wilms' Tumour Patients Undergoing Upfront Radical Nephrectomy.

To analyse the relationship between the preoperative systemic immune-inflammation index (SII) and the relapse-free survival (RFS) of paediatric patients with Wilms' tumour (WT) after radical surgery, and to establish and validate a prognostic survival model. Observational study. Place and Duration of the Study: Department of Oncologic Surgery, Anhui Children's Hospital of Fudan University, Hefei, China, from January 2013 to August 2023. A retrospective analysis was conducted on 79 WT patients treated with radical resection, with their preoperative SII values computed. The best cut-off for SII was determined through the ROC curve, categorising patients into high and low SII groups. The Kaplan-Meier method and Cox-regression were used for survival analysis. A survival prognostic model was constructed and its predictive capability gauged (AUC of the ROC). The study included 79 WT patients with a median RFS of 65 months and an average of 75.5 ± 3.4 months. The optimal cut-off value for SII was 534.95. The low SII group had a higher RFS (Log-rank: χ2 = 9.380, p = 0.002). Preoperative SII (HR = 3.277, 95% CI: 1.167 - 9.200, p = 0.024), clinical staging (HR = 8.408, 95% CI: 2.604 - 27.147, p <0.001), and tissue differentiation (HR = 2.237, 95% CI: 1.043 - 5.828, p = 0.039) were independent risk factors for RFS. The model's diagnostic performance was 0.749 (95% CI: 0.636 - 0.861). Internal validation showed an AUC of 0.723 (95% CI: 0.608 - 0.838). Lower preoperative SII suggests a more favourable prognosis. The SII-based nomogram efficiently forecasts post-radical surgery prognosis for WT. Wilms' Tumour, Systemic immune-inflammation index, Relapse-free survival, Nomogram.

Does serum neutrophil gelatinase–associated lipocalin level predict acute kidney injury in patients with acute rhabdomyolysis in the emergency department? A multicentre prospective study

ObjectivesThe major complication of rhabdomyolysis is acute kidney injury (AKI), which requires prompt treatment. Currently, few biomarkers are available for the early detection of AKI. Serum neutrophil gelatinase–associated lipocalin (NGAL) has been suggested as an early biomarker for renal ischemia. However, its capacity to predict AKI in patients presenting with rhabdomyolysis in the emergency department (ED) remains unclear. The aim of this study was to evaluate the ability of NGAL to predict 48-hour AKI.DesignProspective, multicentre study.SettingFive adult EDs in France from August 2013 to December 2015.ParticipantsNGAL levels were measured on ED admission in patients with rhabdomyolysis. A total of 197 patients were enrolled, and 189 (96%) were analysed, of whom 89 (47%) were women. Patients were included if they presented to the ED with rhabdomyolysis and a creatine phosphokinase (CPK) level above 1000 IU/L. Exclusion criteria were pregnancy, presentation with acute coronary syndrome, the need for iodinated contrast, chronic dialysis or recent use of nephrotoxic drugs (within 72 hours prior to the ED visit). Patients who withdrew consent or had AKI due to other causes were also excluded.Primary and secondary outcome measuresThe primary outcome was AKI at 48 hours, defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Secondary outcomes included in-hospital mortality, length of hospital stay, admission to intensive care and the need for renal replacement therapy.ResultsOverall, 54 (29%) patients developed AKI by day 2. The area under the ROC curve (AUC-ROC) for NGAL in predicting AKI on day 2 was 0.60 (95% CI 0.51 to 0.70), with an optimal cut-off of 129 ng/mL. The sensitivity was 0.65, and specificity was 0.50. After adjustment for CPK levels, age, sex and oxygen saturation, the AUC-ROC for predicting AKI on day 2 increased slightly to 0.64 (95% CI 0.54 to 0.74).ConclusionNGAL has limited ability to predict day 2 AKI in patients presenting with acute rhabdomyolysis in the ED.Trail registration numberNCT01544231.Comité de Protection des Personnes Sud Méditerranée III n°2011-A01059-32.

Boosting the accuracy of existing models by updating and extending: using a multicenter COVID-19 ICU cohort as a proxy

Most published prediction models for Coronavirus Disease 2019 (COVID-19) were poorly reported, at high risk of bias, and heterogeneous in model performance. To tackle methodological challenges faced in previous prediction studies, we investigated whether model updating and extending improves mortality prediction, using the Intensive Care Unit (ICU) as a proxy. All COVID-19 patients admitted to seven ICUs in the Euregio-Meuse Rhine during the first pandemic wave were included. The 4C Mortality and SEIMC scores were selected as promising prognostic models from an external validation study. Five predictors could be estimated based on cohort size. TRIPOD guidelines were followed and logistic regression analyses with the linear predictor, APACHE II score, and country were performed. Bootstrapping with backward selection was applied to select variables for the final model. Additionally, shrinkage was performed. Model discrimination was displayed as optimism-corrected areas under the ROC curve and calibration by calibration slopes and plots. The mortality rate of the 551 included patients was 36%. Discrimination of the 4C Mortality and SEIMC scores increased from 0.70 to 0.74 and 0.70 to 0.73 and calibration plots improved compared to the original models after updating and extending. Mortality prediction can be improved after updating and extending of promising models.

An Analysis of the Effect of Skew Rolling Parameters on the Surface Quality of C60 Steel Parts Using Classification Models.

This paper presents the experimental and numerical results of a study on producing axisymmetric parts made of the C60-grade steel by skew rolling. The experimental part of this study involved conducting the skew rolling process with varying parameters, including the forming angle α, tool angle θ, chuck velocity Vu, and reduction ratio δ. Their effect on the quality of produced parts was examined and described by the roughness parameter Ra. Numerical calculations involved the use of machine learning models to predict the quality class of produced parts. The highest prediction accuracy of the results was obtained with the random forest and logistic regression models. Metrics such as precision, recall and accuracy were used to evaluate the performance of individual models. Confusion matrices and ROC curves were also employed to illustrate the performance of the classification models. The results of this study will make it possible to prevent the formation of spiral grooves on the circumference of steel parts during the rolling process.

A Novel Gene Signature Based on Immune Cell Infiltration Landscape Predicts Prognosis in Lung Adenocarcinoma Patients.

The tumor microenvironment (TME) is created by the tumor and dominated by tumor-induced interactions. Long-term survival of lung adenocarcinoma (LUAD) patients is strongly influenced by immune cell infiltration in TME. The current article intends to construct a gene signature from LUAD ICI for predicting patient outcomes. For the initial phase of the study, the TCGA-LUAD dataset was chosen as the training group for dataset selection. We found two datasets named GSE72094 and GSE68465 in the Gene Expression Omnibus (GEO) database for model validation. Unsupervised clustering was performed on the training cohort patients using the ICI profiles. We employed Kaplan-Meier estimators and univariate Cox proportional-hazard models to identify prognostic differentially expressed genes in immune cell infiltration (ICI) clusters. These prognostic genes are then used to develop a LASSO Cox model that generates a prognostic gene signature. Validation was performed using Kaplan-Meier estimation, Cox, and ROC analysis. Our signature and vital immune-relevant signatures were analyzed. Finally, we performed gene set enrichment analysis (GSEA) and immune infiltration analysis on our finding gene signature to further examine the functional mechanisms and immune cellular interactions. Our study found a sixteen-gene signature (EREG, HPGDS, TSPAN32, ACSM5, SFTPD, SCN7A, CCR2, S100P, KLK12, MS4A1, INHA, HOXB9, CYP4B1, SPOCK1, STAP1, and ACAP1) to be prognostic based on data from the training cohort. This prognostic signature was certified by Kaplan-Meier, Cox proportional-hazards, and ROC curves. 11/15 immune-relevant signatures were related to our signature. The GSEA results indicated our gene signature strongly correlates with immune-related pathways. Based on the immune infiltration analysis findings, it can be deduced that a significant portion of the prognostic significance of the signature can be attributed to resting mast cells. We used bioinformatics to determine a new, robust sixteen-gene signature. We also found that this signature's prognostic ability was closely related to the resting mast cell infiltration of LUAD patients.

Prognostic Value of Preoperative Systemic Immune-Inflammation Index in Non-Metastatic Paediatric Wilms' Tumour Patients Undergoing Upfront Radical Nephrectomy.

To analyse the relationship between the preoperative systemic immune-inflammation index (SII) and the relapse-free survival (RFS) of paediatric patients with Wilms' tumour (WT) after radical surgery, and to establish and validate a prognostic survival model. Observational study. Place and Duration of the Study: Department of Oncologic Surgery, Anhui Children's Hospital of Fudan University, Hefei, China, from January 2013 to August 2023. A retrospective analysis was conducted on 79 WT patients treated with radical resection, with their preoperative SII values computed. The best cut-off for SII was determined through the ROC curve, categorising patients into high and low SII groups. The Kaplan-Meier method and Cox-regression were used for survival analysis. A survival prognostic model was constructed and its predictive capability gauged (AUC of the ROC). The study included 79 WT patients with a median RFS of 65 months and an average of 75.5 ± 3.4 months. The optimal cut-off value for SII was 534.95. The low SII group had a higher RFS (Log-rank: χ2 = 9.380, p = 0.002). Preoperative SII (HR = 3.277, 95% CI: 1.167 - 9.200, p = 0.024), clinical staging (HR = 8.408, 95% CI: 2.604 - 27.147, p <0.001), and tissue differentiation (HR = 2.237, 95% CI: 1.043 - 5.828, p = 0.039) were independent risk factors for RFS. The model's diagnostic performance was 0.749 (95% CI: 0.636 - 0.861). Internal validation showed an AUC of 0.723 (95% CI: 0.608 - 0.838). Lower preoperative SII suggests a more favourable prognosis. The SII-based nomogram efficiently forecasts post-radical surgery prognosis for WT. Wilms' Tumour, Systemic immune-inflammation index, Relapse-free survival, Nomogram.

Assessment and integration of multiparametric MRI for liver fibrosis staging in rat non-alcoholic steatohepatitis: Evaluation of diagnostic efficiency and model interpretation

ObjectivesMulti-parametric MRI techniques, including intravoxel incoherence motion (IVIM), iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL IQ), T2* mapping and T2 mapping, were employed to develop and validate a predictive model for non-alcoholic steatohepatitis (NASH) diagnosis and liver fibrosis (LF) staging in rats. The combined model was interpreted using SHapley Additive exPlanations (SHAP) values for model understanding. Materials and methods160 healthy Sprague-Dawley (SD) rats were divided into control (n = 24) and experimental (n = 136) groups, and the 12-week and 16-week groups were injected intraperitoneally with carbon tetrachloride (CCl4) for 4 weeks, one month before the final feeding period. All rats were subjected to pathological examination to determine LF stage. Upon the study’s completion, 147 SD rats were assessed for liver fibrosis. Results84 SD rats were diagnosed with NASH and 31, 10, and 43 rats were histologically diagnosed with no fibrosis (F0), early LF (F1-F2), and late LF (F3-F4). For diagnosis of NASH and staging of liver fibrosis associated with NASH, a combined multi-parameter magnetic resonance imaging prediction model has a higher area under the ROC curve (AUC) than individual diffusion parameters, especially in advanced stages of fibrosis, with an AUC of 0.929 for the combined model. In SHAP, the free fluid(FF) value contributes most to the model for diagnosing NASH and advanced liver fibrosis, while the T2 value contributes most for diagnosing liver fibrosis and the apparent diffusion coefficient (ADC) value contributes most for diagnosing liver cirrhosis. ConclusionsThe integration of several magnetic resonance imaging methods along with SHAP value analysis provides a more comprehensive and theoretical investigation into the mechanisms and factors contributing to the progression of liver fibrosis in NASH, offering insights from various angles.

Duplex-ultrasound as a single imaging test for Global Limb Anatomic Staging System (GLASS) assignment stage and comparison with intraoperative arteriography

Obtaining a prognostic and predictive classification based on the risk of amputation is crucial in ischemic pathology of the lower limbs. Accurate anatomical characterization of arterial lesions is essential for optimal clinical decision-making. The Global Limb Anatomic Staging System (GLASS) provides a comprehensive anatomy assessment of the femoropopliteal and infrapopliteal arterial system. GLASS introduces concepts such as the target arterial path (TAP) and estimated limb-based patency (LBP), crucial for determining the best arterial route to restore blood flow. A prospective study conducted from May to December 2022 involved 66 patients undergoing revascularization surgery for Critical Limb-Threatening Ischemia (CLTI). Preoperative arterial mapping (PAM) using duplex ultrasonography was the sole imaging modality. The TAP was defined in each patient, and GLASS staging was determined. Intraoperative arteriography (IA) was used on the day of surgery for comparison. Agreement between PAM and IA was assessed using Cohen's kappa coefficient, and diagnostic metrics were compared using ROC curves. Doppler ultrasound demonstrated a sensitivity of 98%, specificity and PPV of 100%, and NPV of 94.7% in the femoropopliteal sector, with a Kappa coefficient of 0.98. For the infrapopliteal sector, sensitivity was 95%, specificity 96.5%, PPV 97.3%, and NPV 93.3%, with a Kappa coefficient of 0.89. Combining both sectors, the overall GLASS stage showed a 93.9% concordance with a Kappa coefficient of 0.91. ROC curves revealed no significant differences between ultrasound and arteriography in terms of staging. This study demonstrates that Doppler Ultrasound can serve as the exclusive imaging modality for determining the preoperative GLASS stage.

Myocardial work assessment to improve baseline risk stratification in patients with transthyretin amyloidosis

BackgroundCardiac transthyretin (ATTR) amyloidosis is an often underdiagnosed and potentially fatal disorder associated with poor survival. The National Amyloidosis Centre (NAC) staging system, based on NT-proBNP level and eGFR value, discriminates patients according to survival rates. However, NAC stage II involves a heterogenous group of patients with variable prognosis. This retrospective single-center study was set up to explore the potential role of myocardial work (MW) analysis to enhance risk stratification of ATTR patients prior to therapy. Methods and Results37 patients diagnosed with ATTR between March 2021 and August 2023 were included. Baseline NT-proBNP and eGFR values were collected and LVEF, GLS and MW parameters were obtained from stored echocardiographic images. Patients were categorized per NAC stage (16 NAC I, 13 NAC II and 8 NAC III). Whereas the survival rate in NAC II and NAC III was significantly worse than in NAC I (p = 0.031 and p = 0.045 respectively), no significant difference was found between NAC II and III. In the ROC analysis, GCW proved to be the best survival predictor (AUC: 0.7) with optimal cut-off value 1294 mmHg%. Patients from NAC stage II were re-stratified according to GCW cut-off into HIGH RISK together with patients from NAC III or LOW RISK together with patients from NAC I. Patients in the HIGH RISK group exhibited a significantly worse prognosis with only 40 % survival at 2 years follow-up. ConclusionOur results demonstrate the advantages of incorporating MW analysis, particularly the use of a GCW cut-off, in the baseline risk stratification of ATTR patients.

Neck Circumference as A Screening Tool for Obesity in Pre- and Primary School Children in Puducherry India – A Cross-Sectional Study

Introduction: Obesity has become the 21st Century’s greatest challenge and threat. Around 50% of the obese children become obese adolescents, of which 80% become obese adults. Methodology: Cross-sectional analytical study was done on 930 pre- and primary school children. Neck circumference, weight, height, waist and hip circumference was recorded. Overweight/obese identified according to WHO standards. Age-specific percentile charts for boys and girls were drawn with data from the apparently normal children in the study population. Results: The Neck Circumference measurements ranged from 23.64(±1.16) to 28.49(±2.87) in boys and 23.59(±1.63) to 27.6(±2.67) in girls. ROC analysis derived cut-offs for overweight and obesity in each age group/gender were found to have high sensitivity (80-100%) and specificity (60-95.9%). Using the reference percentile data/curves, and the cut –offs obtained by ROC analysis, a neck circumference above the 75th percentile for age and gender can be regarded as overweight and above the 90th percentile as obese in both boys and girls. Conclusion: The study concludes that the Neck circumference is a valid and effective screening tool for overweight/obesity in pre and primary school children.

Diagnostic value of antibody immunoblotting detection in rheumatoid arthritis patients

Objective. To assess the diagnostic value of antinuclear antibody (ANA) profiling in rheumatoid arthritis by immunoblotting.Materials and methods. In total, 46 patients with rheumatoid arthritis (RA) with a mean age of 34.6 years (21.3–63.2) were observed. The disease duration was 11.2 years (3.7–19.8); the activity according to DAS 28 was 3.15 ± 1.36 (3.05–3.61) points. The RA diagnosis was based on generally accepted clinical guidelines. The control group included 28 patients with osteoarthritis. Laboratory examinations were conducted using a set of reagents to determine IgG antibodies to nuclear antigens by immunoblotting. Results. The RA patients showed an increased level of antibodies to the RNP/Sm antigen, with its frequency being significantly higher than in the control group (p = 0.007). The most specific testomes for diagnosing RA were anti-RNP/Sm (specificity 96%, sensitivity 25%) and antibodies to the recombinant antigen (sensitivity 25%, specificity 88.1%). The method of ROC analysis found that the value of anti-RNP/Sm = 0 is the point corresponding to the optimal ratio of sensitivity/specificity. This value corresponds to a sensitivity of 50% and a specificity of 73.8%.Conclusion. The studied laboratory tests, as a rule, showed high specificity, but rather low sensitivity. The most specific test for RA is anti-RNP/Sm. The conducted ROC analysis showed that the anti-RNP/Sm test has an average quality index for diagnosing RA.

Diagnostic accuracy of ultrasound classifications – O-RADS US v2022, O-RADS US v2020, and IOTA SR – in distinguishing benign and malignant adnexal masses: Enhanced by combining O-RADS US v2022 with tumor marker HE4

PurposeTo assess the diagnostic accuracy of O-RADS Ultrasound (O-RADS US) v2022, O-RADS US v2020, and IOTA SR, and to evaluate whether combining imaging findings with tumor markers enhances the diagnosis of adnexal masses. MethodsThis retrospective study, conducted between January 2018 and December 2023, included consecutive women with adnexal masses scheduled for surgery. Histopathologic results served as the reference standard. Risk factors for malignancy were identified using univariate and multivariate logistic regression analyses. ROC analysis was employed to assess diagnostic test performances, while Kappa statistics evaluated inter-reviewer agreement. ResultsA total of 613 women (mean age, 49.39 ± 12.81 years; range, 16–87 years) with pelvic masses were included. O-RADS US v2022 exhibited comparable performance to O-RADS US v2020, with areas under the curve (AUC) values of 0.940 and 0.937, respectively (p = 0.02, exceeding the adjusted significance level of 0.0167). Both O-RADS models outperformed the IOTA SR, which had an AUC of 0.862 (p < 0.0001 for both comparisons). Multivariate analysis revealed that O-RADS US v2022 [OR 9.148, 95 %CI (4.912–17.039), p < 0.001] and HE4 [OR 1.023, 95 %CI (1.010–1.036), p = 0.001] were significant factors associated with malignant lesions. Furthermore, the combination of O-RADS US v2022 and HE4 demonstrated an AUC of 0.98, significantly outperforming either O-RADS US v2022 alone (AUC = 0.94) or HE4 alone (AUC = 0.92). The Kappa values for O-RADS US v2022, O-RADS US v2020 and IOTA SR were 0.933, 0.891 and 0.923, respectively, indicating substantial inter-reader agreement. ConclusionsThe O-RADS US v2022 demonstrates comparable performance in predicting ovarian malignant lesions when compared to O-RADS US v2020, while surpassing the performance of IOTA SR. Additionally, the combination of O-RADS US v2022 and HE4 provides improved diagnostic effectiveness over using either O-RADS US v2022 or HE4 alone.