Area Under The ROC Curve Values Research Articles

Impact Exploration of Spatiotemporal Feature Derivation and Selection on Machine Learning-Based Predictive Models for Post-Embolization Cerebral Aneurysm Recanalization.

To enhance the performance of machine learning (ML) models for the post-embolization recanalization of cerebral aneurysms, we evaluated the impact of hemodynamic feature derivation and selection method on six ML algorithms. We utilized computational fluid dynamics (CFD) to simulate hemodynamics in 66 cerebral aneurysms from 65 patients, including 57 stable and nine recanalized aneurysms. We derived a total of 107 features for each aneurysm, encompassing four clinical features, 12 morphological features, and 91 hemodynamic features. To investigate the influence of feature derivation and selection methods on the ML models, we employed two derivation methods, simplified and fully derived, in combination with four selection methods: all features, statistically significant analysis, stepwise multivariate logistic regression analysis (stepwise-LR), and recursive feature elimination (RFE). Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC) on both the training and testing datasets. The AUROC values on the testing dataset exhibited a wide-ranging spectrum, spanning from 0.373 to 0.863. Fully derived features and the RFE selection method demonstrated superior performance in intra-model comparisons. The multi-layer perceptron (MLP) model, trained with RFE-selected fully derived features, achieved the best performance on the testing dataset, with an AUROC value of 0.863 (95% CI: 0.684- 1.000). Our study demonstrated the importance of feature derivation and selection in determining the performance of ML models. This enabled the development of accurate decision-making models without the need to invade the patient.

Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion

BackgroundThe frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure’s expanding use. Our study aims to accomplish two goals: firstly, to develop a suite of explainable machine learning (ML) models capable of predicting adverse postoperative outcomes following ACDF surgery, and secondly, to embed these models in a user-friendly web application, demonstrating their potential utility.MethodsWe utilized data from the National Surgical Quality Improvement Program database to identify patients who underwent ACDF surgery. The outcomes of interest were four short-term postoperative adverse outcomes: prolonged length of stay (LOS), non-home discharges, 30-day readmissions, and major complications. We utilized five ML algorithms - TabPFN, TabNET, XGBoost, LightGBM, and Random Forest - coupled with the Optuna optimization library for hyperparameter tuning. To bolster the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) for evaluating predictor variables’ relative importance and used partial dependence plots to illustrate the impact of individual variables on the predictions generated by our top-performing models. We visualized model performance using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Quantitative metrics calculated were the area under the ROC curve (AUROC), balanced accuracy, weighted area under the PRC (AUPRC), weighted precision, and weighted recall. Models with the highest AUROC values were selected for inclusion in a web application.ResultsThe analysis included 57,760 patients for prolonged LOS [11.1% with prolonged LOS], 57,780 for non-home discharges [3.3% non-home discharges], 57,790 for 30-day readmissions [2.9% readmitted], and 57,800 for major complications [1.4% with major complications]. The top-performing models, which were the ones built with the Random Forest algorithm, yielded mean AUROCs of 0.776, 0.846, 0.775, and 0.747 for predicting prolonged LOS, non-home discharges, readmissions, and complications, respectively.ConclusionsOur study employs advanced ML methodologies to enhance the prediction of adverse postoperative outcomes following ACDF. We designed an accessible web application to integrate these models into clinical practice. Our findings affirm that ML tools serve as vital supplements in risk stratification, facilitating the prediction of diverse outcomes and enhancing patient counseling for ACDF.

A CT based radiomics analysis to predict the CN0 status of thyroid papillary carcinoma: a two- center study

ObjectivesTo develop and validate radiomics model based on computed tomography (CT) for preoperative prediction of CN0 status in patients with papillary thyroid carcinoma (PTC).MethodsA total of 548 pathologically confirmed LNs (243 non-metastatic and 305 metastatic) two distinct hospitals were retrospectively assessed. A total of 396 radiomics features were extracted from arterial-phase CT images, where the strongest features containing the most predictive potential were further selected using the least absolute shrinkage and selection operator (LASSO) regression method. Delong test was used to compare the AUC values of training set, test sets and cN0 group.ResultsThe Rad-score showed good discriminating performance with Area Under the ROC Curve (AUC) of 0.917(95% CI, 0.884 to 0.950), 0.892 (95% CI, 0.833 to 0.950) and 0.921 (95% CI, 868 to 0.973) in the training, internal validation cohort and external validation cohort, respectively. The test group of CN0 with a AUC of 0.892 (95% CI, 0.805 to 0.979). The accuracy was 85.4% (sensitivity = 81.3%; specificity = 88.9%) in the training cohort, 82.9% (sensitivity = 79.0%; specificity = 88.7%) in the internal validation cohort, 85.4% (sensitivity = 89.7%; specificity = 83.8%) in the external validation cohort, 86.7% (sensitivity = 83.8%; specificity = 91.3%) in the CN0 test group.The calibration curve demonstrated a significant Rad-score (P-value in H-L test > 0.05). The decision curve analysis indicated that the rad-score was clinically useful.ConclusionsRadiomics has shown great diagnostic potential to preoperatively predict the status of cN0 in PTC.

Use of MRI Radiomics Models in Evaluating the Low HER2 Expression in Breast Cancer.

To investigate the magnetic resonance imaging (MRI) radiomics models in evaluating the human epidermal growth factor receptor 2(HER2) expression in breast cancer. <P> Materials and Methods: The MRI data of 161 patients with invasive ductal carcinoma (non-special type) of breast cancer were retrospectively collected, and the MRI radiomics models were established based on the MRI imaging features of the fat suppression T2 weighted image (T2WI) sequence, dynamic contrast-enhanced (DCE)-T1WIsequence and joint sequences. The T-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used for feature dimensionality reduction and screening, respectively, and the random forest (RF) algorithm was used to construct the classification model. <P> Results: The model established by the LASSO-RF algorithm was used in the ROC curve analysis. In predicting the low expression state of HER2 in breast cancer, the radiomics models of the fat suppression T2WI sequence, DCE-T1WI sequence, and the combination of the two sequences showed better predictive efficiency. In the receiver operating characteristic (ROC) curve analysis for the verification set of low, negative, and positive HER2 expression, the area under the ROC curve (AUC) value was 0.81, 0.72, and 0.62 for the DCE-T1WI sequence model, 0.79, 0.65 and 0.77 for the T2WI sequence model, and 0.84, 0.73 and 0.66 for the joint sequence model, respectively. The joint sequence model had the highest AUC value. <P> Conclusions: The MRI radiomics models can be used to effectively predict the HER2 expression in breast cancer and provide a non-invasive and early assistant method for clinicians to formulate individualized and accurate treatment plans.

Validating the Baveno Elastography Criteria of Advanced Liver Fibrosis in Two-dimensional Shear Wave Elastography: A Prospective Pathology-based Study.

The Baveno criteria for assessing advanced liver fibrosis were mainly determined by transient elastography (TE), and its pathology-based validation studies in two-dimensional shear-wave elastography (2D-SWE) remain limited. We aimed to validate the Baveno criteria through use of 2D-SWE. Consecutive patients who underwent liver biopsies for various benign liver diseases were prospectively recruited. Liver stiffness measurement (LSM) was simultaneously evaluated by TE and 2D-SWE. The optimal cut-off value to predict advanced liver fibrosis was determined by the Youden Index, and the diagnostic performance was estimated using area under the receiver operating characteristic (AUROC) analysis. A total of 101 patients were enrolled having a median age of 55.0 (IQR: 46.0-63.5) years, with 53 (52.48%) of them being male. Using &lt;9 and &gt;14 kPa as the optimal dual cut-offs, the AUROC values in TE and 2D-SWE were 0.92 (95% CI: 0.83-0.97) and 0.93 (95% CI: 0.84-0.98), respectively (P= 0.61). The sensitivity and specificity of LSM by TE/2D-SWE achieved rates of 94.44%/94.44% and 86.00%/88.00%, respectively. However, using the Baveno criteria, the AUROC values in TE and 2D-SWE could remain achieving 0.91 (95% CI: 0.82-0.97) and 0.93 (95% CI: 0.84-0.98), respectively (P= 0.36). The sensitivity and specificity in TE/2D-SWE were 88.24%/88.24% and 86.79%/90.57%, respectively. This study establishes the compatibility of the Baveno dual cut-off criteria with 2D-SWE, positioning it as an easily used criteria in clinical practice and research.

Predictive value of PD-L1 and TMB for short-term efficacy prognosis in non-small cell lung cancer and construction of prediction models.

To investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated. A total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the "rms" package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model. The mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model. PD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

PREDICTING LENGTH OF STAY OF JOINT REPLACEMENT PATIENTS AT ASHFORD AND ST. PETER'S HOSPITAL USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Arthroplasties are widely performed to improve mobility and quality of life for symptomatic knee/hip osteoarthritis patients. With increasing rates of Total Joint Replacements in the United Kingdom, predicting length of stay is vital for hospitals to control costs, manage resources, and prevent postoperative complications. A longer Length of stay has been shown to negatively affect the quality of care, outcomes and patient satisfaction. Thus, predicting LOS enables us to make full use of medical resources.Clinical characteristics were retrospectively collected from 1,303 patients who received TKA and THR. A total of 21 variables were included, to develop predictive models for LOS by multiple machine learning (ML) algorithms, including Random Forest Classifier (RFC), K-Nearest Neighbour (KNN), Extreme Gradient Boost (XgBoost), and Na¯ve Bayes (NB). These models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance. A feature selection approach was used to identify optimal predictive factors. Based on the ROC of Training result, XgBoost algorithm was selected to be applied to the Test set.The areas under the ROC curve (AUCs) of the 4 models ranged from 0.730 to 0.966, where higher AUC values generally indicate better predictive performance. All the ML-based models performed better than conventional statistical methods in ROC curves. The XgBoost algorithm with 21 variables was identified as the best predictive model. The feature selection indicated the top six predictors: Age, Operation Duration, Primary Procedure, BMI, creatinine and Month of Surgery.By analysing clinical characteristics, it is feasible to develop ML-based models for the preoperative prediction of LOS for patients who received TKA and THR, and the XgBoost algorithm performed the best, in terms of accuracy of predictive performance. As this model was originally crafted at Ashford and St. Peters Hospital, we have naturally named it as THE ASHFORD OUTCOME.

Assessing the Prognostic Significance of Lactate Levels and Arterial Base Excess in Patients After Pancreatic Surgery: A Retrospective Analysis

Assessing the Prognostic Significance of Lactate Levels and Arterial Base Excess in Patients After Pancreatic Surgery: A Retrospective Analysis

Optimization of CT pulmonary angiography for pulmonary embolism using task-based image quality assessment and diagnostic reference levels: A multicentric study

PurposeTo establish size-specific diagnostic reference levels (DRLs) for pulmonary embolism (PE) based on patient CT examinations performed on 74 CT devices. To assess task-based image quality (IQ) for each device and to investigate the variability of dose and IQ across different CTs. To propose a dose/IQ optimization. Methods1051 CT pulmonary angiography dose data were collected. DRLs were calculated as the 75th percentile of CT dose index (CTDI) for two patient categories based on the thoracic perimeters. IQ was assessed with two thoracic phantom sizes using local acquisition parameters and three other dose levels. The area under the ROC curve (AUC) of a 2 mm low perfused vessel was assessed with a non-prewhitening with eye-filter model observer. The optimal IQ-dose point was mathematically assessed from the relationship between IQ and dose. ResultsThe DRLs of CTDIvol were 6.4 mGy and 10 mGy for the two patient categories. 75th percentiles of phantom CTDIvol were 6.3 mGy and 10 mGy for the two phantom sizes with inter-quartile AUC values of 0.047 and 0.066, respectively. After the optimization, 75th percentiles of phantom CTDIvol decreased to 5.9 mGy and 7.55 mGy and the interquartile AUC values were reduced to 0.025 and 0.057 for the two phantom sizes. ConclusionDRLs for PE were proposed as a function of patient thoracic perimeters. This study highlights the variability in terms of dose and IQ. An optimization process can be started individually and lead to a harmonization of practice throughout multiple CT sites.

Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.

Artificial intelligence (AI) models in radiology are frequently developed and validated using datasets from a single institution and are rarely tested on independent, external datasets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multi-center AI competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. In total, 1201 anonymized, full-head NCCT clinical scans from five institutions were pooled to form the dataset. The dataset encompassed normal studies as well as pathologies including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these-task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient's age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed each model's performance. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each AI model, along with the area under the ROC curve (AUROC). 1177 studies were processed by four teams. The median age of patients was 62, with an interquartile range of 33. 19 teams from various academic institutions registered for the competition. Of these, four teams submitted their final results. No commercial entities participated in the competition. For task 1, AUROCs ranged from 0.49 to 0.59. For task 2, two teams completed the task with AUROC values of 0.57 and 0.52. For task 3, teams had little to no agreement with the ground truth. To assess the performance of AI models in real-world clinical scenarios, we analyzed their performance in the ASFNR AI Competition. The first ASFNR Competition underscored the gap between expectation and reality; the models largely fell short in their assessments. As the integration of AI tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.ABBREVIATIONS: AI = artificial intelligence; ASFNR = American Society of Functional Neuroradiology; AUROC = area under the receiver operating characteristic curve; DICOM = Digital Imaging and Communications in Medicine; GEE = generalized estimation equation; IQR = interquartile range; NPV = negative predictive value; PPV = positive predictive value; ROC = receiver operating characteristic; TBI = traumatic brain injury.

Ovarian Weight and Uterine Volume Index Are Useful for Age Estimation in Adult Women

Practically, when only parts of an unidentified human body are found, age estimation with limited materials is required. The purpose of this study was to investigate methods for estimating age using the uterus and ovaries. Among forensic autopsies performed between January 2011 and March 2022, 211 uteruses and 521 ovaries of 322 women were used for this study. Measured values for ovarian weight and uterine volume index were corrected by body surface area to consider the effect of body size. The corrected uterine volume index increased in teenage years and achieved its maximum in the 40–49 group, then gradually decreased with increasing age. The corrected ovarian weight increased until the twenties, after which it decreased with age. For women aged 20 years or more, receiver operating characteristic (ROC) curve analysis suggested that a uterine volume index of 41.2 cm3/m2 was the cutoff value for classifying the age as ≥60 years or &lt;60 years, with an area under the ROC curve (AUC) value of 0.751. Ovarian weights of 2.27 g/m2 and 1.92 g/m2 were the cutoff values for classifying the age as ≥40 years or &lt;40 years, or ≥50 years or &lt;50 years, with AUC values of 0.935 and 0.930, respectively. These methods can help determine an unknown individual’s age group simply and quickly, even for incomplete cadavers.

Medical image foundation models in assisting diagnosis of brain tumors: a pilot study.

To build self-supervised foundation models for multicontrast MRI of the whole brain and evaluate their efficacy in assisting diagnosis of brain tumors. In this retrospective study, foundation models were developed using 57,621 enhanced head MRI scans through self-supervised learning with a pretext task of cross-contrast context restoration with two different content dropout schemes. Downstream classifiers were constructed based on the pretrained foundation models and fine-tuned for brain tumor detection, discrimination, and molecular status prediction. Metrics including accuracy, sensitivity, specificity, and area under the ROC curve (AUC) were used to evaluate the performance. Convolutional neural networks trained exclusively on downstream task data were employed for comparative analysis. The pretrained foundation models demonstrated their ability to extract effective representations from multicontrast whole-brain volumes. The best classifiers, endowed with pretrained weights, showed remarkable performance with accuracies of 94.9, 92.3, and 80.4%, and corresponding AUC values of 0.981, 0.972, and 0.852 on independent test datasets in brain tumor detection, discrimination, and molecular status prediction, respectively. The classifiers with pretrained weights outperformed the convolutional classifiers trained from scratch by approximately 10% in terms of accuracy and AUC across all tasks. The saliency regions in the correctly predicted cases are mainly clustered around the tumors. Classifiers derived from the two dropout schemes differed significantly only in the detection of brain tumors. Foundation models obtained from self-supervised learning have demonstrated encouraging potential for scalability and interpretability in downstream brain tumor-related tasks and hold promise for extension to neurological diseases with diffusely distributed lesions. The application of our proposed method to the prediction of key molecular status in gliomas is expected to improve treatment planning and patient outcomes. Additionally, the foundation model we developed could serve as a cornerstone for advancing AI applications in the diagnosis of brain-related diseases.

BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs.

Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological research. The experimental validation of drug-ADR associations often entails expensive and time-consuming investigations. Thus, a computational model to predict ADRs from known associations is essential for enhanced efficiency and cost-effectiveness. Here, we propose BiMPADR, a novel model that integrates drug gene expression into adverse reaction features using a message passing neural network on a bipartite graph of drugs and adverse reactions, leveraging publicly available data. By combining the computed adverse reaction features with the structural fingerprints of drugs, we predict the association between drugs and adverse reactions. Our models obtained high AUC (area under the receiver operating characteristic curve) values ranging from 0.861 to 0.907 in an external drug validation dataset under differential experiment conditions. The case study on multiple BET inhibitors also demonstrated the high accuracy of our predictions, and our model's exploration of potential adverse reactions for HWD-870 has contributed to its research and development for market approval. In summary, our method would provide a promising tool for ADR prediction and drug safety assessment in drug discovery and development.

The enhanced reliability of higher national institute of health stroke scale thresholds over the conventional 6-point scale

IntroductionIt is still uncertain if higher thresholds on National Institute of Health Stroke Scale (NIHSS) are better predictors of large infarctions than the conventional 6-point cutoff. MethodsWe used 6-point and higher NIHSS thresholds including 8, 9, and 10-point to predict relative infarct areas, expressed as percentage of the affected hemisphere on axial brain computed tomography images, beginning at 5% with 5% increments each time until reaching the 40% cutoff for large infarctions, or achieving 100% sensitivity. Results were compared using area under the receiver operating characteristic curves (AUROC). ResultsWe enrolled 151 patients of acute ischemic stroke (Mean age: 62.88 years ± 12.71; Female: 48.34%). 77 patients (50.99%) exhibited left hemisphere strokes, while 74 (49%) had right hemisphere involvement. Sensitivity values of the 6-point for infarcts measuring 5%, 10%, 20%, 30%, and 40% were 62%, 64%, 77%, 82%, and 100%, respectively. At 40% infarct-size, 8-point achieved comparable results (52%, 55%, 69%, 76%, 100%), closely aligning with the 9-point (50%, 53%, 69%, 76%, 100%). The10-point was slightly trailing behind in sensitivity at 40% infarct-core (96%). Moreover, higher thresholds exhibited improved false-positive rates (FPR). At 40% infarct size, the FPRs of 6, 8, 9, and 10 points were 39%, 27%, 27%, and 21% respectively. Higher thresholds had augmented AUROC values (0.86, 0.86, 0.89) as compared to the 6-point (0.80). Logistic regression identified 14-point as definitive cutoff for large infarctions. ConclusionHigher thresholds can better differentiate small and medium infarcts as true-negatives and substantially reduce false-positive referrals for mechanical thrombectomy.

The content of soluble forms of galectins -1, -3, -4, -7, -9 in patients with renal cell cancer of various morphological types

Background: Galectins are a family of β-galactoside binding proteins that regulate the vast majority of cellular functions, including proliferation, migration, adhesion, and phagocytosis in both health and disease. More and more experimental and clinical evidence indicates that galectins are involved in many stages of carcinogenesis, including patients with renal cell carcinoma (RCC).&#x0D; Aim: To analyze the clinical significance of soluble forms of galectins -1, -3, -4, -7, -9 in patients with various histological RCC types.&#x0D; Materials and methods: We performed a retrospective analysis of the clinical significance of galectins -1, -3, -4, -7, -9 in the serum of 140 RCC patients (84 with clear cell RCC (ccRCC), 38 with papillary (papRCC), 18 with chromophobe (chrRCC)) and in 73 healthy donors (control group), who were examined and treated from 2019 to 2023 in the N. N. Blokhin National Medical Research Center of Oncology. Galectin levels were measured in serum (obtained according to standard methods before the initiation of specific treatment) with an enzyme-linked immunosorbent assay.&#x0D; Results: There was a significant increase in serum galectin -1, -3, -9 levels in the whole RCC patient group, compared to the healthy donor control group; no increase was found for galectins -4 and -7. Serum galectin-1 levels in the ccRCC and papRCC patients were significantly higher than those in the controls (p = 0.0003 and p = 0.0135, respectively). No association between the serum galectins -1 and -7 and the clinical and morphological characteristics of RCC was found; however, serum galectin-7 levels in the papRCC patients correlated with the grade of tumor differentiation (r = -0.592; p = 0.001). The area under the ROC curve (AUC) for galectin-1 in ccRCC was 0.721 (p 0.0001), in papRCC 0.673 (p = 0.0086), and in chrRCC 0.576 (p = 0.355). For galectin-7, the ROC AUC values were 0.527 (p = 0.634) in ccRCC, 0.513 (p = 0.845) in papRCC, and 0.566 (p = 0.425) in chrRCC. In all histological types of RCC, there was a significant increase in serum galectin-3 compared to the controls (ccRCC, p = 0.0208; papRCC, p = 0.0014; chrRCC, p = 0.0041). The ROC analysis for galectin-3 in patients with RCC of various histological types showed AUC = 0.721 (p 0.0001) for ccRCC, 0.673 (p = 0.0086) for papRCC, and 0.576 (p = 0.355) for chrRCC. Galectin-9 levels was directly and significantly associated with the tumor size, as well as with regional metastases (r = 0.251, p = 0.021; r = 0.239, p = 0.028, respectively). The AUC values for galectin-4 were 0.619 (p = 0.021) in ccRCC, 0.577 (p = 0.214) in papRCC, and 0.534 (p = 0.666) for chrRCC. For galectin-9, they were 0.649 (p = 0.0075), 0.613 (p = 0.087), and 0.539 (p = 0.637), respectively.&#x0D; Conclusion: The study has demonstrated a certain association between serum galectin -1, -3, -4, -7, and -9 in the patients with RCC of various histological types. Although the results of the ROC analysis indicated average quality of the model, which does not allow for the use of the obtained data for diagnostic purposes, it is necessary to continue the research for better understanding of the mechanisms of galectin functioning, before galectin-based therapeutic agents would be introduced into clinical practice for the treatment of RCC.

FL% is associated with the severity of acute DeBakey type I aortic dissection in patients undergoing frozen elephant trunk and total arch replacement.

The diameter, area, and volume of the true lumen and false lumen (FL) have been measured in previous studies to evaluate the extent of DeBakey type I aortic dissection. However, these indicators have limitations because of the irregular shapes of the true and false lumens and the constant oscillation of intimal flap during systole and diastole. The ratio of arch lengths seems to be a more reliable indicator. FL% was defined as the ratio of the arch length of FL to the circumference of the aorta at the different levels of the aorta. The purpose of this article was to investigate whether FL% is a predictor of the severity of acute DeBakey type I aortic dissection in patients undergoing frozen elephant trunk (FET) and total arch replacement. In this retrospective observational study, we analyzed a total of 344 patients with acute DeBakey type I aortic dissection that underwent FET and total arch replacement at our center from October 2015 to October 2019. The patients were divided into two groups by cluster analysis according to the perioperative course. Binary logistic regression analyses were performed to determine whether FL% could predict the severity of acute DeBakey type I aortic dissection. The area under the receiver operating characteristic curve (AUROC) was used to assess the power of the multivariate logistic regression model for the severity of acute DeBakey type I aortic dissection. The patients in the ultra-high-risk group (109 patients) had significantly more severe clinical comorbidities and complications than the patients in the high-risk group (235 patients). The ascending aortic FL% [odds ratio (OR), 11.929 (95% CI: 1.421-100.11); P = 0.022], location of initial tear [OR, 0.68 (95% CI: 0.47-0.98); P = 0.041], the degree of left iliac artery involvement [OR, 1.95 (95% CI: 1.15-3.30); P = 0.013], and the degree of right coronary artery involvement [OR, 1.46 (95% CI: 1.01-2.12); P = 0.045] on preoperative computed tomography angiography were associated with the severity of acute DeBakey type I aortic dissection. The AUROC value of this multivariate logistic regression analysis was 0.940 (95% CI: 0.914-0.967; P < 0.001). The AUROC value of ascending aortic FL% was 0.841 (95% CI: 0.798-0.884; P < 0.001) for the severity of acute DeBakey type I aortic dissection in patients undergoing FET and total arch replacement. Ascending aortic FL% was validated as an essential radiologic index for assessing the severity of acute DeBakey type I aortic dissection in patients undergoing FET and total arch replacement. Higher values of ascending aortic FL% were more severe.

Optimizing Software Defect Prediction Models: Integrating Hybrid Grey Wolf and Particle Swarm Optimization for Enhanced Feature Selection with Popular Gradient Boosting Algorithm

Software defects, also referred to as software bugs, are anomalies or flaws in computer program that cause software to behave unexpectedly or produce incorrect results. These defects can manifest in various forms, including coding errors, design flaws, and logic mistakes, this defect have the potential to emerge at any stage of the software development lifecycle. Traditional prediction models usually have lower prediction performance. To address this issue, this paper proposes a novel prediction model using Hybrid Grey Wolf Optimizer and Particle Swarm Optimization (HGWOPSO). This research aims to determine whether the Hybrid Grey Wolf and Particle Swarm Optimization model could potentially improve the effectiveness of software defect prediction compared to base PSO and GWO algorithms without hybridization. Furthermore, this study aims to determine the effectiveness of different Gradient Boosting Algorithm classification algorithms when combined with HGWOPSO feature selection in predicting software defects. The study utilizes 13 NASA MDP dataset. These dataset are divided into testing and training data using 10-fold cross-validation. After data is divided, SMOTE technique is employed in training data. This technique generates synthetic samples to balance the dataset, ensuring better performance of the predictive model. Subsequently feature selection is conducted using HGWOPSO Algorithm. Each subset of the NASA MDP dataset will be processed by three boosting classification algorithms namely XGBoost, LightGBM, and CatBoost. Performance evaluation is based on the Area under the ROC Curve (AUC) value. Average AUC values yielded by HGWOPSO XGBoost, HGWOPSO LightGBM, and HGWOPSO CatBoost are 0.891, 0.881, and 0.894, respectively. Results of this study indicated that utilizing the HGWOPSO algorithm improved AUC performance compared to the base GWO and PSO algorithms. Specifically, HGWOPSO CatBoost achieved the highest AUC of 0.894. This represents a 6.5% increase in AUC with a significance value of 0.00552 compared to PSO CatBoost, and a 6.3% AUC increase with a significance value of 0.00148 compared to GWO CatBoost. This study demonstrated that HGWOPSO significantly improves the performance of software defect prediction. The implication of this research is to enhance software defect prediction models by incorporating hybrid optimization techniques and combining them with gradient boosting algorithms, which can potentially identify and address defects more accurately

Predicting Current and Future Habitat Suitability of an Endemic Species Using Data-Fusion Approach: Responses to Climate Change

Fritillaria imperialis L., an indicator plant species in Iran, is facing threats and its population has declined in recent years. To provide insights into the drivers affecting its loss, this research aims to identify the effects of three groups of factors, including climate, soil, and physiographic variables, on the current and future spatial distributions of F. imperialis. For this purpose, we used five machine learning algorithms as well as an ensemble forecasting of species distribution approach to explain the geographical distributions of the species as a function of these factors. In addition, we used two shared socio-economic pathways scenarios – SSP 1-2.6 and SSP 5-8.5 – to project the future distributions of F. imperialis in 2030, 2050, 2070, and 2090. Based on evaluation indices, area under the ROC curve (AUC) and true skill statistic (TSS), the Random Forest (RF) model generated the strongest prediction of the distribution of F. imperialis (TSS>0.9 and AUC>0.9). No significant difference observed among the three datasets (climate-only variables, climate + physiography variables, and climate + physiography + soil variables) in terms of AUC values. In models using climate + physiography + soil datasets, soil electrical conductivity, clay, and pH emerged as the most important variables affecting the growth and development of F. imperialis while climate factors played a lesser role in its distribution. Future projections revealed different patterns when using the optimistic (SSP 1-2.6) and pessimistic (SSP 5-8.5) socio-economic pathway scenarios and either the climate only or climate + physiography models. The climate + physiography + soil model produced similar prediction patterns for the scenarios. The climate-only models predicted larger areas suitable for crown imperial in the future than did the climate + physiography + soil model. These results emphasize the consideration of factors beyond climate scenarios when modeling biological responses to global warming and regional climate change.

Changes in the Range of Four Advantageous Grasshopper Habitats in the Hexi Corridor under Future Climate Conditions.

Angaracris rhodopa (Fischer et Walheim), Calliptamus abbreviatus (Ikonnikov), Myrmeleotettix palpalis (Zubowsky), and Oedaleus decorus asiaticus (Bey-Bienko) are the main grasshoppers that harm the natural grassland in the Hexi Corridor in Gansu, northwest China. In this study, the MaxEnt model was employed to identify the key environmental factors affecting the distribution of the four grasshoppers' habitats and to assess their distribution under current and future climate conditions. The aim was to provide a basis for grasshopper monitoring, prediction, and precise control. In this study, distribution of suitable habitats for A. rhodopa, C. abbreviates, M. palpalis, O. decorus asiaticus were predicted under current and future climatic scenarios using the Maxent model. The average AUC (area under the ROC curve) and TSS (true skill statistic) values of the four grasshoppers were greater than 0.9, and the simulation results were excellent and highly reliable. The mean annual precipitation was the main factor limiting the current range of suitable areas for these four species. Under the current climate, A. rhodopa, C. abbreviatus, and O. decorus asiaticus were mainly distributed in the central and eastern parts of the Hexi Corridor, and M. palpalis was distributed throughout the Hexi Corridor, with a suitable area of 1.29 × 104, 1.43 × 104, 1.44 × 104, and 2.12 × 104 km2, accounting for 13.7%, 15.2%, 15.3%, and 22.5% of the total area of the grasslands in the Hexi Corridor, respectively. The highly suitable areas of A. rhodopa, C. abbreviatus, and O. decorus asiaticus were mainly distributed in the eastern-central part of Zhangye City, the western part of Wuwei City, and the western and southern parts of Jinchang City, with areas of 0.20 × 104, 0.29 × 104, and 0.35 × 104 km2, accounting for 2.2%, 3%, and 3.7% of the grassland area, respectively. The high habitat of M. palpalis was mainly distributed in the southeast of Jiuquan City, the west, middle, and east of Zhangye City, the west of Wuwei City, and the west and south of Jinchang City, with an area of 0.32 × 104 km2, accounting for 3.4% of the grassland area. In the 2030s, the range of A. rhodopa, C. abbreviatus, and O. decorus asiaticus was predicted to increase; the range of M. palpalis will decrease. The results of this study could provide a theoretical basis for the precise monitoring and control of key areas of grasshoppers in the Hexi Corridor.

Comparison of scoring systems for predicting short- and long-term type 2 diabetes remission after bariatric surgery.

Our study aimed to compare the short- and particularly long-term type 2 diabetes mellitus (T2DM) remission prediction abilities of ABCD, individualised metabolic surgery (IMS), DiaRem2, Ad-DiaRem and DiaBetter scoring systems in Turkish adult type 2 diabetic morbidly obese patients who underwent bariatric surgery. Our study was planned as a retrospective cohort study. A total of 137 patients with T2DM, including 78 sleeve gastrectomy (SG) and 59 Roux-en-Y gastric bypass (RYGB) patients, were included in the 1st-year evaluation after bariatric surgery, and a total of 115 patients with T2DM, including 64 SG and 51 RYGB patients, were included in the evaluation at the end of the 5th year. In the 1st year after bariatric surgery, area under the ROC curve (AUC) values for diabetes remission scores were 0.863 for Ad-DiaRem, 0.896 for DiaBetter, 0.840 for DiaRem2, 0.727 for ABCD and 0.836 for IMS. At 5 years after bariatric surgery, the AUC values for diabetes remission were 0.834 for Ad-DiaRem, 0.888 for DiaBetter, 0.794 for DiaRem2, 0.730 for ABCD and 0.878 for IMS. According to our study, the DiaBetter score provided a better AUC value than the other scores both in the short and long term but showed similar predictive performance to Ad-DiaRem in the short term and IMS in the long term. We believe that DiaBetter and Ad-DiaRem scores might be more appropriate for short-term assessment and DiaBetter and IMS scores for long-term remission assessment.