Receiver Operating Characteristic Research Articles

Clinical characteristics and associated factors of pediatric acute necrotizing encephalopathy: a retrospective study.

In the clinic, pediatric acute necrotizing encephalopathy (ANE) primarily affects children under five years of age and is characterized by severe brain damage and high mortality. However, some challenges remain regarding the diagnosis and treatment of ANE. In the present study, we analyzed the clinical characteristics and related factors of ANE with the aim of providing improved diagnostic and treatment strategies. Thirty-four pediatric ANE patients admitted to Zhejiang University School of Medicine Hospital between February 2019 and December 2023 were included in this study. To identify the factors associated with mortality, clinical, laboratory and imaging data were analyzed with independent-sample t tests, Mann‒Whitney U tests, Fisher's exact probability tests and receiver operating characteristic (ROC) curve analyses. In this cohort of 34 patients, the most common symptoms were fever, seizures, altered consciousness, vomiting, diarrhea and shock. The mortality rate was 55.9%. Laboratory tests revealed that patients who died had higher creatinine, lactate, activated partial thromboplastin time (APTT), thrombin time (TT), interleukin-6 (IL-6), interleukin-10 (IL-10), creatine kinase (CK), and D-dimer than survivors. Imaging examinations predominantly revealed symmetrical lesions in the thalamus. The fatal group displayed lower Glasgow Coma Scale (GCS) scores and severe complications. Other factors related to mortality included the arterial pH, GCS score and hospitalization duration. The most common symptoms of ANE are fever, seizures, altered consciousness, vomiting, diarrhea and shock, and ANE has a high mortality rate. The GCS score and arterial pH are critical biomarkers for assessing the severity of ANE.

NIMG-66. DEFINING PROGRESSIVE DISEASE: LOCALIZING VOXEL-AT-RISK ON MAGNETIC RESONANCE IMAGING USING DEEP LEARNING

Abstract Follow-up magnetic resonance imaging (MRI) of post-treatment glioblastoma (GBM) can be diagnostically challenging in differentiating progressive disease (PD) from coexisting treatment-related changes. The study aim was to train a machine learning model using multiple MRI timepoints from treated GBM patients with subsequent PD or at least six months of tumor-like, persistently stable disease (SD) to estimate PD risk. A retrospective, post-treatment GBM cohort (n=189) with 453 timepoints was identified with available MRI sequences (T1-weighted with and without contrast, T2-weighted, and fluid-attenuated inversion recovery). PD cases (n=154) showed new, enhancing PD followed by death within 3-6 months; timepoints included the MRI on the PD diagnosis date and the one directly prior. SD cases (n=35) exhibited stable, tumor-like enhancement over timepoints. Cases underwent voxel-wise annotation of PD versus SD. A multiclass, three-dimensional nnU-Net was trained to label voxels as PD, SD, or neither using timepoint sequences with five-fold cross-validation. Model performance was assessed using the ratio of predicted PD/SD voxels compared to each case’s actual PD or SD status. Classification accuracy of PD versus SD status, PD case-wise sensitivity and specificity, and a Receiver Operating Characteristic (ROC) analysis using the PD/SD ratio were presented. The five-fold model ensemble was evaluated on a holdout test set. Validation set performance showed 74.3% accuracy, 0.73 PD case-wise sensitivity, and 0.80 specificity. The holdout set exhibited 79.8% accuracy, 0.81 sensitivity, and 0.78 specificity for PD cases. The ROC analysis showed an area under the curve of 0.80 and 0.83 on the validation and holdout sets, respectively. The model showed high performance and classification accuracy in predicting which voxels were at risk of PD in post-treatment GBM cases. The model also differentiated between PD and SD regions. The model’s predicted PD/SD ratio may be clinically used to stratify patients’ PD risk.

Assessing the predictive potential of ADAM8 for disease control in chronic rhinosinusitis with nasal polyps

BackgroundA disintegrin and metalloproteinase 8 (ADAM8) has been implicated in eosinophilic inflammation; however, its role in chronic rhinosinusitis with nasal polyps (CRSwNP) remains to be elucidated. This study aimed to investigate the predictive significance of ADAM8 levels in nasal secretions for the endotypes and disease control status of CRSwNP.MethodsA cohort comprising 120 CRSwNP patients and 45 healthy controls (HCs) was assembled, delineating 53 non-eosinophilic CRSwNP (neCRSwNP) and 67 eosinophilic CRSwNP (eCRSwNP) patients. Immunohistochemistry and enzyme-linked immunosorbent assay (ELISA) were utilized to measure ADAM8 levels in nasal mucosal tissues and secretions from all participants. The receiver operating characteristic (ROC) curves and Pearson correlation analysis were employed to assess the predictive capability of ADAM8 levels in predictiving CRSwNP endotypes and disease control status.ResultsADAM8 levels in nasal secretions were elevated in CRSwNP patients compared to HCs, with a more pronounced increase observed in eCRSwNP patients. Elevated ADAM8 concentrations in nasal secretions were positively correlated with peripheral blood eosinophil counts and percentages, tissue eosinophil counts, serum total IgE, Lund–Mackay scores, and Lund-Kennedy scores. Ultimately, 103 CRSwNP patients completed the follow-up protocol, with 72 classified as the controlled group and 31 as the uncontrolled group. Uncontrolled CRSwNP patients exhibited significantly higher ADAM8 levels in nasal secretions compared to the controlled group. The ROC curves indicated that ADAM8 in nasal secretions exhibits robust discriminatory capacity for eCRSwNP and postoperative disease control status.ConclusionADAM8 in nasal secretions emerges as a potential novel biomarker for the prognostication of CRSwNP endotypes and the postoperative disease control status.

NIMG-51. PROSPECTIVE CASE CONTROL STUDY OF HYPOXIA FMISO PET/MRI AS A DIAGNOSTIC FEATURE OF GLIOBLASTOMA IMMUNOTHERAPY FAILURE

Abstract BACKGROUND Hypoxia is a prognostic biological feature of glioblastoma. 18F-fluoromisonidazole (FMISO) PET quantifies tumor hypoxia. Efficacy of checkpoint immunotherapy is being investigated, but specific response assessment criteria remain unknown. We hypothesized that tumor hypoxia is a defining feature of immunotherapy failure in patients with glioblastoma. METHODS A prospective case control study was undertaken in patients with newly diagnosed IDH wildtype glioblastoma (NCT03649880). Cases were concurrently enrolled in a phase II study (NCT03347617) of pembrolizumab immunotherapy (200 mg IV every 3 weeks) combined with Stupp protocol temozolomide-based chemoradiotherapy following maximal safe resection. Controls did not receive pembrolizumab. FMISO PET/MRI (3.7 MBq/kg, 90 min uptake) was performed at time of mRANO presumed progression. Hypoxic volume (HV) was defined by pixels with FMISO uptake ≥1.2x that of mean cerebellar uptake. MRI-Gd enhancing volume was segmented using a semi-automated approach. Hypoxic fraction (HF) was defined as the HV-to-MRI enhancing volume ratio. Disease progression or pseudoprogression (Psp) was confirmed through surgery or follow-up MRI. T-test and receiver operator characteristic (ROC) analysis assessed differences and diagnostic performance. RESULTS 18 patients (14 cases [11 progression and 3 Psp] and 4 controls [3 progression and 1 Psp]) provided evaluable disease. For all patients, HF was significantly elevated with progression (1.44 ± 0.8; N=14) compared to Psp (0.31 ± 0.3; N= 4; P=0.01). HV was not different (P=0.10). No difference in HV or HF was observed between Cases and Controls (P> 0.42). Use of HF yielded an AUROC of 0.96 with an optimal cut off of 0.74, to differentiate progression from Psp. CONCLUSION At mRANO presumed progression HF with FMISO PET/MRI accurately distinguishes tumor progression from neuroinflammatory Psp, irrespective of prior pembrolizumab immunotherapy. The incorporation of FMISO PET/MRI in larger prospective therapeutic clinical trials in patients with glioblastoma is underway and likely to improve response assessment criteria.

A nomogram model to predict recurrence of early-onset endometrial cancer after resection based on clinical parameters and immunohistochemical markers: a multi-institutional study

ObjectiveThis study aimed to investigate the prognosis value of the clinical parameters and immunohistochemical markers of patients with early-onset endometrial cancer (EC) and establish a nomogram to accurately predict recurrence-free survival (RFS) of early-onset EC after resection.MethodsA training dataset containing 458 patients and an independent testing dataset consisting of 170 patients were employed in this retrospective study. The independent risk factors related to RFS were confirmed using Cox regression models. A nomogram model was established to predict RFS at 3 and 5 years post-hysterectomy. The C-index, area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and calibration curve were calculated to assess the predictive accuracy of the nomogram.ResultsIn all early-onset EC patients, more than half (368/628, 58.6%) were diagnosed in the age range of 45-49 years. Meanwhile, the recurrence rate of early-onset EC is approximately 10.8%. Multivariate Cox regression analyses showed that histological subtype, FIGO stage, myometrial invasion, lymphovascular space invasion (LVSI), P53 expression, and MMR status were independent prognostic factors related to RFS (all P < 0.05) and established the nomogram predicting 3- and 5-year RFS. The C-index and calibration curves of the nomogram demonstrated a close correlation between predicted and actual RFS. Patients were divided into high- and low-risk groups according to the model of RFS.ConclusionsCombining clinical parameters and immunohistochemical markers, we developed a robust nomogram to predict RFS after surgery for early-onset EC patients. This nomogram can predict prognosis well and guide treatment decisions.

NIMG-42. ANALYSIS OF TUMOR RELAPSE PROBABILITY AND OVERALL SURVIVAL PREDICTION FOLLOWING TEMOZOLOMIDE CHEMORADIATION USING FET PET INCLUDING RADIOMICS IN PATIENTS WITH GLIOBLASTOMA

Abstract BACKGROUND Early after surgery and completion of first-line concomitant chemoradiation with temozolomide, the evaluation of tumor relapse probability and overall survival prediction is of considerable interest for the management of patients with glioblastoma METHODS Sixty-three newly diagnosed patients with glioblastoma (age range, 19-82) who received static and dynamic PET imaging using the radiolabeled amino acid O-(2-[18F]fluoroethyl)-L-tyrosine (FET) after surgery or biopsy and completion of concomitant chemoradiation with temozolomide were evaluated. Static FET PET parameters such as maximum and mean tumor-to-brain ratios (TBRmax, TBRmean) and metabolic tumor volumes (MTV), and the dynamic FET PET parameter time-to-peak (TTP) were obtained. Additionally, FET PET radiomics features (n=1,303) were extracted for each patient and a test-retest analysis was performed to identify robust features prior to feature selection. The prognostic value of FET PET parameters and radiomics features was evaluated using receiver-operating-characteristic (ROC) analyses with regard to a significantly prolonged progression-free and overall survival (PFS, OS). Subsequently, univariate and multivariate survival estimates were performed to assess the prognostic value of these parameters to predict a significantly longer PFS and OS RESULTS ROC analyses revealed that the parameter TBRmax was a powerful parameter to predict both a significantly longer PFS (20.3 vs. 8.9 months; P=0.002; threshold, 2.75) and OS (34.9 vs. 18.7 months; P=0.005; threshold, 2.75). MTV had a similar prognostic value for both PFS (16.7 vs. 7.2 months; P=0.002; threshold, 34.0 mL) and OS (30.6 vs. 14.8 months; P=0.002; threshold, 32.6 mL). After feature selection, 3 of 15 selected radiomics features predicted a significantly longer PFS and OS in univariate analyses. TBRmax, MTV, and one of 15 radiomics features remained significant in multivariate survival analysis (all P≤0.03) CONCLUSION Our results suggest that FET PET and radiomics parameters were highly prognostic in this group of patients at an early stage of first-line therapy.

Systematic review of risk prediction tools for primary cutaneous melanoma outcomes and validation of sentinel lymph node positivity prediction in a UK tertiary cohort.

It is difficult for clinicians to make predictions for cancer progression or outcomes based on AJCC staging for individual patients. Models individualising risk prediction for clinical outcomes are developed using patient level data, advanced statistical techniques, and artificial intelligence. A systematic search identified cutaneous melanoma prognostic prediction tools published between January 1985-March 2023. Population comparisons of key clinico-pathological variables, external prediction of receiver operating characteristics and calibration analysis are applied to an unselected group of patients undergoing sentinel lymph node biopsy in a UK University hospital setting (n = 1564). Twenty-nine models were identified which predicted survival, disease recurrence or sentinel lymph node positivity (Internal validation n = 19 and external validation n = 14). 3 out of 7 tools for sentinel node positivity were contemporaneous with available characteristics for external validation. External validation of models by Lo et al. Friedman et al. & Bertolli et al. highlighted good discriminative performance (AUC 68.1% (64.5-71.8%), 77.1% (66.8-85.7%) & 68.6% (63.3-74.1%) respectively) but were sub-optimally calibrated for the UK patient cohort (Calibration intercept & slope Friedman: -4.01 & 32.92, Lo: -1.17 & 0.44, Bertolli: -2.75 & 4.88). This work highlights the complexity of predictive modelling and the rigorous validation process necessary to ensure accurate predictions. Our search highlights a tendency to focus on discriminative performance over calibration, and the possibility for inconsistent predictions when tools are applied to populations with differing characteristics.

Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer patient-derived xenografts.

Patient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and organoids - in terms of structural complexity, heterogeneity, and stromal interactions. Here, we characterise 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic levels, along with their response to cetuximab, an EGFR inhibitor used clinically for metastatic colorectal cancer. After evaluating the PDXs' quality, stability, and molecular concordance with publicly available patient cohorts, we present results from training, interpreting, and validating the integrative ensemble classifier CeSta. This model takes in input the PDXs' multi-omic characterisation and predicts their sensitivity to cetuximab treatment, achieving an area under the receiver operating characteristics curve > 0.88. Our study demonstrates that large PDX collections can be leveraged to train accurate, interpretable drug sensitivity models that: (1) better capture patient-derived therapeutic biomarkers compared to models trained on CCL data, (2) can be robustly validated across independent PDX cohorts, and (3) could contribute to the development of future therapeutic biomarkers.

Elevated serum growth differentiation factor 15 and decorin predict the fibrotic progression of metabolic dysfunction-associated steatotic liver disease.

Mitochondrial dysfunction with oxidative stress contributes to metabolic dysfunction-associated steatotic liver disease (MASLD) progression. We aimed to evaluate the fibrosis predictive efficacy of a novel non-invasive diagnostic panel using metabolic stress biomarkers. From a population-based general cohort, 144 subjects with MASLD were recruited in the development group and underwent magnetic resonance imaging-based liver examinations, anthropometric and laboratory tests. As an external validation group, 41 patients enrolled in a biopsy-evaluated MASLD cohort participated in this study. Liver fat content and stiffness were measured by magnetic resonance (MR) imaging-proton density fat fraction and MR elastography (MRE), respectively. Serologic stress biomarkers were quantitated by ELISA. Multivariate regression showed that waist-to-height ratio, growth differentiation factor-15 (GDF15), γ-glutamyltransferase, decorin, and alkaline-phosphatase were independent predictors of hepatic fibrosis (rank-ordered by Wald). The area under receiver-operator characteristics curve [AUROC (95% CI)) of the metabolic stress index for fibrosis (MSI-F) was 0.912 (0.85‒0.98) and 0.977 (0.92‒1.00) in development and validation groups, respectively. MSI-F also had better diagnostic accuracy (82.6‒92.4%) than other fibrosis indices in the both study cohorts. MSI-F consistently differentiated fibrosis severities across cohorts of MRE-evaluated general population and biopsy-proven patients with MASLD, while other indices showed no or less discrimination. MSI-F, as a novel non-invasive index based on a stress-stimulated protective hormone GDF15 and decorin, effectively predicted hepatic fibrosis. Furthermore, MSI-F may serve as pre-screening tool to increase the population that could be excluded from further evaluation, reducing unnecessary invasive investigations more effectively than other indices.

The triglyceride-glucose index as an indicator of latent atherogenicity of the plasma lipid profile in healthy men with normolipidaemia

ABSTRACT Objectives The triglyceride-glucose (TyG) index is a novel diagnostic marker for various metabolic and cardiovascular diseases. However, little is known about the association of the TyG index with plasma atherogenicity, especially with its latent forms. The aim of this study was to assess the potential of the use of the TyG index as a marker of atherogenic risk. Methods A total of 202 men with normolipidaemia, aged 20–60 years, were enrolled in this study. Fasting biochemical parameters were measured. The TyG index was calculated as ln[triglyceride(mg/dL)×glucose(mg/dL)]/2. The diagnostic ability of the TyG index for detecting atherogenic risk was tested by receiver operating characteristic (ROC) curve analysis. Results A substantial portion of normolipidaemic men had deviations from the reference values for the indices calculated using apolipoproteins. Unfavorable values for the apolipoprotein (apo) B/apoA-I ratio, low-density lipoprotein cholesterol/apoB (LDL-C/apoB) ratio, and the atherogenic index (ATH index) were observed in 32.7%, 31.7%, and 14.4% of men, respectively. The results of ROC curve analysis showed that the TyG index had good diagnostic ability for identifying unfavorable apolipoprotein indices in normolipidaemic men. Conclusions Thus, the TyG index can be a valuable additional marker for assessing latent atherogenic risk; it can provide useful information for the diagnosis and treatment of early atherosclerosis.

Stratified Care in Cognitive Behavioural Therapy: A Comparative Evaluation of Predictive Modelling Approaches for Individualized Treatment: La stratification des soins pour l'orientation vers une thérapie cognitivo-comportementale: une évaluation comparative des approches de modélisation prédictive pour un traitement individualisé.

In response to high demand and prolonged wait times for cognitive behavioural therapy (CBT) in Ontario, Canada, we developed predictive models to stratify patients into high- or low-intensity treatment, aiming to optimize limited healthcare resources. Using client records (n = 953) from Ontario Shores Centre for Mental Health Sciences (January 2017-2021), we estimated four binary outcome models to assign patients into complex and standard cases based on the probability of reliable improvement in Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) scores. We evaluated two choices of cut-offs for patient complexity assignment: models at an ROC (receiver operating characteristic)-derived cut-off and a 0.5 probability cut-off. Final model effectiveness was assessed by assigning treatment intensity (high-intensity or low-intensity CBT) based on the combined performance of both GAD-7 and PHQ-9 models. We compared the treatment assignment recommendations provided by the models to those assigned by clinical assessors. The predictive models demonstrated higher accuracy in selecting treatment modality compared to provider-assigned treatment selection. The ROC cut-off achieved the highest prediction accuracy of case complexity (0.749). The predictive models exhibited large sensitivity and specificity trade-offs (which influence the rates of patient assignment to high-intensity CBT) despite having similar accuracy statistics (ROC cut-off = 0.749, 0.5 cut-off = 0.690), emphasizing the impact of cut-off choices when implementing predictive models. Overall, our findings suggest that the predictive model has the potential to improve the allocation of CBT services by shifting incoming clients with milder symptoms of depression to low-intensity CBT, with those at highest risk of not improving beginning in high-intensity CBT. We have demonstrated that models can have significant sensitivity and specificity trade-offs depending on the chosen acceptable threshold for the model to make positive predictions of case complexity. Further research could assess the use of the predictive model in real-world clinical settings.

The minimally clinically important difference in the 2-minute walk test for people in the subacute phase after a stroke

ABSTRACT Background The 2-Minute Walk Test (2MWT) is a simple and reliable test used by clinicians to assess gait function in people with stroke (pwST). No studies established the minimal clinically important difference (MCID) of the 2MWT. Objective To determine the MCID of the 2MWT in subacute pwST using data from a longitudinal cohort study. Methods PwST within 180 days of stroke onset were recruited from the Italian National Health System (NHS) rehabilitation services across the country. Participants underwent physical therapy to improve balance and gait according to their specific needs. The 2MWT was used to assess gait performance at the beginning (T0) and after a minimum of 10 rehabilitation sessions (T1). The Global Perceived Effect (GPE), Activities-specific Balance Confidence Scale (ABC) and the ABC_gait were used to assess balance confidence and the perceived effect of the intervention at T1. Results 51 pwST (69 ± 12 years; 66.7% males) were included in the analysis. Statistically significant improvements were observed in 2MWT, ABC, and ABC_gait scores after rehabilitation using the Wilcoxon signed-rank test. Using an anchor-based approach the receiver operating characteristic (ROC) curves were calculated to establish the MCID. The MCID of the 2MWT was 31 meters with an Area under the curve (AUC) = 0.74 [0.60–0.89], a specificity of 71% and a sensitivity of 63%. Conclusions An improvement of 31 meters on the 2MWT can be considered clinically significant in subacute pwST undergoing rehabilitation. This study provides valuable insights for clinicians to assess walking performance in pwST and determine clinically meaningful changes post-rehabilitation.

Expression of Salivary miRNAs, Clinical, and Demographic Features in the Early Detection of Gastric Cancer: A Statistical and Machine Learning Analysis.

Gastric cancer ranks as one of the top five deadliest cancers worldwide and is often diagnosed at late stages. Analysis of saliva may provide a non-invasive approach for detection of malignancies in organs associated with the oral cavity. This research aims to analyze salivary microRNA expression together with clinical and demographic features with the aim of diagnosing gastric cancer. The study included 19 patients with early-stage gastric cancer and 19 healthy controls. Saliva samples were collected and processed for RNA isolation. Salivary expression of miR-223-3p and miR-21-5p were measured using quantitative reverse-transcription polymerase chain reaction (RT-qPCR). Receiver operating characteristic (ROC) curves were generated to evaluate the accuracy of diagnostic models. Machine learning algorithms, multiple logistic regression, and principal component analysis (PCA) were used to assess the predictive power of miRNAs in conjunction with clinical-demographic features. Significant upregulation of miR-223-3p and downregulation of miR-21-5p in saliva were observed in patients with gastric cancer. The area under ROC curve (AUC) values for salivary miR-21-5p, salivary miR-223-3p, and their multiple logistic regression were determined to be 0.723, 0.791, and 0.850, respectively. The AUC for multiple logistic regression model was 0.919. The PCA model led to the highest diagnostic odds ratio (DOR) of 134.33 (sensitivity = 0.785, specificity = 1.00, AUC = 903). Application of machine learning methods, and in particular a random forest algorithm, showed high accuracy in diagnosing patients with gastric cancer (sensitivity = 1.00, specificity = 0.857, AUC = 0.93). The application of validated salivary diagnostics in clinical practice could help facilitate earlier diagnosis of gastric cancer and improve medical outcome. Expression of miR-21 and miR-223-3p in saliva together with clinical and demographic features, appears promising in screening for GC.

Establishment of potential lncRNA-related hub genes involved competitive endogenous RNA in lung adenocarcinoma

Long non-coding RNAs (lncRNAs) have a notable role in the diagnosis and prognosis of cancer. However, the associations between lncRNA-related hub genes (LRHGs) expression and the corresponding outcomes have not been fully understood in lung adenocarcinoma (LUAD). Here, a total of 71 patients diagnosed with LUAD and 60 healthy volunteers at The First Affiliated Hospital of Huzhou University from April, 2023 to December, 2023 were enrolled in the present study. A LRHGs model was established using least absolute shrinkage and selection operator analyses of The Cancer Genome Atlas-LUAD datasets. The underlying mechanisms of the LRHGs were investigated via Gene Set Enrichment Analysis and Gene Set Variation Analysis. Additionally, the diagnostic role of serum HOXD cluster antisense RNA 2 (HOXD-AS2) was assessed by receiver operating characteristic (ROC) curve analysis. Lastly, TCGA-LUAD samples were divided into high- and low-HOXD-AS2 expression groups based on the median expression. The associations between HOXD-AS2 expression and miR-4538 as well as Calmodulin-Dependent Protein Kinase Type II subunit Beta (CAMK2B) levels were conducted through Pearson correlation analysis. A comprehensive analysis identified 141 differentially expressed lncRNAs between 539 LUAD tissues and 59 normal samples. A prognostic marker for overall survival was established by constructing a predictive signature consisting of 9 LRHGs. Subsequently, 474 LUAD samples were categorized into a high or low-risk group based on the median of the risk score. An independent prognostic model was constructed to confirm the validity of this categorization. Further comparisons of the clinicopathological features and LRHG-related pathways were performed between the two groups. Examinations of LRHG expression in two LUAD clusters and of the association between LRHG expression and immune infiltration were also conducted. HOXD-AS2 expression was shown to be elevated in LUAD tissues compared with matched normal tissues, and the serum HOXD-AS2 level was also notably increased in LUAD samples compared with healthy controls. The results of the ROC analysis indicated that the sensitivity and specificity of HOXD-AS2 were higher than that of cytokeratin-19 fragment (CYFRA21-1), which is a serum marker for LUAD. Pearson analyses indicated that miR-4538 level was negatively associated with HOXD-AS2 expression, but CAMK2B level showed positive correlation in LUAD. The results of the present study therefore indicated that the constructed LRHG model, particularly HOXD-AS2, could independently diagnose and predict the prognosis of LUAD, which suggested the underlying mechanism of the HOXD-AS2/miR-4538/CAMK2B, and might offer efficient strategies for LUAD treatment.

Exploring the ceRNA network involving AGAP2-AS1 as a novel biomarker for preeclampsia

Preeclampsia (PE) is an important research subject in obstetrics. Nevertheless, the underlying mechanisms of PE remain elusive. PE-related expression datasets (GSE96983, GSE96984 and GSE24129) were downloaded from the Gene Expression Omnibus (GEO) database. Firstly, the differentially expressed messenger RNAs (DE-mRNAs), DE-microRNA (DE-miRNAs) and DE-long non-coding RNA (DE-lncRNAs) between PE and control cohorts were identified, and the ceRNA network was constructed. Then candidate hub genes were obtained through five algorithms by the protein-protein intersection (PPI) network of the mRNAs. Further, five hub genes were identified by receiver operating characteristic (ROC) curve and gene expression profiles: DAXX, EFNB1, NCOR2, RBBP4 and SOCS1. The function of 5 hub genes was analyzed and the interaction between drugs and hub genes was predicted. A total of 5 small molecule drugs were predicted, namely benzbromarone, 9,10-phenanthrenequinone, chembl312032, insulin and aldesleukin. AGAP2-AS1 was mainly located in exosome and cytoplasm. Agap2-as1-related regulatory subnetworks were extracted from ceRNA networks which included 41 mRNAs, 2 miRNAs and 1 lncRNA, including the regulated relationship pairs AGAP2-AS1-hsa-miR-497-5p-SRPRB, and AGAP2-AS1-hsa-miR-195-5p-RPL36. In summary, we constructed a competitive endogenous RNA (ceRNA) network to identify five potential biomarkers (DAXX, EFNB1, NCOR2, SOCS1 and RBBP4) of PE. The in-depth analysis of the AGAP2-AS1 regulatory network will help to uncover more important molecules closely related to PE and provide a scientific Reference.

Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data

AimsTo develop and validate an individualized nomogram for differentiating the histologic subtypes (adenocarcinoma and squamous cell carcinoma) of subpleural non-small cell lung cancer (NSCLC) based on ultrasound parameters and clinical data.MethodsThis study was conducted retrospectively between March 2018 and December 2019. Patients were randomly assigned to a development cohort (DC, n=179) and a validation cohort (VC, n=77). A total of 7 clinical parameters and 16 ultrasound parameters were collected. Least absolute shrinkage and selection operator regression analysis was employed to identify the most significant predictors utilizing a 10-fold cross-validation. The multivariate logistic regression model was applied to investigate the relevant factors. An individualized nomogram was then developed. Receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were applied for model validation in both DC and VC.ResultsFollowing the final regression analysis, gender, serum carcinoembryonic antigen, lesion size and perfusion defect in contrast-enhanced ultrasound were entered into the nomogram. The model showed moderate predictive ability, with an area under the ROC curve of 0.867 for DC and 0.838 for VC. The calibration curves of the model showed good agreement between actual and predicted probabilities. The ROC and DCA curves demonstrated that the nomogram exhibited a good predictive performance.ConclusionWe developed a nomogram that can predict the histologic subtypes of subpleural NSCLC. Both internal and external validation revealed optimal discrimination and calibration, indicating that the nomogram may have clinical utility. This model has the potential to assist clinicians in making treatment recommendations.

Combined fibrinogen concentration and neutrophil-to-lymphocyte ratio, an integrative model of the inflammatory response and coagulation cascades, for predicting prognosis in patients with upper tract urothelial carcinoma.

Inflammation and coagulation cascades are closely correlated with cancer occurrence and progression. This study investigated the prognostic value of the combination of plasma fibrinogen level and neutrophil-to-lymphocyte ratio (F-NLR) in patients with upper tract urothelial carcinoma (UTUC). The predictive ability of the F-NLR for overall survival (OS), cancer-specific survival (CSS), and progression-free survival (PFS) was initially established and then further validated in patients who underwent radical nephroureterectomy for UTUC. As a result, patients were divided into three groups following the establishment of cut-off values for the NLR (≥2.53 vs. <2.53) and fibrinogen (≥4.55 vs. <4.55) through receiver operating characteristic (ROC) curve analysis: F-NLR score 0 (low fibrinogen and low NLR), 2 (high fibrinogen and high NLR), or 1 (remaining patients). The F-NLR score was then identified as an independent risk factor for OS, CSS, and PFS (all P-value <0.05) by multivariate regression analysis in both the training and validation cohorts. In addition, F-NLR-based nomograms for OS, CSS, and PFS were developed and evaluated using the concordance index (C-index) and calibration curves. The integration of the F-NLR into existing nomograms improved predictive accuracy compared to the use of nomograms without the F-NLR score. This suggests that the addition of F-NLR is beneficial for enhancing the accuracy of prognosis prediction in patients with UTUC. The F-NLR score may serve as a powerful predictor for patients with UTUC.

Application Value of Two-Dimensional Ultrasound Combined With Three-Dimensional Power Doppler in the Prediction of Macrosomia in Early Pregnancy.

To investigate the value of two-dimensional (2D) ultrasound combined with three-dimensional power Doppler (3D-PD) in the prediction of macrosomia in early pregnancy. A total of 171 singleton pregnant women who underwent prenatal ultrasound examination at 11+0-13+6 weeks of gestation in the First Affiliated Hospital of Shihezi University from October 2021 to October 2022 were selected as the research subjects. They were followed up until delivery. Their general data and Doppler ultrasound parameters were recorded and analyzed. The differences in ultrasound data between the two groups were compared, and the receiver operating characteristic (ROC) curve was drawn to evaluate the parameters. Compared with the normal group, the macrosomia group had significantly higher biparietal diameter, placental volume, and placental microvascular index (p < 0.05). Increased placental vascular perfusion occurs in macrosomia during early pregnancy. The vascularization-flow index (VFI) had a high predictive value for macrosomia (AUC = 0.826), and 2D ultrasound combined with 3D-PD had the best predictive efficiency for macrosomia in early pregnancy (AUC = 0.926). Future studies should also consider combining maternal and fetal factors to predict macrosomia, to reduce the occurrence of adverse pregnancy outcomes.

Prospectively predicting 6-month risk for non-suicidal self-injury among adolescents after psychiatric hospitalization based on a predictive model

BackgroundIt is challenging to predict the occurrence of non-suicidal self-injury (NSSI) among adolescents over short periods. Moreover, the predictive value of indices for NSSI remains elusive. Thus, this study aimed to identify predictors of NSSI within 6 months among adolescents after psychiatric hospitalization by establishing a risk assessment model.MethodsA total of 632 high-risk participants were included in this study. The distribution characteristics of adolescent NSSI were initially assessed through a cross-sectional survey, following which risk factors were identified using logistic regression analysis. The risk score method was then used to construct a 6-month risk assessment model for NSSI. Lastly, the predictive effect of the model was evaluated by indicators such as the area under the receiver operating characteristic (ROC) curve and the positive predictive value.ResultsAfter 6 months, 412 cases of NSSI were identified. According to the logistic regression model, the frequency of relapses, medication status, and NSSI history were identified as influencing factors. Higher scores on the Impulsive Behavior Scale and Pittsburgh Sleep Quality Index were associated with a higher risk of NSSI. Conversely, higher scores on the Pain and Belief Perception Scale were correlated with a lower risk of NSSI. Moreover, the area under the ROC curve for the predictive model was 0.9989, with a 95% confidence interval of (0.9979, 0.9999), highlighting its high predictive ability and accuracy. The predictive model was validated using 78 patients, yielding an area under the ROC curve of 0.9703 and a 95% confidence interval of (0.9167, 0.9999), demonstrating outstanding predictability.ConclusionThese results collectively showed that the predictive model could accurately predict adolescent NSSI. Thus, the model’s primary variables may be applied to predict the risk of NSSI in the clinical setting.

Cervicovaginal lavages uncover growth factors as key biomarkers for early diagnosis and prognosis of endometrial cancer

Endometrial cancer (EC) rates are continuing to rise and it remains the most common gynecologic cancer in the US. Existing diagnostic methods are invasive and can cause pain and anxiety. Hence, there is a need for less invasive diagnostics for early EC detection. The study objective was to evaluate the utility of growth factors collected through minimally invasive cervicovaginal lavage (CVL) sampling as diagnostic and prognostic biomarkers for EC. CVL samples from 192 individuals undergoing hysterectomy for benign or malignant conditions were collected and used to quantify the concentrations of 19 growth and angiogenic factors using multiplex immunoassays. Patients were categorized based on disease groups: benign conditions (n = 108), endometrial hyperplasia (n = 18), and EC (n = 66). EC group was stratified into grade 1/2 endometrial endometrioid cancer (n = 53) and other EC subtypes (n = 13). Statistical associations were assessed using receiver operating characteristics, Spearman correlations and hierarchical clustering. Growth and angiogenic factors: angiopoietin-2, endoglin, fibroblast activation protein (FAP), melanoma inhibitory activity, and vascular endothelial growth factor-A (VEGF-A) were significantly (p < 0.0001) elevated in EC patients. A multivariate model combining 11 proteins with patient age and body mass index exhibited excellent discriminatory potential (area under curve = 0.918) for EC, with a specificity of 90.7% and a sensitivity of 87.8%. Moreover, angiopoietin-2, FAP and VEGF-A significantly (p < 0.05–0.001) associated with tumor grade, size, myometrial invasion, and mismatch repair status. Our results highlight the innovative use of growth and angiogenic factors collected through CVL sampling for the detecting endometrial cancer, showcasing not only their diagnostic potential but also their prognostic value.