King-Devick Sensitivity and Specificity to Concussion in Collegiate Athletes.

Evidence suggests neurodevelopmental disorders (NDs) may be associated with an increased incidence of concussion, but no studies have cross-sectionally and longitudinally assessed the associations of NDs and sex with concussion in collegiate athletes. We sought to assess the odds and relative risk (RR) of concussion in athletes self-reporting a diagnosis of attention deficit/hyperactivity disorder (ADHD), learning disability (LD), and ADHD+LD. Data from the Concussion Assessment, Research and Education (CARE) Consortium (2014-2017) were used to evaluate the likelihood of concussion for male and female athletes with ADHD, LD, and ADHD+LD, relative to controls. Odds ratios (ORs) of concussion history prior to enrollment and relative risk ratios for incurring a concussion following enrollment, with and without concussion history were calculated for all groups. Athletes with self-reported diagnosis of ADHD, LD, and ADHD+LD were more likely to report a single concussion (OR range = 1.528 to 1.828) and multiple concussions (OR range = 1.849 to 2.365) prior to enrollment in the CARE Consortium, irrespective of sex compared with control athletes. While enrolled in CARE, male athletes with ADHD, LD, and ADHD+LD had greater risk of incurring a concussion (RR range = 1.369 to 2.243) than controls, irrespective of concussion history. Male athletes with ADHD+LD with concussion history (RR = 2.221) and without concussion history (RR = 1.835) had greater risk of incurring a concussion than controls. These results suggest NDs may be associated with increased odds of single and multiple concussions, irrespective of sex. However, when we accounted for concussion history, it appears only male athletes with ADHD+LD had greater risk than respective controls. There were no significant differences between females and males with ADHD, LD, or ADHD+LD for either odds of concussion history or risk for incurring concussion.

Usefulness of nutritional assessment using Geriatric Nutritional Risk Index as an independent predictor of 30-day mortality after hip fracture surgery

Microvascular invasion (MVI) is an important predictor of postoperative recurrence or poor outcomes of hepatocellular carcinoma (HCC). Radiomics is able to predict MVI in HCC preoperatively. This study aims to investigate the influence of different region of interest (ROI) sizes on CT-based radiomics model for MVI prediction in HCC. Patients with HCC with or without MVI confirmed by pathology and those who underwent preoperative plain or enhanced abdominal CT scans in the Third Xiangya Hospital of Central South University from January 2010 to December 2020 were retrospectively and consecutively included. According to the ratio of 7 to 3, the patients were randomly assigned into a training set and a validation set. Clinical data were collected from medical records, and radiomics features were extracted from the arterial phase (AP) and portal venous phase (PVP) of preoperatively acquired CT in all patients. Six different ROI sizes were employed. The original ROI (OROI) was manually delineated along the visible borders of the tumor layer-by-layer. The OROI was expanded out by 1-5 mm. The OROI was combined with 5 different peritumoral regions to generate the other 5 ROIs, named Plus1-Plus5. Feature extraction, dimension reduction, and model development were conducted in 6 different ROIs separately. Supporter vector machine (SVM) was used for model construction. Model performance was assessed via receiver operating characteristic (ROC) curve. A total of 172 HCC patients were included, in which 83 (48.3%) were MVI positive, and 89 (51.7%) were MVI negative. Three hundred and ninety-six features based on AP or PVP images were extracted from each ROI. After feature selection and dimension reduction, 4, 5, 15, 11, 6, and 3 features of OROI, Plus1, Plus2, Plus 3, Plus4, and Plus5 were selected for model construction, respectively. In the training set, the sensitivity, specificity, and area under the curve (AUC) of OROI were 0.759, 0.806, and 0.855, respectively. The AUC values of Plus2 (0.979) and Plus3 (0.954) were higher than that of OROI. The AUC values of Plus1 (0.802), Plus4 (0.792), and Plus5 (0.774) were not significantly different from those of OROI. In the validation set, the sensitivity, specificity, and AUC value of OROI were 0.640, 0.630, and 0.664, respectively. The AUC value of Plus3 was 0.903, which was higher than that of OROI. The AUC values of Plus1 (0.679), Plus2 (0.536), Plus4 (0.708), and Plus5 (0.757) were not significantly different from that of OROI (P>0.05). The size of ROI significantly inflluences on the performance of CT-based radiomics model for MVI prediction in HCC. Including appropriate area around the tumor into ROI could improve the predictive performance of the model, and 3 mm might be appropriate distance.

To investigate the relation between obesity-related indices and mild cognitive impairment (MCI) in elderly patients with type 2 diabetes (T2D). A total of 597 eligible elderly patients with T2D were included in this retrospective study. All patients were divided into MCI group and normal cognitive group based on neuropsychological assessment. Twelve obesity-related indices were calculated, including body mass index (BMI), waist-hip ratio (WHR), waist-to-height ratio (WHtR), lipid accumulation product (LAP), body roundness index (BRI), conicity index (CI), visceral adiposity index (VAI), body adiposity index (BAI), abdominal volume index (AVI), a body shape index (ABSI), triglyceride glucose (TyG) index and cardiometabolic index (CMI). Multivariate logistic regression analysis, tests for trend and restricted cubic splines were used to assess the relationships between the tests for trend and MCI in elderly patients with T2D. Receiver operating characteristic (ROC) curves and areas under the curves (AUC) were used to assess the performance and predictive ability of the obesity-related indices for identifying MCI in elderly patients with T2D. Multivariate logistic regression showed that elevated BMI, WHR, WHtR, LAP, BRI, CI, VAI, AVI, TyG index, and CMI were associated with an increased risk of MCI in elderly T2D patients after adjusting for potential confounders (all P<0.05). In addition, TyG index, LAP, CMI, VAI, AVI, WHR, WHtR, BRI, and CI had negative correlations with Montreal Cognitive Assessment (MoCA) scores (all P<0.05). There was a significant linear trend between the levels of BMI (P for trend = 0.004, P for non-linearity = 0.637), WHR (P for trend = 0.006, P for non-linearity = 0.430), WHtR (P for trend <0.001, P for non-linearity = 0.452), BRI (P for trend <0.001, P for non-linearity = 0.252), AVI ( P for trend <0.001, P for non-linearity = 0.944), and TyG index (P for trend <0.001, P for non-linearity = 0.514) and risk of MCI in elderly patients with T2D after adjusting for potential confounders. There was a nonlinear association between LAP, VAI or CMI and risk of MCI in elderly patients with T2D (all P for non-linearity < 0.001). CMI had the greatest AUC (AUC = 0.682), followed by VAI (AUC = 0.679), TyG index (AUC = 0.673), LAP (AUC = 0.669), AVI (AUC = 0.580), WHtR and BRI (AUC = 0.575), BMI (AUC = 0.560), CI (AUC = 0.556), WHR (AUC = 0.554), BAI (AUC = 0.547), and ABSI (AUC = 0.536). Elevated obesity-related indices, particularly CMI, VAI, TyG index and LAP, which displayed the higher predictive power, were instrumental in forecasting and evaluating MCI in elderly T2D patients. These findings may provide clues for future studies exploring early diagnostic biomarkers and treatment of MCI in elderly T2D patients.

Machine learning-based receiver operating characteristic (ROC) curves for crisp and fuzzy classification of DNA microarrays in cancer research

Clinical prediction rules could help identify children at risk of slowed growth after an episode of diarrheal illness, and these rules may be generalizable to all children, regardless of diarrhea status.

PURPOSE: Muscular and cardiorespiratory fitness are associated with adiposity and cardiovascular disease risk in youth. The degree to which these measures discriminate obesity is not lucid. This study investigated muscular and cardiorespiratory fitness capacities to discriminate obesity. METHODS: Participants (N = 210; 116 males) (9.7 ± 1.08 years; 138.6 ± 9.4 cm; 42.3 ± 14.4 kg) (84% Hispanic/Latino) muscular and cardiorespiratory fitness were assessed using the 20-meter Progressive Aerobic Cardiovascular Endurance Run (PACER) and 90o push-up test, respectively, per FITNESSGRAM® protocols. Weight status was classified using CDC Growth Charts. Non-obesity was coded as “0” and obesity was coded as “1.” A Receiver Operating Characteristics (ROC) curve analysis was performed to identify the True Positive Rate (TPR) (i.e., Sensitivity) and False Positive Rate (FPR) (i.e., 1 - Specificity) using SPSS. The area under the curve (AUC) indicated the degree to which each fitness measure distinguishes between the presence and absence of obesity. The measurement threshold with the greatest TPR-FPR distance was considered optimally discriminative. Statistical significance was set at P < .05. RESULTS: Of the sample, 43% had obesity and 55% did not. PACER was fairly discriminative of obesity in girls (AUC = .748; P = .000; 95% CI [.651, .845]) and boys (AUC = .755; P = .000; 95% CI [.665, .845]). 90o push-up was fairly discriminative in girls (AUC = .732; P = .000; 95% CI [.632, .831]) and boys (AUC = .789; P = .000; 95% CI [.703, .876]). PACER thresholds of 8 and 8 laps were optimally discriminative in girls (TPR = .714, FPRs = .352) and boys (TPR = .635, FPRs = .190), respectively. 90o push-up thresholds of 7 and 5 repetitions were optimally discriminative in girls (TPR = .714, FPRs = .333) and boys (TPR = .692, FPRs = .175), respectively. CONCLUSIONS: Based on the probability of classifying individuals with and without obesity as having obesity (i.e., TPRs and FPRs, respectively), PACER and 90o push-up appear similarly discriminative of obesity. Notably, the optimal PACER threshold to discriminate obesity was considerably lower than cut-off scores (23 for boys and 15 for girls) for HFZ classification in the same age group. Cardiorespiratory-related disease risk may persist in subpopulations of Hispanic/Latino youth without obesity.

Anthropometric indices are affordable and non-invasive methods for screening metabolic syndrome (MetS). However, determining the most effective index for screening can be challenging. To investigate the accuracy of anthropometric indices as a screening tool for predicting MetS among apparently healthy individuals in Karachi, Pakistan. A community-based cross-sectional survey was conducted in Karachi, Pakistan, from February 2022 to August 2022. A total of 1,065 apparently healthy individuals aged 25 years and above were included. MetS was diagnosed using International Diabetes Federation guidelines. Anthropometric indices were defined based on body mass index (BMI), neck circumference (NC), mid-upper arm circumference (MUAC), waist circumference (WC), waist to height ratio (WHtR), conicity index, reciprocal ponderal index (RPI), body shape index (BSI), and visceral adiposity index (VAI). The analysis involved the utilization of Pearson's correlation test and independent t-test to examine inferential statistics. The receiver operating characteristic (ROC) analysis was also applied to evaluate the predictive capacities of various anthropometric indices regarding metabolic risk factors. Moreover, the area under the curve (AUC) was computed, and the chosen anthropometric indices' optimal cutoff values were determined. All anthropometric indices, except for RPI in males and BSI in females, were significantly higher in MetS than those without MetS. VAI [AUC 0.820 (95% CI 0.78-0.86)], WC [AUC 0.751 (95% CI 0.72-0.79)], WHtR [AUC 0.732 (95% CI 0.69-0.77)], and BMI [AUC 0.708 (95% CI 0.66-0.75)] had significantly higher AUC for predicting MetS in males, whereas VAI [AUC 0.693 (95% CI 0.64-0.75)], WHtR [AUC 0.649 (95% CI 0.59-0.70)], WC [AUC 0.646 (95% CI 0.59-0.61)], BMI [AUC 0.641 (95% CI 0.59-0.69)], and MUAC [AUC 0.626 (95% CI 0.57-0.68)] had significantly higher AUC for predicting MetS in females. The AUC of NC for males was 0.656 (95% CI 0.61-0.70), while that for females was 0.580 (95% CI 0.52-0.64). The optimal cutoff points for all anthropometric indices exhibited a high degree of sensitivity and specificity in predicting the onset of MetS. BMI, WC, WHtR, and VAI were the most important anthropometric predictors for MetS in apparently healthy individuals of Pakistan, while BSI was found to be the weakest indicator.

Research Type: Level 3 - Retrospective cohort study, Case-control study, Meta-analysis of Level 3 studies Introduction/Purpose: The recently proposed Progressive Collapsing Foot Deformity (PCFD) classification system categorizes different deformity patterns into five classes (A, Hindfoot Valgus; B, Midfoot/Forefoot Abduction; C, Arch Collapse; D, Peritalar Subluxation; E, Ankle Valgus Instability). Currently, the decision-making process to determine the presence or absence of each deformity class relies on the surgeon's experience and interpretation of clinical and radiographic assessments, without established cut-off threshold values in the literature for PCFD measurements. This limits the reliability and reproducibility of the classification system. The goal of the current study was to establish cut-off threshold values for the most commonly used PCFD measurements assessing Class A to D deformities, using a large cohort of PCFD patients and controls. Methods: Two patient cohorts were included: a prospective cohort consisting of normally aligned and asymptomatic control volunteers (n=197 patients, 103 females/94 males, average age 41.7 years, average BMI 28.9) and a retrospective cohort of PCFD patients (n=321 patients, 136 females/185 males, average age 50.7 years, average BMI 29.8). All patients underwent Weight-Bearing Computed Tomography (WBCT) scans of the foot and ankle. Foot bone segmentation was performed semi-automatically using specialized software, followed by automatic measurements of various parameters including: Class A (Hindoot Moment Arm – HMA), Class B (Talonavicular Coverage Angle – TCA; Talus-First Metatarsal Angle Axial – TFMA-A), Class C (Talus-First Metatarsal Angle Sagittal – TFMA-S; Forefoot Arch Angle - FAA), Class D (Coverage Maps of the Subtalar Joint Middle-MF, Posterior Facets-PF; and Sinus Tarsi-ST). Receiver Operating Characteristic (ROC) curves, Youden's Indexes and Areas Under the Curve (AUC) were calculated for each measurement. P-values of 0.05 or less were considered significant. Results: The Cut-Off Threshold Values identified by the ROC curves using Youden’s Index for different PCFD measurements were as follows (all p-values &lt; 0.0001): HMA of 13.9mm or more (AUC 85.3%) was considered diagnostic for Class A PCFD deformity. For Class B PCFD deformity, a TNCA of 38.7o or higher (AUC 84.4%) and TFMA-A of 20.3o or higher (AUC 82.1%) were considered diagnostic. For Class C PCFD deformity, a FAA of 8.7o or lower (AUC 83.9%) and TFMA-S of 18.7o or higher (AUC 82.9%) were diagnostic. For Class D Deformity, Coverage values for Middle Facet of 73.5% or lower (AUC 73.4%), Posterior Facet of 84.5% or lower (AUC 82.7%), and Sinus Tarsi of 25.7% or higher (AUC 84.4%) were diagnostic. Conclusion: In this diagnostic study involving 312 PCFD patients and 197 volunteer controls, we successfully determined the cut-off threshold values for key PCFD measurements related to Hindfoot Valgus (Class A), Midfoot/Forefoot Abduction (Class B), Arch Collapse (Class C), and Peritalar Subluxation (Class D) deformities. By utilizing a large cohort of patients and controls, we achieved high diagnostic accuracy for most measurements assessed (AUC of 80% or more) in identifying the presence or absence of these PCFD deformity classes. Our findings can potentially serve as normative threshold values to enhance the utilization, reliability, and reproducibility of the PCFD classification system. Figure 1 Scatter plots and ROC (Receiver Operating Characteristic) curves used to analyze parameters for the various deformity classifications in Progressive Collapsing Foot Deformity (PCFD). For the scatter plots, red indicates a normal control, green indicates an asymptomatic PCFD, and blue indicates a symptomatic PCFD.

The Adult Deformity Surgery Complexity Index (ADSCI): a valid tool to quantify the complexity of posterior adult spinal deformity surgery and predict postoperative complications

The Athlete Psychological Strain Questionnaire (APSQ) is a 10-item mental health screen in the International Olympic Committee Sport Mental Health Assessment Tool 1. This study evaluated its accuracy compared to six validated mental health screening tools in collegiate athletes. Division I collegiate athletes (n=2758) completed the APSQ alongside validated screening tools for anxiety (Generalised Anxiety Disorder-7 (GAD-7)), depression (Patient Health Questionnaire-9 (PHQ-9)), sleep disturbance (Athlete Sleep Screening Questionnaire-Sleep Difficulty Score (ASSQ-SDS)), alcohol use (Alcohol Use Disorders Identification Test-Consumption (AUDIT-C)), substance use (Cutting down, Annoyance by Criticism, Guilty feelings and Eye openers Adapted to Include Drugs (CAGE-AID) and disordered eating (Eating Disorder Examination-Questionnaire Short (EDE-QS)) between June 2020 and June 2023. The APSQ's performance was assessed using sensitivity, specificity and area under the curve (AUC). Of 2758 completed APSQ, 720 (26.1%) screened positive. The rates of positive screens for other measures were GAD-7=7.7%, PHQ-9=11.6% (3.1% reported suicidal ideation on item 9), ASSQ-SDS=21.0%, AUDIT-C=14.5%, CAGE-AID=2.9% and EDE-QS=4.8%. Compared with the APSQ, we observed high accuracy for anxiety (GAD-7: AUC=0.92, sensitivity=90%, specificity=83%) and depression (PHQ-9: AUC=0.91, sensitivity=82%, specificity=84%). There was moderate accuracy for disordered eating (EDE-QS: AUC=0.85, sensitivity=82%, specificity=81%), while accuracy was lower for sleep disturbance (ASSQ-SDS: AUC=0.72, sensitivity=52%, specificity=84%), substance use (CAGE-AID: AUC=0.69, sensitivity=61%, specificity=79%) and alcohol use (AUDIT-C: AUC=0.64, sensitivity=46%, specificity=81%). False-negative rates ranged from 9.9% (GAD-7) to 55% (AUDIT-C). The APSQ accurately identifies athletes at risk for anxiety and depression but is less accurate for detecting other mental health issues. Adding more specific questions on sleep disturbance and alcohol use to the APSQ could further improve its accuracy.

BACKGROUND: There is no consensus on the predictors of arrhythmia recurrence following treatment for typical atrial flutter. AIM: The study aimed to identify the factors associated with arrhythmia recurrence in patients with coronary artery disease and typical atrial flutter after cardioversion. METHODS: The study included 165 patients who underwent different treatment modalities, including pharmacological cardioversion with amiodarone (61 patients), electrical cardioversion (20 patients), transesophageal atrial pacing (48 patients), and radiofrequency ablation (36 patients). Patients underwent a 5-year follow-up at intervals of 6, 12, 24, 36, 48, and 60 months. The predictors of arrhythmia recurrence were determined by analyzing correlation coefficients using Pearson’s (r) and Spearman’s (R) tests, depending on data distribution, followed by multiple regression analysis and receiver operating characteristic (ROC) analysis, including area under the curve (AUC) calculation. The statistical analysis assessed the influence of sex, age, height, weight, body mass index, body surface area, smoking status, arrhythmia characteristics, and comorbidities (atrial flutter type, arrhythmia duration, time since last recurrence, history of atrial fibrillation, symptom severity using the EHRA scale, CHA₂DS₂-VASc and HAS-BLED scores, presence of silent myocardial ischemia and ventricular arrhythmias based on 24-hour Holter ECG monitoring, lipid profile, functional class of angina and heart failure, prior percutaneous coronary intervention with stenting, and history of coronary artery bypass grafting). Structural and functional parameters of the atria and left ventricle were assessed using transthoracic echocardiography. Statistical significance was set at p 0.05. RESULTS: The success rates in restoring sinus rhythm were as follows: 54.1% with pharmacological cardioversion, 87.5% with transesophageal atrial pacing combined with amiodarone, 95.0% with electrical cardioversion, and 100% with radiofrequency ablation with cryoisolation of the pulmonary vein ostia. Arrhythmia recurrence was identified in 62.42% of patients. Significant predictors of atrial flutter recurrence included obesity grade ≥2 (AUC = 0.655; p = 0.0117), regular-type atrial flutter (AUC = 0.736; p 0.0001), and a history of coronary angioplasty with stenting (AUC = 0.687; p 0.0001). Following conservative cardioversion, a history of atrial fibrillation was the primary predictor of atrial fibrillation recurrence. After ablation, recurrence was predicted by age 62 years (AUC = 0.703; p = 0.0211) and left ventricular diastolic diameter 5.3 cm (AUC = 0.703; p = 0.0305). Predictors of atrial flutter-to-fibrillation recurrence included a history of atrial fibrillation (AUC = 0.702; p = 0.0193), left atrial dilation (AUC = 0.714; p = 0.0439), and left ventricular hypertrophy (AUC = 0.703; p = 0.0121). CONCLUSION: The five-year recurrence of atrial flutter is associated with the severity of underlying cardiovascular pathology, whereas atrial fibrillation recurrence is linked to a history of atrial fibrillation, patient age, and structural remodeling of the left heart chambers.

Freezing of gait (FOG) is one of the most debilitating motor symptoms experienced by patients with Parkinson's disease (PD). FOG detection is possible using acceleration data from wearable sensors, and a convolutional neural network (CNN) is often used to determine the presence of FOG epochs. We compared the performance of a standard CNN for the detection of FOG with two more complex networks, which are well suited for time series data, the MiniRocket and the InceptionTime. We combined acceleration data of people with PD across four studies. The final data set was split into a training (80%) and hold-out test (20%) set. A fifth study was included as an unseen test set. The data were windowed (2 s) and five-fold cross-validation was applied. The CNN, MiniRocket, and InceptionTime models were evaluated using a receiver operating characteristic (ROC) curve and its area under the curve (AUC). Multiple sensor configurations were evaluated for the best model. The geometric mean was subsequently calculated to select the optimal threshold. The selected model and threshold were evaluated on the hold-out and unseen test set. A total of 70 participants (23.7 h, 9% FOG) were included in this study for training and testing, and in addition, 10 participants provided an unseen test set (2.4 h, 11% FOG). The CNN performed best (AUC = 0.86) in comparison to the InceptionTime (AUC = 0.82) and MiniRocket (AUC = 0.76) models. For the CNN, we found a similar performance for a seven-sensor configuration (lumbar, upper and lower legs and feet; AUC = 0.86), six-sensor configuration (upper and lower legs and feet; AUC = 0.87), and two-sensor configuration (lower legs; AUC = 0.86). The optimal threshold of 0.45 resulted in a sensitivity of 77% and a specificity of 58% for the hold-out set (AUC = 0.72), and a sensitivity of 85% and a specificity of 68% for the unseen test set (AUC = 0.90). We confirmed that deep learning can be used to detect FOG in a large, heterogeneous dataset. The CNN model outperformed more complex networks. This model could be employed in future personalized interventions, with the ultimate goal of using automated FOG detection to trigger real-time cues to alleviate FOG in daily life.

The area under the curve (AUC) is commonly used as a summary measure of the receiver operating characteristic (ROC) curve. It indicates the overall performance of a diagnostic test in terms of its accuracy at various diagnostic thresholds used to discriminate cases and non-cases of disease. The AUC measure is also used in meta-analyses, where each component study provides an estimate of the test sensitivity and specificity. These estimates are then combined to calculate a summary ROC (SROC) curve which describes the relationship between-test sensitivity and specificity across studies. The partial AUC has been proposed as an alternative measure to the full AUC. When using the partial AUC, one considers only those regions of the ROC space where data have been observed, or which correspond to clinically relevant values of test sensitivity or specificity. In this paper, we extend the idea of using the partial AUC to SROC curves in meta-analysis. Theoretical and numerical results describe the variation in the partial AUC and its standard error as a function of the degree of inter-study heterogeneity and of the extent of truncation applied to the ROC space. A scaled partial area measure is also proposed to restore the property that the summary measure should range from 0 to 1. The results suggest several disadvantages of the partial AUC measures. In contrast to earlier findings with the full AUC, the partial AUC is rather sensitive to heterogeneity. Comparisons between tests are more difficult, especially if an empirical truncation process is used. Finally, the partial area lacks a useful symmetry property enjoyed by the full AUC. Although the partial AUC may sometimes have clinical appeal, on balance the use of the full AUC is preferred.

Given the limitations of conventional imaging in accurately grading gliomas, predicting molecular subtypes, and assessing tumor proliferation and angiogenesis, there is a growing need for advanced quantitative magnetic resonance imaging (MRI) biomarkers. This study aimed to compare the diagnostic performance of histogram features of dynamic contrast enhanced (DCE) and dynamic susceptibility contrast (DSC) imaging in predicting glioma grade and genotyping, as well as to explore the association between DCE and DSC with Ki-67 and microvascular density (MVD). Forty-six patients with gliomas were enrolled prospectively. The histogram features of DCE and DSC were extracted from the entire tumor and compared among subgroups based on the grades and status of isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion. Pearson correlation analyses were employed to examine the associations among Ki-67, MVD, and histogram features of perfusion model. Receiver operating characteristic (ROC) analyses were used to evaluate the accuracy of DCE and DSC in differentiating glioma grades and genotypes, while Cox regression analyses identified prognostic factors associated with survival after surgical resection. A cohort of 46 patients with IDH-mutant (n=23) and IDH-wildtype (n=23) gliomas was analyzed. DCE-MRI histogram features demonstrated superior performance to DSC parameters in both IDH genotyping and tumor grading. For IDH discrimination, 90th percentile of volume fraction of the extravascular-extracellular space (Ve) [area under the curve (AUC) =0.849, sensitivity 91.3%, specificity 78.3%] and mean value of volume transfer constant (Ktrans) (AUC =0.847, sensitivity 87.0%, specificity 82.6%) showed optimal performance (both P<0.001). In contrast, DSC-derived relative cerebral blood volume (rCBV) (AUC =0.577) and relative cerebral blood flow (rCBF) (AUC =0.537) exhibited limited diagnostic value (P>0.05). In tumor grading, 90th percentile of Ve (AUC =0.923) and mean value of Ktrans (AUC =0.908) achieved near-perfect differentiation (sensitivity 83.9%, specificity 93.3%; P<0.001). While DSC-derived parameters showed improved performance compared to genotyping (rCBV: AUC =0.742, P=0.008; rCBF: AUC =0.688, P=0.040), DCE-based models remained significantly superior (DeLong's test P<0.05 for all comparisons). Significant correlations were observed between DCE parameters and both Ki-67 proliferation index (r=0.42-0.51) and MVD (r=0.35-0.56; all P<0.05). Survival analysis identified three independent prognostic factors: O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation [hazard ratio (HR) =4.15, 95% confidence interval (CI): 1.10-15.68, P=0.04], elevated Ktrans (HR =1.08, 95% CI: 1.01-1.17, P=0.03), and rCBV (HR =1.87, 95% CI: 1.17-2.97, P=0.009). The histogram features of DCE and DSC demonstrate exceptional diagnostic efficacy in glioma grading, with DCE providing deeper insights into the molecular characteristics of glioma.