Receiver Operating Characteristic Analysis Research Articles

Impact of tumour stroma-immune interactions on survival prognosis and response to neoadjuvant chemotherapy in bladder cancer

The tumour stroma is associated with unfavourable prognosis in diverse solid tumours, but its prognostic and predictive value in bladder cancer (BCa) is unclear. In this multicentre, retrospective study, we included 830 patients with BCa from six independent cohorts. Differences in overall survival (OS) and cancer-specific survival (CSS) were investigated between high-tumour stroma ratio (TSR) and low-TSR groups. Multi-omics analyses, including RNA sequencing, immunohistochemistry, and single-cell RNA sequencing, were performed to study stroma-immune interactions. TSR prediction models were developed based on pelvic CT scans, and the best performing model was selected based on receiver operator characteristic analysis. Compared to low-TSR tumours, high-TSR tumours were significantly associated with worse OS (HR=1.193, 95% CI: 1.046-1.361, P=0.008) and CSS (HR=1.337, 95% CI: 1.139-1.569, P<0.001), and lower rate of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). High-TSR tumours exhibited higher infiltration of immunosuppressive cells, including Tregs and tumour-associated neutrophils, while low-TSR tumours exhibited higher infiltration of immune-activating cells such as CD8+ Teff and XCR1+ dendritic cells. The TSR prediction model was developed by combining the intra-tumour and tumour base radiomics features, and showed good performance to predict high-TSR, as indicted by area under the curve of 0.871 (95% CI: 0.821-0.921), 0.821 (95% CI: 0.731-0.911), and 0.801 (95% CI: 0.737-0.865) in the training, internal validation, and external validation cohorts, respectively. In patients with low predicted TSR, 92.3% (12/13) achieved pCR, while only 35.3% (6/17) of patients with high predicted TSR achieved pCR. The tumour stroma was found to be significantly associated with clinical outcomes in patients with BCa as a result of tumour stroma-immune interactions. The radiomics prediction model provided non-invasive evaluation of TSR and was able to predict pCR in patients receiving NAC for BCa. This work was supported by National Natural Science Foundation of China (Grant No. 82373254 and 81961128027), Guangdong Provincial Natural Science Foundation (Grant No. 2023A1515010258), Science and Technology Planning Project of Guangdong Province (Grant No. 2023B1212060013). Science and Technology Program of Guangzhou (SL2022A04J01754), Sun Yat-Sen Memorial Hospital Clinical Research 5010 Program (Grant No. SYS-5010Z-202401).

Individualized radiology screening for newly diagnosed nasopharyngeal carcinoma

ObjectivesCurrent guidelines recommend universal PET/CT screening for metastases staging in newly diagnosed nasopharyngeal carcinoma (NPC) despite the low rate of synchronous distant metastasis (SDM). The study aims to achieve individualized screening recommendations of NPC based on the risk of SDM. Methods and materials18 pre-treatment peripheral blood indicators was retrospectively collected from 2271 primary NPC patients. A peripheral blood risk score (PBRS) was constructed by indicators associated with SDM on least absolute shrinkage and selection operator (LASSO) regression. The PBRS-based distant metastases (PBDM) model was developed from features selected by logistic regression analyses in the training cohort and then validated in the validation cohort. Receiver operator characteristic curve analysis, calibration curves, and decision curve analysis were applied to evaluate PBDM model performance. ResultsPre-treatment Epstein–Barr viral DNA copy number, percentage of total lymphocytes, serum lactate dehydrogenase level, and monocyte-to-lymphocyte ratio were most strongly associated with SDM in NPC and used to construct the PBRS. Sex (male), T stage (T3-4), N stage (N2-3), and PBRS (≥1.076) were identified as independent risk factors for SDM and applied in the PBDM model, which showed good performance. Through the model, patients in the training cohort were stratified into low-, medium-, and high-risk groups. Individualized screening recommendations were then developed for patients with differing risk levels. ConclusionThe PBDM model offers individualized recommendations for applying PET/CT for metastases staging in NPC, allowing more targeted screening of patients with greater risk of SDM compared with current recommendations.

Predictive value of the neutrophil percentage-to-albumin ratio for coronary atherosclerosis severity in patients with CKD

BackgroundThe neutrophil percentage-to-albumin ratio (NPAR), which is defined as the percentage of neutrophils divided by the concentration of albumin, is a cost-effective and readily available biomarker of inflammation. This study aimed to evaluate the association between the NPAR and the severity of coronary atherosclerosis in patients with chronic kidney disease (CKD).MethodsA total of 280 CKD patients who underwent coronary angiography were retrospectively enrolled in this study. The severity of coronary atherosclerosis was evaluated using the Gensini score (GS). Patients were divided into low-, medium- and high-NPAR groups according to the tertiles of the NPAR values. Logistic regression analysis was conducted to analyze the relationship between the NPAR and the GS. The cutoff points for the sensitivity and specificity of the NPAR in predicting the GS were estimated via receiver operating characteristic (ROC) analysis.ResultsThere was a higher prevalence of coronary artery disease (CAD) among CKD patients with higher NPARs (P =0.041). More patients in the high-NPAR group had complex CAD (triple-vessel disease and/or left main coronary artery stenosis) and chronic total occlusion lesions, and more of these patients required revascularization therapy (P<0.05). Multivariate logistic regression analysis revealed a significant positive correlation between the NPAR and the severity of coronary stenosis (adjusted OR 2.68, 95% CI 1.25-5.76, p=0.012), particularly among female and older (age ≥65) patients. The ROC analysis indicated that the optimal cutoff value for the NPAR in predicting severe coronary artery stenosis (GS>60) in CKD patients was 1.91 (sensitivity 0.495, specificity 0.749), with an area under the curve (AUC) of 0.650 (95% CI 0.581-0.719, P<0.001). A subgroup analysis according to sex revealed that the NPAR exhibited stronger predictive value in female patients (AUC 0.730, 95% CI 0.643-0.817) than in male patients (AUC 0.565, 95% CI 0.460-0.670) (P<0.001), and the optimal cutoff value for the NPAR in female patients was 1.80 (sensitivity 0.667, specificity 0.705).ConclusionsOur study demonstrated that the NPAR is independently associated with the severity of coronary atherosclerosis in CKD patients, especially in female and elderly patients (≥65 years old). Moreover, the NPAR can effectively predict the severity of coronary atherosclerosis, exhibiting greater predictive value in females than in males.

Correlation of hematocrit and cardiovascular events in peritoneal dialysis patients

Objective: To investigate the correlation between hematocrit (HCT) and cardiovascular events in peritoneal dialysis (PD) patients. Methods: Patients undergoing maintenance PD in the PD center of Guizhou Provincial People's Hospital from March 19, 2012 to July 9, 2020 were included. Demographic, baseline clinical and laboratory data of the patients were collected and patients were followed up until April 8, 2022. The primary endpoint was the first occurrence of a cardiovascular event. According to the tertiles of baseline HCT, the patients were divided into group Q1 (HCT≤26.6%), group Q2 (HCT>26.6%-32.4%), and group Q3 (HCT>32.4%). Laboratory indexes and cardiovascular events were compared among the three groups. Kaplan-Meier survival curve, Cox regression analysis and sensitivity analysis were used to analyze the effect of HCT on cardiovascular outcomes. Receiver operating characteristic (ROC) curve was used to analyze the predictive value of HCT for cardiovascular events in PD patients. Results: A total of 860 PD patients were included, including 494 males (57.4%) and 366 females (42.6%), with a mean age of (41.5±15.0) years. There were 287 cases in group Q1, 289 cases in group Q2, and 284 cases in group Q3, respectively. A total of 265 (30.8%) patients experienced first cardiovascular events during the follow-up period. The incidence of cardiovascular events in groups Q1, Q2 and Q3 was 36.2% (104/287), 34.3% (99/289), and 21.8% (62/284), respectively, with a statistically significant difference (P<0.001). The incidence of cardiovascular events decreased with the increase of HCT. Multivariate Cox proportional hazards regression model analysis showed that decreased HCT was a risk factor for cardiovascular events. Compared with group Q3, the risk of cardiovascular events in group Q1 increased by 50.7% (group Q2: HR=1.444, 95%CI: 1.029-2.028, P=0.034; group Q1: HR=1.570, 95%CI: 1.096-2.250, P=0.014). In the sensitivity analysis, using kidney transplantation as the competition event, the risk of cardiovascular events was lower in group Q3 than that in group Q1 (subdistributional HR=1.413, 95%CI: 1.006-1.990, P=0.046). Kaplan-Meier survival curve showed that compared with the other two groups, the cardiovascular events-free survival rate of patients in group Q1 was significantly lower (log-rank χ2=9.722, P=0.008). ROC analysis showed that the area under the curve (AUC) of HCT for predicting cardiovascular events in PD patients was 0.583 (95%CI: 0.542-0.623, P<0.001), with the sensitivity of 40.6% and the specificity of 75.1%. Conclusion: Low-level HCT is associated with an increased risk of the first cardiovascular event in PD patients.

Short- and long-term performance of risk calculation tools for mortality in patients with acute coronary syndrome

BackgroundThe mortality rate of acute coronary syndrome (ACS) remains high. Therefore, patients with ACS should undergo early risk stratification, for which various risk calculation tools are available. However, it remains uncertain whether the predictive performance varies over time between risk calculation tools for different target periods. This study aimed to compare the predictive performance of risk calculation tools in estimating short- and long-term mortality risks in patients with ACS, while considering different observation periods using time-dependent receiver operating characteristic (ROC) analysis.MethodsThis study included 404 consecutive patients with ACS who underwent coronary angiography at our hospital from March 2017 to January 2021. The ACTION and GRACE scores for short-term risk stratification purposes and CRUSADE scores for long-term risk stratification purposes were calculated for all participants. The participants were followed up for 36 months to assess mortality. Using time-dependent ROC analysis, we evaluated the area under the curve (AUC) of the ACTION, CRUSADE, and GRACE scores at 1, 6, 12, 24, and 36 months.ResultsSixty-six patients died during the observation periods. The AUCs at 1, 6, 12, 24, and 36 months of the ACTION score were 0.942, 0.925, 0.889, 0.856, and 0.832; those of the CRUSADE score were 0.881, 0.883, 0.862, 0.876, and 0.862; and those of the GRACE score 0.949, 0.928, 0.888, 0.875, and 0.860, respectively.ConclusionsThe ACTION and GRACE scores were excellent risk stratification tools for mortality in the short term. The prognostic performance of each risk score was almost similar in the long term, but the CRUSADE score might be a superior risk stratification tool in the longer term than 3 years.

Risk factors for pneumonia among children with coinfection of influenza A virus and Mycoplasma pneumoniae.

To investigate the clinical characteristics and risk factors for pneumonia in children co-infected with influenza A virus (IAV) and Mycoplasma pneumoniae (MP). Children who were diagnosed with IAV and MP infection between January and December, 2023 were enrolled and divided into a non-pneumonia group and a pneumonia group. Univariate analysis and logistic regression analysis were used to evaluate each index, and the risk factors for pneumonia caused by coinfection in the two groups were explored. A total of 209 patients were enrolled, of which 107 and 102 patients were in the pneumonia and non-pneumonia groups, respectively. The patients in the pneumonia group were older and had a longer duration of fever (P < 0.05). Univariate analysis revealed that the median age, duration of fever, and CD3+, CD4+, CD8+ and IL-10 levels were significantly correlated with pneumonia (P < 0.05). Multivariate logistic regression analysis revealed that the median age, duration of fever, and CD4+, CD8+ and IL-10 levels were independent risk factors for pneumonia. Area under the curve of the five combined indicators in the ROC (receiver operator characteristic) analysis was 0.883, was higher than single factor. Children with IAV and MP infection whose age older than 6.08years, had a fever longer than 4days, had a CD4+ count < 22.12%, had a CD8+ count < 35.21%, had an IL-10 concentration > 22.08 ng/ml were more likely to develop pneumonia.

Quantifying local cardiac substrate heterogeneity from high density recordings: an experimental study

Abstract Background and Purpose High-density (HD) grid catheters offer enhanced local electrical conduction characterization, owing to their unique grid arrangement. This arrangement enables the generation of orientation-independent EGMs, termed omnipolar EGMs (oEGMs), overcoming limitations associated with traditional unipolar and bipolar EGMs. This innovation holds potential for differentiating healthy tissue from scarred or arrhythmogenic areas. In this study, we introduce a novel metric, the Vector Field Heterogeneity (VFH), designed to quantify the heterogeneity of propagation wavefronts in local vector maps derived from oEGMs. Methods We developed and validated the VFH metric through a combination of simulations and experiment conducted on Langendorff-perfused rabbit hearts. Simulations involved creating a model that generated 100,000 simulated maps exhibiting various levels of disorganization. For experimental data, 68 propagation maps were utilized, including 29 from basal recordings, 39 from recordings with stimulation (4 or 6Hz), from five Langendorff-perfused rabbit hearts at 37ºC, using a multielectrode mapping catheter with 128 electrodes spaced 1mm apart. The VFH metric, based on vector field theory (see figure 2), was computed and compared to the widely adopted the Spatial Inhomogeneity Index (SI) from Statistical analysis was then performed to assess the distribution of heterogeneity values. Results The analysis of the VFH metric through simulations revealed a linear increase in VFH values with disorganization up to a 45º angle, followed by a plateau. Different catheter grid sizes demonstrated that larger grids reduced the standard deviation, with the VFH metric converging to a lower value of approximately 0.62 for larger grids. Experimental data indicated that for a 4x4 grid, VFH values averaged 0.35 without stimulation and decreased to 0.11 with stimulation. The VFH metric consistently outperformed the SI index in binary classification of basal vs stimulated conditions, as evidenced by higher Area Under Curve (AUC) values in both Receiver Operating Characteristic (ROC) and Precision-Recall (PR) analyses across various catheter sizes. For example, in the 4x4 grid, the AUROC for the VFH metric was 0.9752 compared to 0.8311 for the SI index, showcasing the VFH metric’s superior accuracy in assessing cardiac wavefront heterogeneity (see figure 2). Conclusion Vector Field Heterogeneity (VFH) emerges as an effective tool for characterizing cardiac tissue. It successfully differentiates between stimulated and non-stimulated tissue, accurately representing progressive disorganization in simulated maps. The VFH metric demonstrates superiority over the established Spatial Inhomogeneity (SI) index, making it a reliable parameter for assessing cardiac heterogeneity, especially on small high-density grids. It holds promise for clinical use in studying and understanding the cardiac substrate during arrhythmic conditions.Figure 1.Abstract Figure.Figure 2.Methods and Results.

#635 Effect of multi-biomarker panels based on urinary N-terminal osteopontin on prediction of diabetic kidney disease in patients with diabetes mellitus

Abstract Background and Aims Diabetic kidney disease (DKD), occurs in 20–40% of patients with diabetes mellitus (DM), is the leading cause of end-stage renal disease (ESRD). DKD is a clinical diagnosis mainly based on the persistent albuminuria and reduced estimated glomerular filtration rate (eGFR) [1]. However, increased UACR and reduced eGFR are the final consequences due to DKD, novel biomarkers are critical for predicting DKD development. Osteopontin (OPN) is a profibrotic adhesion phosphoprotein that participates in cell chemotaxis, adhesion, migration, and proliferation, as well as extracellular matrix (ECM) hyperplasia. Hyperglycemia could enhance OPN gene expression through the activation of the renin-angiotensin system (RAS), mTOR pathway, NF-κB, and TGF-β pathway and then cause podocyte injury and ECM hyperplasia. Therefore, OPN upregulation is not only the result of various pathophysiological processes in DKD, but also results in kidney injury. Osteopontin (OPN) could predict incident DKD in DM patients [2], N-terminal OPN (ntOPN) has a stronger profibrotic adhesion effect than full-length OPN. This study aims to reveal the clinical benefit of ntOPN as a potential marker to identify DM patients at high risk of DKD, and establish ntOPN-based diagnostic and forecast models for renal outcomes in DM patients. Method We performed a cross-sectional study of 316 adults with Type 1 DM≥ 5 years or Type 2 DM, then followed by a prospective observational cohort study of 143 adult DM patients without renal involvement at baseline and follow-up for at least one year. During the follow-up period, the primary endpoint was “DKD occurrence”, defined as the presence of one of the following conditions in DM patients [3]: (1) repeat UACR ≥30 mg/g at least 2 of 3 measurements within 3 to 6 months; (2) eGFR &amp;lt;60 mL/min/1.73 m2 for more than three months; (3) renal pathological findings were consistent with DKD. The secondary endpoint was “DKD progression”, which included: (1) eGFR sustained decreased by at least 25%; (2) development of ESRD, and/or need for renal replacement therapy; (3) death from the renal cause. Logistic regression analysis was performed to analyze the relationship between parameters and the events of DKD occurrence and progression. Receiver operator characteristic (ROC) analysis was used to assess the predictive ability of established models for clinical endpoints. Results The median value of urinary ntOPN (UntOPN) was 44.15 ng/ml in the cross-sectional cohort, DKD prevalence was significantly higher in the high UntOPN group than in the low UntOPN group. The ROC curves of UntOPN, urinary neutrophil gelatinase-associated lipocalin (UNGAL), and their combination indicated that both combination and ntOPN alone perform better than UNGAL for DKD diagnosis (Fig. 1A). In the prospective cohort, UntOPN was an independent risk factor and further improved the predictive ability for DKD occurrence and DKD progression than UNGAL (Figs. 1B and 1C). Based on the parameters detected as risk factors for DKD occurrence and progression, we set up a series of multi-biomarker panels for DKD prediction using UntOPN, UNGAL, serum cystatin C, serum creatinine (Scr), UACR, and TCH/HDL-C ratio. Compared with the model of Scr + UACR, the area under ROC curve (AUC) of the six-biomarker model was higher, and also ranked the highest among the six ROC curves in predicting 1-year risk of DKD occurrence and DKD progression (Figs. 1B and 1C). Conclusion Our results showed that urinary ntOPN is associated with DKD development, and elevated urinary ntOPN is an independent predictor for DKD occurrence and progression. Compared with the traditional biomarkers of Scr + UACR, our multi-biomarker models based on urinary ntOPN performed better in predicting DKD development, which could provide more accurate tools for DKD risk prediction, thereby improving the renal prognosis in DM patients.

Preoperative prediction of HER-2 expression status in breast cancer based on MRI radiomics model

Objective: This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer. Methods: A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness. Results: The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model (P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model (P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort (P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort (P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions: T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics

BackgroundRecent studies have provided evidence supporting the functional role and mechanism of lactate in suppressing anticancer immunity. However, there is no systematic analysis of lactate metabolism-related genes (LMRGs) and ovarian cancer (OV) prognosis.ResultsSix genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) were selected as prognostic genes and a prognostic model was utilized. Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) analyses were further performed and indicated that the prognostic model was effective. Subsequently, the neoplasm_cancer_status and RiskScore were determined as independent prognostic factors, and a nomogram was established with relatively accurate forecasting ability. Additionally, 2 types of immune cells (Central memory CD8 T cell and Immature B cell), 4 types of immune functions (APC co inhibition, DCs, Tfh and Th1 cells), 9 immune checkpoints (BTLA, CTLA4, IDO1, LAG3, VTCN1, CXCL10, CXCL9, IFNG, CD27) and tumor immune dysfunction and exclusion (TIDE) scores were significantly different between risk groups. The expression of 6 genes were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and the expression of 6 genes were higher in the high-grade serous carcinoma (HGSC) samples.ConclusionA prognostic model related to lactate metabolism was established for OV based on six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) that could provide new insights into therapy.

The value of the malignant subregion-based texture analysis in predicting the Ki-67 status in breast cancer

ObjectiveTo evaluate the value of the malignant subregion-based texture analysis in predicting Ki-67 status in breast cancer.Materials and methodsThe dynamic contrast-enhanced magnetic resonance imaging data of 119 histopathologically confirmed breast cancer patients (81 patients with high Ki-67 expression status) from January 2018 to February 2023 in our hospital were retrospectively collected. According to the enhancement curve of each voxel within the tumor, three subregions were divided: washout subregion, plateau subregion, and persistent subregion. The washout subregion and the plateau subregion were merged as the malignant subregion. The texture features of the malignant subregion were extracted using Pyradiomics software for texture analysis. The differences in texture features were compared between the low and high Ki-67 expression cohorts and then the receiver operating characteristic (ROC) curve analysis to evaluate the predictive performance of texture features on Ki-67 expression. Finally, a support vector machine (SVM) classifier was constructed based on differential features to predict the expression level of Ki-67, the performance of the classifier was evaluated using ROC analysis and confirmed using 10-fold cross-validation.ResultsThrough comparative analysis, 51 features exhibited significant differences between the low and high Ki-67 expression cohorts. Following feature reduction, 5 features were selected to build the SVM classifier, which achieved an area under the ROC curve (AUC) of 0.77 (0.68–0.87) for predicting the Ki-67 expression status. The accuracy, sensitivity, and specificity were 0.76, 0.80, and 0.68, respectively. The average AUC from the 10-fold cross-validation was 0.72 ± 0.14.ConclusionThe texture features of the malignant subregion in breast cancer were potential biomarkers for predicting Ki-67 expression level in breast cancer, which might be used to precisely diagnose and guide the treatment of breast cancer.

Lipid-based insulin-resistance markers predict cardiovascular events in metabolic dysfunction associated steatotic liver disease

BackgroundInsulin resistance (IR) is the cornerstone of Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD), pathophysiologically being the key link between MASLD, metabolic disorders, and cardiovascular (CV) diseases. There are no prospective studies comparing the predictive values of different markers of insulin resistance (IR) in identifying the presence of MASLD and the associated risk of cardiovascular events (CVEs).MethodsPost hoc analysis of the prospective Plinio Study, involving dysmetabolic patients evaluated for the presence of MASLD. The IR markers considered were Homeostatic Model Assessment for IR (HOMA-IR), Triglycerides-Glycemia (TyG) index, Triglycerides to High-Density Lipoprotein Cholesterol ratio (TG/HDL-C), Lipid Accumulation Product (LAP) and Visceral Adiposity Index (VAI). Receiver operative characteristic (ROC) analyses were performed to find the optimal cut-offs of each IR marker for detecting MASLD and predicting CVEs in MASLD patients. Logistic and Cox multivariable regression analyses were performed, after dichotomizing the IR markers based on the optimal cut-offs, to assess the factors independently associated with MASLD and the risk of CVEs.ResultsThe study included 772 patients (age 55.6 ± 12.1 years, 39.4% women), of whom 82.8% had MASLD. VAI (Area Under the Curve [AUC] 0.731), TyG Index (AUC 0.723), and TG/HDL-C ratio (AUC: 0.721) predicted MASLD but was greater with HOMA-IR (AUC: 0.792) and LAP (AUC: 0.787). After a median follow-up of 48.7 (25.4–75.8) months, 53 MASLD patients experienced CVEs (1.8%/year). TyG index (AUC: 0.630), LAP (AUC: 0.626), TG/HDL-C (AUC: 0.614), and VAI (AUC: 0.590) demonstrated comparable, modest predictive values in assessing the CVEs risk in MASLD patients.ConclusionIn dysmetabolic patients HOMA-IR and LAP showed the best accuracy in detecting MASLD. The possible use of lipid-based IR markers in stratifying the CV risk in patients with MASLD needs further validation in larger cohorts.

The number of resected lymph nodes from the upper mediastinal area predicts long-term outcomes of esophageal squamous cell carcinoma after minimally invasive esophagectomy.

The total number of resected lymph nodes (LNs) is an important determinant of longer survival after esophagectomy for esophageal squamous cell carcinoma (ESCC). However, the resected LN counts from areas that affect long-term outcomes remain unclear. This study included 406 patients who underwent minimally invasive esophagectomies (MIEs) at Kobe University Hospital. Resected LN counts were evaluated in the following areas: upper mediastinal (UM), middle mediastinal (MM), lower mediastinal (LM), and abdominal (Abd). Cut-off values for LN counts from each area were determined using receiver operating characteristics analysis of the survival status. Cox proportional hazards regression analyses were performed to identify prognostic factors. The cut-off values for large or small numbers of resected LN counts in the UM, MM, LM, and Abd areas were 4, 8, 5, and 18, respectively, in patients with upper and middle thoracic (Ut/Mt) ESCC and 7, 6, 5, and 24, respectively, in patients with lower thoracic (Lt) ESCC. Multivariate analysis in patients with Ut/Mt ESCC revealed that tumor invasion depth, LN metastasis, and the resected LN count from the UM area were independent risk factors for overall survival [hazard ratio (HR), 7.04; 95% confidence interval (CI) 4.47-11.1; HR, 4.01; 95% CI 1.96-8.21; HR, 2.18; 95% CI 1.24-3.82, respectively]. In patients with Lt ESCC, tumor invasion depth, LN metastasis, and pulmonary complications were independent risk factors for overall survival (HR, 4.23; 95% CI 2.14-8.35; HR, 3.83; 95% CI 1.75-8.38; HR, 2.80; 95% CI 1.38-5.65, respectively). Resected LN counts from no areas were prognostic factors. The number of resected LNs from the UM area influenced the survival outcomes of patients with Ut/Mt ESCC after MIE.

A novel discriminant algorithm for differential diagnosis of mild to moderate thalassemia and iron deficiency anemia.

Mild to moderate thalassemia trait (TT) and iron deficiency anemia (IDA) are the most common conditions of microcytic hypochromic anemia (MHA) and they exhibit highly similar clinical and laboratory features. It is sometimes difficult to make a differential diagnosis between TT and IDA in clinical practice. Therefore, a simple, effective, and reliable index is needed to discriminate between TT and IDA. Data of 598 patients (320 for TT and 278 for IDA) were enrolled and randomly assigned to training set (278 of 598, 70%) and validation set (320 of 598, 30%). Stepwise discriminant analysis was used to define the best diagnostic formula for the discrimination between TT and IDA in training set. The accuracy and diagnostic performance of formula was tested and verified by receiver operating characteristic (ROC) analysis in validation set and its diagnostic performance was compared with other published indices. A novel formula, Thalassemia and IDA Discrimination Index (TIDI) = -13.932 + 0.434 × RBC + 0.033 × Hb + 0.025 ×MCHC + 53.593 × RET%, was developed to discriminate TT from IDA. TIDI showed a high discrimination performance in ROC analysis, with the Area Under the Curve (AUC) = 0.936, Youden' s index = 78.7%, sensitivity = 89.5%, specificity = 89.2%, respectively. Furthermore, the formula index also obtained a good classification performance in distinguishing 5 common genotypes of TT from IDA (AUC from 0.854-0.987). The new, simple algorithm can be used as an effective and robust tool for the differential diagnosis of mild to moderate TT and IDA in Guangxi region, China.

Investigation of the Shared Biomarkers in Heterotopic Ossification Between Ossification of the Ligamentum Flavum and Ankylosing Spondylitis.

Bioinformatics analysis of Gene Expression Omnibus (GEO). Ossification of the ligamentum flavum (OLF) and ankylosing spondylitis (AS) represent intricate conditions marked by the gradual progression of endochondral ossification. This investigation endeavors to unveil common biomarkers associated with heterotopic ossification and explore the potential molecular regulatory mechanisms. Microarray and RNA-sequencing datasets retrieved from the Gene Expression Omnibus (GEO) repository were harnessed to discern differentially expressed genes (DEGs) within the OLF and AS datasets. Subsequently, Weighted Gene Co-expression Network Analysis (WGCNA) was implemented to pinpoint co-expression modules linked to OLF and AS. Common genes were further subjected to an examination of functional pathway enrichment. Moreover, hub intersection genes were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by an evaluation of diagnostic performance in external OLF and AS cohorts. Lastly, an analysis of immune cell infiltration was conducted to scrutinize the correlation of immune cell presence with shared biomarkers in OLF and AS. A total of 1353 and 91 Differentially Expressed Genes (DEGs) were identified in OLF and AS, respectively. Using the Weighted Gene Co-expression Network Analysis (WGCNA), 2 modules were found to be notably significant for OLF and AS. The integrative bioinformatic analysis revealed 3 hub genes (MAB21L2, MEGF10, ISLR) as shared risk biomarkers, with MAB21L2 being the central focus. Receiver Operating Characteristic (ROC) analysis exhibited a strong diagnostic potential for these hub genes. Gene Ontology (GO) analysis indicated their involvement in the positive regulation of myoblast proliferation. Notably, MAB21L2 was singled out as the optimal common biomarker for OLF and AS. Furthermore, an analysis of immune infiltration demonstrated a correlation between MAB21L2 expression and changes in immune cells. Activated CD8 T cells were identified as shared differential immune infiltrating cells significantly linked to MAB21L2 in both OLF and AS. This study represents the first instance of identifying MAB21L2 as a prospective diagnostic marker for patients contending with OLF associated with AS. The research results indicate that the ECM-receptor interaction and the cell-cell adhesion may play a role in both disease processes. This newfound knowledge not only enhances our understanding of the pathogenesis behind spinal ligament ossification but also uncovers potential targets for therapeutic interventions.

Dual FDG/PSMA PET imaging to predict lesion-based progression of mCRPC during PSMA-RLT

Candidates for prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) of metastatic castration-resistant prostate cancer (mCRPC) frequently have “mismatch” lesions with pronounced 18-fluorodeoxyglucose ([18F]FDG) but attenuated PSMA ligand uptake on positron emission tomography (PET). However, no quantitative criteria yet exist to identify mismatch lesions and predict their response to RLT. To define such criteria, we retrospectively analyzed 267 randomly-selected glucometabolic mCRPC metastases from 22 patients. On baseline PET, we determined [18F]FDG and [68Ga]Ga-PSMA-11 maximum standardized uptake value (SUVmax), and calculated the [18F]FDG SUVmax/[68Ga]Ga-PSMA-11 SUVmax quotient (FPQ). From follow-up [18F]FDG PET after two lutetium-177-PSMA-617 RLT cycles, we evaluated the treatment response and categorized the lesions into three subgroups (partial remission, stable disease, progression) based on change in [18F]FDG SUVmax. Lastly, we compared the baseline PET variables in progressing versus non-progressing lesions. Variables differing significantly, and a score incorporating them, were assessed via receiver operator characteristic (ROC) curve analysis, regarding ability to predict lesional progression, with area under the curve (AUC) as metric. Cut-offs with optimal sensitivity and specificity were determined using the maximum value of Youden's index. Fifty-one of 267 lesions (19.1%) progressed, 102/267 (38.2%) manifested stable disease, and 114/267 (42.7%) partially responded after two RLT cycles. At baseline, median [68Ga]Ga-PSMA-11 SUVmax was significantly lower (p < 0.001), median FPQ significantly higher (p < 0.001), and median [18F]FDG SUVmax similar in progressing versus non-progressing lesions. [68Ga]Ga-PSMA-11 SUVmax and FPQ showed predictive power regarding progression (AUCs: 0.89, 0.90). An introduced clinical score combining both further improved predictive performance (AUC: 0.94). Optimal cut-offs to foretell progression were: [68Ga]Ga-PSMA-11 SUVmax < 11.09 (88.2% sensitivity, 81.9% specificity), FPQ ≥ 0.92 (90.2% sensitivity, 78.7% specificity), clinical score ≥ 6/9 points (88.2% sensitivity, 87.5% specificity). At baseline, a low [68 Ga]Ga-PSMA-11 SUVmax and a high FPQ predict early lesional progression under RLT; [18F]FDG SUVmax does not. A score combining [68 Ga]Ga-PSMA-11 SUVmax and FPQ predicts early lesional progression even more effectively and might therefore be useful to quantitatively identify mismatch lesions.

Predictive value of TCCD and regional cerebral oxygen saturation for detecting early postoperative brain injury.

This study aims to analyze the risk factors for early postoperative brain injury in patients undergoing cardiovascular surgery and explore the predictive value of transcranial color Doppler (TCCD) and regional cerebral oxygen saturation (rSO2) for detecting early postoperative brain injury in cardiovascular surgery patients. A total of 55 patients undergoing cardiovascular surgery with cardiopulmonary bypass in Changzhou No.2 The People's Hospital of Nanjing Medical University were included in this study. Neuron-specific enolase (NSE) concentration was measured 24h after operation. Patients were divided into brain injury (NSE ≥ 16.3 ng/mL) and normal (0 < NSE < 16.3 ng/mL) groups according to the measured NSE concentration. The clinical outcomes between the two groups were compared, including decreased rSO2 and cerebral blood flow (as measured by TCCD) levels. The risk factors of early postoperative brain injury were analyzed by multivariate logistic regression analysis, and the significant variables were analyzed by receiver operating characteristic (ROC) analysis. A total of 50 patients were included in this study, with 20 patients in the brain injury group and 30 patients in the normal group. Cardiopulmonary bypass time (min) (107 ± 29 vs. 90 ± 28, P = 0.047) and aortic occlusion time (min) (111 (IQR 81-127) vs. 87 (IQR 72-116), P = 0.010) were significantly longer in the brain injury group than in the normal group. Patients in the brain injury group had greater decreased rSO2 (%) (27.0 ± 7.3 vs. 17.5 ± 6.1, P < 0.001) and cerebral blood flow (%) (44.9 (IQR 37.8-69.2) vs. 29.1 (IQR 12.0-48.2), P = 0.004) levels. Multivariate logistic regression analysis suggested that decreased rSO2 and cerebral blood flow levels, aortic occlusion time, and history of atrial fibrillation were independent risk factors for early postoperative brain injury (P < 0.05). ROC analysis reported that the best cutoff values for predicting early postoperative brain injury were 21.4% and 37.4% for decreased rSO2 and cerebral blood flow levels, respectively (P < 0.05). The decreased rSO2 and cerebral blood flow levels, aorta occlusion time, and history of atrial fibrillation were independent risk factors for early postoperative brain injury. TCCD and rSO2 could effectively monitor brain metabolism and cerebral blood flow and predict early postoperative brain injury.

A predictive model in patients with chronic hydrocephalus following aneurysmal subarachnoid hemorrhage: a retrospective cohort study

ObjectiveOur aim was to develop a nomogram that integrates clinical and radiological data obtained from computed tomography (CT) scans, enabling the prediction of chronic hydrocephalus in patients with aneurysmal subarachnoid hemorrhage (aSAH).MethodA total of 318 patients diagnosed with subarachnoid hemorrhage (SAH) and admitted to the Department of Neurosurgery at the Affiliated People’s Hospital of Jiangsu University between January 2020 and December 2022 were enrolled in our study. We collected clinical characteristics from the hospital’s medical record system. To identify risk factors associated with chronic hydrocephalus, we conducted both univariate and LASSO regression models on these clinical characteristics and radiological features, accompanied with penalty parameter adjustments conducted through tenfold cross-validation. All features were then incorporated into multivariate logistic regression analyses. Based on these findings, we developed a clinical-radiological nomogram. To evaluate its discrimination performance, we conducted Receiver Operating Characteristic (ROC) curve analysis and calculated the Area Under the Curve (AUC). Additionally, we employed calibration curves, and utilized Brier scores as an indicator of concordance. Additionally, Decision Curve Analysis (DCA) was performed to determine the clinical utility of our models by estimating net benefits at various threshold probabilities for both training and testing groups.ResultsThe study included 181 patients, with a determined chronic hydrocephalus prevalence of 17.7%. Univariate logistic regression analysis identified 11 potential risk factors, while LASSO regression identified 7 significant risk factors associated with chronic hydrocephalus. Multivariate logistic regression analysis revealed three independent predictors for chronic hydrocephalus following aSAH: Periventricular white matter changes, External lumbar drainage, and Modified Fisher Grade. A nomogram incorporating these factors accurately predicted the risk of chronic hydrocephalus in both the training and testing cohorts. The AUC values were calculated as 0.810 and 0.811 for each cohort respectively, indicating good discriminative ability of the nomogram model. Calibration curves along with Hosmer-Lemeshow tests demonstrated excellent agreement between predicted probabilities and observed outcomes in both cohorts. Furthermore, Brier scores (0.127 for the training and 0.09 for testing groups) further validated the predictive performance of our nomogram model. The DCA confirmed that this nomogram provides superior net benefit across various risk thresholds when predicting chronic hydrocephalus. The decision curve demonstrated that when an individual’s threshold probability ranged from 5 to 62%, this model is more effective in predicting the occurrence of chronic hydrocephalus after aSAH.ConclusionA clinical-radiological nomogram was developed to combine clinical characteristics and radiological features from CT scans, aiming to enhance the accuracy of predicting chronic hydrocephalus in patients with aSAH. This innovative nomogram shows promising potential in assisting clinicians to create personalized and optimal treatment plans by providing precise predictions of chronic hydrocephalus among aSAH patients.

Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model.

Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF). To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model. Thirty mice with carbon tetrachloride (CCl4)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000s/mm2) diffusion-weighted imaging (DWI) on a 3T scanner. DWI data were processed using RSI model (2-5 compartments) with the Bayesian Information Criterion (BIC) determining the optimal model. Conventional ADC value and signal fraction of each compartment in the optimal RSI model were compared across groups. Receiver operating characteristics (ROC) curve analysis was performed to determine the diagnosis performances of different parameters, while Spearman correlation analysis was employed to investigate the correlation between different tissue compartments and the stage of LF. According to BIC results, a 4-compartment RSI model (RSI4) with optimal ADCs of 0.471×10-3, 1.653×10-3, 9.487×10-3, and>30×10-3, was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C1 and C3 compartments were observed between LF and control groups (P=0.018 and 0.003, respectively). ROC analysis showed that RSI4-C3 was the most effective single diffusion parameter for characterizing LF (AUC=0.876, P=0.003). Furthermore, the combination of ADC values and RSI4-C3 value increased the diagnosis performance significantly (AUC=0.894, P=0.002). The 4-compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI4-C3 showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI4-C3 values further improved the discrimination performance.

Association of Glutathione Peroxidase 3 (GPx3) and miR-196a with Carbohydrate Metabolism Disorders in the Elderly.

The escalating prevalence of carbohydrate metabolism disorders (CMDs) prompts the need for early diagnosis and effective markers for their prediction. Hyperglycemia, the primary indicator of CMDs including prediabetes and type 2 diabetes mellitus (T2DM), leads to overproduction of reactive oxygen species (ROS) and oxidative stress (OxS). This condition, resulting from chronic hyperglycemia and insufficient antioxidant defense, causes damage to biomolecules, triggering diabetes complications. Additionally, aging itself can serve as a source of OxS due to the weakening of antioxidant defense mechanisms. Notably, previous research indicates that miR-196a, by downregulating glutathione peroxidase 3 (GPx3), contributes to insulin resistance (IR). Additionally, a GPx3 decrease is observed in overweight/obese and insulin-resistant individuals and in the elderly population. This study investigates plasma GPx3 levels and miR-196a expression as potential CMD risk indicators. We used ELISA to measure GPx3 and qRT-PCR for miR-196a expression, supplemented by multivariate linear regression and receiver operating characteristic (ROC) analysis. Our findings included a significant GPx3 reduction in the CMD patients (n = 126), especially in the T2DM patients (n = 51), and a decreasing trend in the prediabetes group (n = 37). miR-196a expression, although higher in the CMD and T2DM groups than in the controls, was not statistically significant, potentially due to the small sample size. In the individuals with CMD, GPx3 levels exhibited a negative correlation with the mass of adipose tissue, muscle, and total body water, while miR-196a positively correlated with fat mass. In the CMD group, the analysis revealed a weak negative correlation between glucose and GPx3 levels. ROC analysis indicated a 5.2-fold increased CMD risk with GPx3 below 419.501 ng/mL. Logistic regression suggested that each 100 ng/mL GPx3 increase corresponded to a roughly 20% lower CMD risk (OR = 0.998; 95% CI: 0.996-0.999; p = 0.031). These results support the potential of GPx3 as a biomarker for CMD, particularly in T2DM, and the lack of a significant decline in GPx3 levels in prediabetic individuals suggests that it may not serve reliably as an early indicator of CMDs, warranting further large-scale validation.