Nonzero Coefficients Research Articles

Artificial intelligence-assisted precise preoperative prediction of lateral cervical lymph nodes metastasis in papillary thyroid carcinoma via a clinical-CT radiomic combined model.

This study aimed to develop an artificial intelligence-assisted model for the preoperative prediction of lateral cervical lymph node metastasis (LCLNM) in papillary thyroid carcinoma (PTC) using computed tomography (CT) radiomics, providing a new noninvasive and accurate diagnostic tool for PTC patients with LCLNM. This retrospective study included 389 confirmed PTC patients, randomly divided into a training set (n = 272) and an internal validation set (n = 117), with an additional 40 patients from another hospital as an external validation set. Patient demographics were evaluated to establish a clinical model. Radiomic features were extracted from preoperative contrast-enhanced CT images (venous phase) for each patient. Feature selection was performed using analysis of variance and the least absolute shrinkage and selection operator algorithm. We employed support vector machine, random forest (RF), logistic regression, and XGBoost algorithms to build CT radiomic models for predicting LCLNM. A radiomics score (Rad-score) was calculated using a radiomic signature-based formula. A combined clinical-radiomic model was then developed. The performance of the combined model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). A total of 1724 radiomic features were extracted from each patient's CT images, with 13 features selected based on nonzero coefficients related to LCLNM. Four clinically relevant factors (age, tumor location, thyroid capsule invasion, and central cervical lymph node metastasis) were significantly associated with LCLNM. Among the algorithms tested, the RF algorithm outperformed the others with five-fold cross-validation on the training set. After integrating the best algorithm with clinical factors, the areas under the ROC curves for the training, internal validation, and external validation sets were 0.910 (95% confidence interval [CI]: 0.729-0.851), 0.876 (95% CI: 0.747-0.911), and 0.821 (95% CI: 0.555-0.802), respectively, with DCA demonstrating the clinical utility of the combined radiomic model. This study successfully established a clinical-CT radiomic combined model for predicting LCLNM, which may significantly enhance surgical decision-making for lateral cervical lymph node dissection in patients with PTC.

An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer

To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort of 266 patients, comprising 115 individuals diagnosed with PNETs and 151 with pancreatic cancer. These patients were randomly assigned to the training or test group in a 7:3 ratio. The least absolute shrinkage and selection operator algorithm was employed to reduce the dimensionality of deep learning (DL) features extracted from pre-standardized EUS images. The retained nonzero coefficient features were subsequently applied to develop predictive eight DL models based on distinct machine learning algorithms. The optimal DL model was identified and used to establish a clinical signature, which subsequently informed the construction and evaluation of a nomogram. Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) were implemented to interpret and visualize the model outputs. A total of 2048 DL features were initially extracted, from which only 27 features with coefficients greater than zero were retained. The support vector machine (SVM) DL model demonstrated exceptional performance, achieving area under the curve (AUC) values of 0.948 and 0.795 in the training and test groups, respectively. Additionally, a nomogram was developed, incorporating both DL and clinical signatures, and was visually represented for practical application. Finally, the calibration curves, decision curve analysis (DCA) plots, and clinical impact curves (CIC) exhibited by the DL model and nomogram indicated high accuracy. The application of Grad-CAM and SHAP enhanced the interpretability of these models. These methodologies contributed substantial net benefits to clinical decision-making processes. A novel interpretable DL model and nomogram were developed and validated using EUS images, cooperating with machine learning algorithms. This approach demonstrates significant potential for enhancing the clinical applicability of EUS in predicting PNETs from pancreatic cancer, thereby offering valuable insights for future research and implementation.

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors

ObjectivesThe objective is to develop and validate intratumoral and peritumoral ultrasomics models utilizing endoscopic ultrasonography (EUS) to predict pathological grading in pancreatic neuroendocrine tumors (PNETs).MethodsEighty-one patients, including 51 with grade 1 PNETs and 30 with grade 2/3 PNETs, were included in this retrospective study after confirmation through pathological examination. The patients were randomly allocated to the training or test group in a 6:4 ratio. Univariate and multivariate logistic regression were used for screening clinical and ultrasonic characteristics. Ultrasomics is ultrasound-based radiomics. Ultrasomics features were extracted from both the intratumoral and peritumoral regions of conventional EUS images. Subsequently, the dimensionality of these radiomics features was reduced using the least absolute shrinkage and selection operator (LASSO) algorithm. A machine learning algorithm, namely multilayer perception (MLP), was employed to construct prediction models using only the nonzero coefficient features and retained clinical features, respectively.ResultsOne hundred seven ultrasomics features based on EUS were extracted, and only features with nonzero coefficients were ultimately retained. Among all the models, the combined ultrasomics model achieved the greatest performance, with an AUC of 0.858 (95% CI, 0.7512 - 0.9642) in the training group and 0.842 (95% CI, 0.7061 - 0.9785) in the test group. A calibration curve and a decision curve analysis (DCA) also demonstrated its accuracy and utility.ConclusionsThe integrated model using EUS ultrasomics features from intratumoral and peritumoral tumors accurately predicts PNETs' pathological grades pre-surgery, aiding personalized treatment planning.Trial registrationChiCTR2400091906.

Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis

ObjectiveIn clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aims to develop and validate an AI-driven radiomics prediction model for such nodules to enhance diagnostic accuracy.MethodsData of 2,591 pulmonary nodules from five medical centers (Zhengzhou People’s Hospital, etc.) were collected. Applying exclusion criteria, 370 nodules (78 benign, 292 malignant) with 3D CTR ≥ 50% were selected and randomly split 7:3 into training and validation cohorts. Using R programming, Lasso regression with 10-fold cross-validation filtered features, followed by univariate and multivariate logistic regression to construct the model. Its efficacy was evaluated by ROC, DCA curves and calibration plots.ResultsLasso regression picked 18 non-zero coefficients from 108 features. Three significant factors—patient age, solid component volume and mean CT value—were identified. The logistic regression equation was formulated. In the training set, the ROC AUC was 0.721 (95%CI: 0.642–0.801); in the validation set, AUC was 0.757 (95%CI: 0.632–0.881), showing the model’s stability and predictive ability.ConclusionThe model has moderate accuracy in differentiating benign from malignant 3D CTR ≥ 50% nodules, holding clinical potential. Future efforts could explore more to improve its precision and value.Clinical trial numberNot applicable.

Lung function-associated exposome profile in the era of climate change: Pooled analysis of 8 population-based European cohorts within the EXPANSE project.

The independent and interrelated long-term effects of the exposome such as air pollution, greenness, and ambient temperature on lung function are not well understood, yet relevant in the light of climate change. Pre-bronchodilation FEV1 from five mature birth cohorts (N=4724) and three adult cohorts (N=6052) from five European countries were used to assess cross-sectional associations with air pollution, greenness, and ambient temperature, assigned to their residential address. All two-way interactions and square terms were a priori included in building the final elastic net regression model. Elastic net regression results were put into the context of different environmental scenarios such as improvement of air quality, improvement of greenness, climate change, or their combinations. Elastic net regression of FEV1 z-scores identified non-zero coefficients for many interaction terms, indicating the importance of joint effects of exposure to air pollution, greenness, and temperature. The non-zero coefficients were bigger and more stable in adults than in children. Upon exploring lung function benefits for different environmental scenarios, an improvement of FEV1 was expected in the scenario of improving air quality or greenness. In contrast, negative changes in FEV1 z-scores were expected in the scenario of climate change, characterized by daily temperature increase in summer and decrease in winter. The beneficial FEV1 effects of improving air pollution or greenness were attenuated in the presence of climate change. Complex exposome profiles of long-term exposure to air pollution, greenness, and temperature showed associations with FEV1 in European adults, and to less extent in children and adolescents. Climate change seems to have a negative impact on lung function and modifies the association of air pollution and greenspace with lung function.

Assessment and analysis of factors influencing suicidal ideation in young adults: a large cohort study using an elastic network logistic regression model

ObjectivesThis study aims to review and analyze factors associated with suicidal ideation to provide a rationale for subsequent effective interventions.MethodsData from this study were obtained from the Assessing Nocturnal Sleep/Wake Effects on Risk of Suicide (ANSWERS). The University of Arizona evaluated 404 young adults aged 18–25 years using different scales. Then, general demographic data was recorded. An elastic network (EN) was used to optimize feature selection, combined with logistic regression, to determine the influencing factors associated with SI in young adults.ResultsThe EN regression retained 11 potential influencing factors with nonzero coefficients. In the multivariate logistic regression analysis, INQ-15 perceived burdensomeness (PB) scores (OR: 1.10, 95% CI: 1.04–1.17), CESD depression mood scores (OR: 1.16, 95% CI: 1.07–1.26), and age (OR: 0.72, 95% CI: 0.55–0.94) were significant factors for SI.ConclusionsThis is the first study to use an Elastic Network logistic regression model to assess the factors affecting suicidal ideation in young adults. Perceived Burdensome, depression, and age play an important risk role and are the best predictor combination of suicidal ideation in young adults, with depression being the most significant risk factor. Increased focus on Perceived Burdensome and negative emotions, along with simultaneous interventions for other potentially influential factors, can be more effective in preventing suicidal behavior in young adults.

Construction and validation of a personalized risk prediction model for in-hospital mortality in patients with acute myocardial infarction undergoing percutaneous coronary intervention.

Although emergency Percutaneous Coronary Intervention (PCI) has been shown to reduce mortality in patients with Acute Myocardial Infarction (AMI), the risk of in-hospital death remains high. In this study, the authors aimed to identify risk factors associated with in-hospital mortality in AMI patients who underwent PCI, develop a nomogram prediction model, and evaluate its effectiveness. The authors retrospectively analyzed data from 1260 patients who underwent emergency PCI at Dongyang People's Hospital between June 1, 2013, and December 31, 2021. Patients were divided into two groups based on in-hospital mortality: the death group (n = 61) and the survival group (n = 1199). Clinical data between the two groups were compared. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select non-zero coefficients of predictive factors. Multivariable logistic regression analysis was then performed to identify independent risk factors for in-hospital mortality in AMI patients after emergency PCI. A nomogram model for predicting the risk of in-hospital mortality in AMI patients after PCI was constructed, and its predictive performance was evaluated using the c-index. Internal validation was performed using the bootstrap method with 1000 resamples. The Hosmer-Lemeshow test was used to assess the goodness of fit, and a calibration curve was plotted to evaluate the model's calibration. LASSO regression identified d-dimer, B-type natriuretic peptide, white blood cell count, heart rate, aspartate aminotransferase, systolic blood pressure, and the presence of postoperative respiratory failure as important predictive factors for in-hospital mortality in AMI patients after PCI. Multivariable logistic regression analysis showed that d-dimer, B-type natriuretic peptide, white blood cell count, systolic blood pressure, and the presence of postoperative respiratory failure were independent factors for in-hospital mortality. A nomogram model for predicting the risk of in-hospital mortality in AMI patients after PCI was constructed using these independent predictive factors. The Hosmer-Lemeshow test yielded a Chi-Square value of 9.43 (p = 0.331), indicating a good fit for the model, and the calibration curve closely approximated the ideal model. The c-index for internal validation was 0.700 (0.560‒0.834), further confirming the predictive performance of the model. Clinical decision analysis demonstrated that the nomogram model had good clinical utility, with an area under the ROC curve of 0.944 (95 % CI 0.903‒0.963), indicating excellent discriminative ability. This study identified B-type natriuretic peptide, white blood cell count, systolic blood pressure, d-dimer, and the presence of respiratory failure as independent factors for in-hospital mortality in AMI patients undergoing emergency PCI. The nomogram model based on these factors showed high predictive accuracy and feasibility.

On nonzero coefficients of binary cyclotomic polynomials

On nonzero coefficients of binary cyclotomic polynomials

Essential covers of the hypercube require many hyperplanes

Abstract We prove a new lower bound for the almost 20-year-old problem of determining the smallest possible size of an essential cover of the $n$ -dimensional hypercube $\{\pm 1\}^n$ , that is, the smallest possible size of a collection of hyperplanes that forms a minimal cover of $\{\pm 1\}^n$ and such that, furthermore, every variable appears with a non-zero coefficient in at least one of the hyperplane equations. We show that such an essential cover must consist of at least $10^{-2}\cdot n^{2/3}/(\log n)^{2/3}$ hyperplanes, improving previous lower bounds of Linial–Radhakrishnan, of Yehuda–Yehudayoff, and of Araujo–Balogh–Mattos.

Small Tuning Parameter Selection for the Debiased Lasso

In this study, we investigate the bias and variance properties of the debiased Lasso in linear regression when the tuning parameter of the node-wise Lasso is selected to be smaller than in previous studies. We consider the case where the number of covariates p is bounded by a constant multiple of the sample size n. First, we show that the bias of the debiased Lasso can be reduced without diverging the asymptotic variance by setting the order of the tuning parameter to 1 / n . This implies that the debiased Lasso has asymptotic normality provided that the number of nonzero coefficients s 0 satisfies s 0 = o ( n / log p ) , whereas previous studies require s 0 = o ( n / log p ) if no sparsity assumption is imposed on the inverse of the second moment matrix of covariates. Second, we propose a data-driven tuning parameter selection procedure for the node-wise Lasso that is consistent with our theoretical results. Simulation studies show that our procedure yields confidence intervals with good coverage properties in various settings. We also present a real economic data example to demonstrate the efficacy of our selection procedure.

Designing unit Ising models for logic gate simulation through integer linear programming

Ising models minimizing a quadratic objective function with spin variables of either − 1 or + 1 are instrumental in tackling combinatorial optimization problems by programmable Quantum Annealers. This paper introduces unit Ising models, where non-zero coefficients are restricted to − 1 or + 1 . Due to the limited resolution of quantum annealers, unit Ising models are more suitable for quantum annealers. A fixed unit Ising model for logic circuits could lead to Application-Specific Unit Quantum Annealers (ASUQAs) for inverse function computation, similar to ASICs. Our findings suggest a powerful new method for compromising the RSA cryptosystem by leveraging ASUQAs for factorization.

T1WI Radiomics Analysis of Anterior Scalene Muscle: A Preliminary Application in Neurogenic Thoracic Outlet Syndrome.

This study aimed to analyze the differences in radiomic features of the anterior scalene muscle and evaluate the diagnostic performance of MRI-based radiomics model for neurogenic thoracic outlet syndrome (NTOS). Imaging data of patients with NTOS who underwent preoperative brachial plexus magnetic resonance neurography were collected and were randomly divided into training and test groups. The anterior scalene muscle area was sliced in the T1WI sequence as the region of interest for the extraction of radiomics features. The most significant features were identified using feature selection and dimensionality-reduction methods. Various machine learning algorithms were applied to construct regression models. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Totally, 267 radiomics features were extracted, of which 57 showed significant differences (P ≤ 0.05) between the abnormal and normal anterior scalene muscle groups. The least absolute shrinkage and selection operator regression model identified 13 optimal radiomic features with nonzero coefficients for constructing the model. In the training set, the AUROCs of diagnostic models built by different machine learning algorithms, ranked from highest to lowest, were as follows: support vector machine (SVM), 0.953; multilayer perception (MLP), 0.936; logistic regression (LR), 0.926; light gradient boosting machine (LightGBM), 0.906; and K-nearest neighbors (KNN), 0.813. In the testing set, the rankings were as follows: LR, 0.933; SVM, 0.886; KNN, 0.843; LightGBM, 0.824; and MLP, 0.706. NTOS is attributed to anterior scalene muscle abnormalities and exhibits distinct radiomic features. Integrating these features with machine learning can improve traditional manual image interpretation, offering further clarity in NTOS diagnosis.

Nomogram Based on HRV for Predicting the Therapeutic Effects of Orthostatic Training in Children with Vasovagal Syncope.

This study intended to find out whether the parameters of heart rate variability (HRV) can predict the treatment efficacy of orthostatic training among pediatric cases of vasovagal syncope (VVS). Patients with VVS who underwent orthostatic training were retrospectively enrolled. Lasso and logistic regression were used to sift through variables and build the model, which is visualized using a nomogram. The model's performance was evaluated through calibration plots, a receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) for both datasets. In total, 119 participants were included in the analysis, and 73 and 46 were assigned to the training and validation datasets, respectively. Five factors with nonzero coefficients were chosen based on lasso regression: age, the root means square of successive differences between normal sinus beats (rMSSD), standard deviation of the averages normal-to-normal intervals in all 5-min segments, minimum heart rate, and high frequency. Drawing from the logistic regression analysis results, the visual predictive model incorporated two variables, namely age and rMSSD. For the training dataset, the sensitivity was 0.686 and the specificity was 0.868 with an area under the curve (AUC) of 0.81 (95% CI, 0.71-0.91) for the ROC curve. For the validation dataset, the AUC of the ROC was 0.80 (95% CI, 0.66-0.93), while sensitivity and specificity were recorded at 0.625 and 0.909, respectively. In the calibration plots for both datasets, the predicted probabilities correlated well with the actual probabilities. According to the DCA, the visual predictive model gained a significant net benefit across a wide threshold range. Pediatric patients with VVS can benefit from orthostatic training using a visual predictive model comprising age and rMSSD.

Development of a Novel Nomogram Based on Ultrasonic Radiomics for Predicting Intrauterine Pregnancy After Frozen Embryo Transfer Cycle.

This study aimed to develop a nomogram for predicting intrauterine pregnancy after an in vitro frozen embryo transfer cycle using endometrial ultrasound radiomics. A total of 211 patients who underwent ultrasound examination on the day of endometrial transformation before the frozen embryo transfer cycle were enrolled. The patients were divided into an intrauterine pregnancy group and a pregnancy failure group based on ultrasound results. Clinical characteristics and radiomic features were analyzed using univariate and multivariate logistic regression analyses. A nomogram prediction model was established based on radiomic signatures and significant clinical factors. The model's robustness was assessed in training and external validation cohorts. Nine radiomic features were selected using least absolute shrinkage and selection operator (LASSO), and the radiomics score (Rad-score) was calculated as the sum of each feature multiplied by the nonzero coefficient from LASSO. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve based on the Rad-score was 0.72, 0.65, and 0.69 in the training, validation, and combined cohorts, respectively. To improve diagnostic efficiency, the Rad-score was further integrated with clinical factors to form a novel predictive nomogram. The results indicated that the AUC increased to 0.81, 0.67, and 0.77 in the training, validation, and combined cohorts, respectively. Decision curve analysis showed that the radiomics nomogram was clinically useful. The radiomics and clinical predictive nomogram can effectively predict intrauterine pregnancy after in vitro frozen embryo transfer and can be further applied in clinical strategy.

A new image compression method based on comparative thresholding

In recent years, image compression methods are the important research topics, these methods are classified into reversible and irreversiblet techniques. The irreversible techniques or called transformation techniques are the most widely used today. In fact, the transformation methods decompose the image into an orthonormal basis, such as a great number of the coefficients be equal to zero, without significantly impairing the visual aspect of the original image. Decades ago, the wavelet theory has given high contributions in the transformation methods by its nice mathematical properties and using the traditional thresholding as Hard and soft thresholding. However, if the image has a large singularity, we will be obliged to increase the number of non-zero wavelet coefficients in the reconstructed image. So, this situation costs us a lot, especially with color images. Therefore, we can pose the following problem to answer: are there thresholding operators can reduce the wavelet coefficients number independently on the singularity contained in the image?. In this paper, we propose a new method for image compression, called Comparative Thresholding (CT), which is based on constructing new thresholding operators that solve the imposed compression problem. These solution operators can enhance the traditional thresholding in terms of the visual quality of the compressed image. Experiments on a grayscale image using Discrete Wavelet Transform (DWT) with our proposed method (CT), show that CT method provides better performance compared to traditional thresholding in terms of the Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).

Targeted metabolomics identified novel metabolites, predominantly phosphatidylcholines and docosahexaenoic acid-containing lipids, predictive of incident chronic kidney disease in middle-to-elderly-aged Chinese adults

BackgroundEvidence is limited regarding the association of circulating metabolites with decline of kidney function, letting alone their value in prediction of development of chronic kidney disease (CKD). MethodsThis study included 3802 participants aged 64.1 ± 7.4 years from the Dongfeng-Tongji cohort, among whom 3327 were CKD-free at baseline (estimated glomerular filtration rate [eGFR] > 60 ml/min per 1.73 m2). We measured baseline levels of 211 metabolites with liquid chromatography coupled with mass spectrometry, including 25 amino acids, 12 acyl-carnitines, 161 lipids, and 13 other metabolites. ResultsThe mean (SD) absolute annual change in eGFR was −0.14 ± 4.11 ml/min per 1.73 m2 per year, and a total of 472 participants who were free of CKD at baseline developed incident CKD during follow-up of 4.6 ± 0.2 years (14.2 %). We identified a total of 22 metabolites associated with annual eGFR change and survived Bonferroni correction for multiple testing, including seven metabolites associated with eGFR increase (six being docosahexaenoic acid [DHA]-containing lipids) and 15 associated with eGFR decline (nine being phosphatidylcholines [PCs]). Among them, eight metabolites obtained non-zero coefficients in least absolute shrinkage and selection operator (LASSO) regression on incident CKD, indicating predictive potential, including one amino acid (arginine), one acyl-carnitine (C2), one lysophosphatidylcholine (LPC 22:6), two PCs (32:1 and 34:3), one triacylglycerol (TAG 56:8 [22:6]) and two other metabolites (inosine, niacinamide), and the composite score of these eight metabolites showed an odds ratio (OR) of 8.79 (95 % confidence interval [CI]: 7.49, 10.32; P < 0.001) per SD increase in association with incident CKD. The addition of the metabolite score increased the c-statistic of the reference model of traditional risk factors (including baseline eGFR) by 0.065 (95 % CI: 0.046 to 0.084; P = 3.39 × 10−11) to 0.765 (0.742 to 0.788) in 1000 repetitions of 10-fold cross-validation, while the application of two advanced machine learning algorithms, random forest (RF), and extreme gradient boosting (XGBoost) models produced similar c-statistics, to 0.753 (0.729 to 0.777) and 0.778 (0.733 to 0.824) with increases of 0.074 (0.055 to 0.093; P = 4.11 × 10−14) and 0.073 (0.032 to 0.114; P = 4.00 × 10−4), respectively. ConclusionsIn this study, we identified 22 metabolites associated with longitudinal eGFR change, nine of which were PCs and six were DHA-containing lipids. We screened out a panel of eight metabolites which improved prediction for the development of CKD by 9 % beyond traditional risk factors including baseline eGFR. Our findings highlighted involvement of lipid metabolism in kidney function impairment, and provided novel predictors for CKD risk.

MRI Radiomics-Based Machine Learning to Predict Lymphovascular Invasion of HER2-Positive Breast Cancer.

This study aims to develop and prospectively validate radiomic models based on MRI to predict lymphovascular invasion (LVI) status in patients with HER2-positive breast cancer. A total of 225 patients with HER2-positive breast cancer who preoperatively underwent breast MRI were selected, forming the training set (n = 99 LVI-positive, n = 126 LVI-negative). A prospective validation cohort included 130 patients with breast cancer from the Affiliated Zhongshan Hospital of Dalian University (n = 57 LVI-positive, n = 73 LVI-negative). A total of 390 radiomic features and eight conventional radiological characteristics were extracted. For the optimum feature selection phase, the LASSO regression model with tenfold cross-validation (CV) was employed to identify features with non-zero coefficients. The conventional radiological (CR) model was determined based on visual morphological (VM) features and the optimal radiomic features correlated with LVI, identified through multivariate logistic analyses. Subsequently, various machine learning (ML) models were developed using algorithms such as support vector machine (SVM), k-nearest neighbor (KNN), gradient boosting machine (GBM), and random forest (RF). The performance of ML and CR models. The results show that the AUC of the CR model in the training and validation sets were 0.81 (95% confidence interval [CI], 0.74-0.86) and 0.82 (95% CI, 0.69-0.89), respectively. The ML model achieved the best performance, with AUCs of 0.96 (95% CI, 0.99-1.00) in the training set and 0.95 (95% CI, 0.89-0.96) in the validation set. There were significant differences between the CR and ML models in predicting LVI status. Our study demonstrated that the machine learning models exhibited superior performance in predicting LVI status based on pretreatment MRI compared to the CR model, which does not necessarily rely on a priori knowledge of visual morphology.

A nomogram of inflammatory indexes for preoperatively predicting the risk of lymph node metastasis in colorectal cancer

PurposeTo investigate the independent risk factors associated with the development of lymph node metastasis (LNM) in patients with colorectal cancer (CRC), focusing on preoperative systemic inflammatory indicators, and to construct a corresponding risk predictive model.Materials and MethodsThe clinical data of 241 patients with CRC who underwent surgery after the first diagnosis between January 2012 and December 2017 at our hospital were reviewed. A best logistic regression model was constructed by Lasso regression for multivariate analysis, from which a Nomogram was derived. Using bootstrap to conduct internal validation. The model’s predictive performance and clinical practicability were evaluated using the receiver operating characteristic curve (ROC) curve, calibration curve, and decision curve analysis (DCA). External validation was conducted using retrospective data from 170 patients who underwent surgery between January 2020 and May 2022 at another hospital.ResultsCross-validation indicated smoking history, neutrophil–lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), fibrinogen-albumin ratio (FAR), and fecal occult blood (FOB) as variables with non-zero coefficients. These factors were included in the logistic regression, and multivariate analysis confirmed that smoking history, NLR, LMR, FAR, and FOB were independent risk factors (P < 0.05). The ROC and calibration curve of the original model and external validation indicated strong predictive power of the model. DCA suggested the model’s favorable clinical utility.ConclusionsThe model constructed in this study has robust predictive performance and clinical utility for the preoperative determination of CRC LMN, offering significant for clinical decision-making in patients with CRC.

Tridiagonal Weighting‐Based Interference Cancellation Method in High‐Mobility OFDM Systems

ABSTRACTIn orthogonal frequency division multiplexing (OFDM) systems, the time‐selective fading channel causes the signal Doppler expansion in high‐mobility scenarios, leading to intercarrier interference (ICI) in the received signals. People had employed a full‐dimensional weighting matrix method to reduce the Doppler‐assisted ICI, though it required a high implementation complexity. In order to reduce the ICI and produce lower complexities, this paper proposes to exploit the tridiagonal weighting matrix to suppress ICIs, in which an optimization problem is constructed to design the tridiagonal weighting matrix, and the nonzero coefficients within the tridiagonal matrix denote optimization variables. Moreover, the optimization objective exploits the system bit error rate (BER) instead of the conventional signal to interference plus noise ratio (SINR), which results in a highly nonlinear optimization problem. Therefore, an iterative solving approach is proposed according to the successive convex approximation (SCA). Simulations show that the proposed method can achieve a good balance between the complexity and BER degradation, especially in the scenario with moderate Doppler spreads.

Construction and Evaluation of a Model for the Prediction of Risk Factors Associated with Severe Pneumonia in Patients with Positive Hepatitis B Core Antibody and COVID-19 Infection in China

Background: Hepatitis B virus (HBV) infection is a high-risk factor for severe COVID-19 cases, leading clinicians to enhance the monitoring of patients with concurrent HBV and COVID-19 infections. However, this focus is typically on patients with active HBV infection. Patients who are HBV surface antigen (HBsAg) negative and HBV core antibody (anti-HBc) positive are often considered to have either a past infection that has naturally cleared or to be in a low-replication state, and thus receive less clinical attention. However, in immunosuppressed states, such as during immunosuppressive treatment following COVID-19 infection, the virus in these patients may reactivate. In such cases, this reactivation can increase the risk of COVID-19 progressing to a severe illness. Therefore, being HBsAg negative and anti-HBc positive could be a potential risk factor for severe COVID-19. Studying the combination of anti-HBc positivity and COVID-19 infection can help identify high-risk populations, allowing for the implementation of targeted prevention and management measures, thereby reducing the occurrence of severe COVID-19 cases. Objectives: To establish and evaluate a model for predicting severe pneumonia in patients with positive anti-HBc combined with COVID-19 infection. Methods: We retrospectively enrolled 380 patients who tested positive for anti-HBc, negative for HBsAg, and HBV e-antigen (HBeAg), combined with COVID-19 infection, in our hospital from December 2022 to May 2023. Based on the inclusion criteria, 163 patients were included in the study. We applied the Lasso binary logistic regression model to optimize feature selection, identifying eight non-zero coefficients using a minimum of one standard error. Using the multiple logistic regression method with backward selection, we screened six factors from the eight selected by the Lasso binary logistic regression model. These six factors were used to construct the predictive model and a nomogram. The validity of our nomogram was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow (HL) test, and decision-curve analysis (DCA). Results: Hypertension, diabetes, decreased absolute lymphocyte count, prolonged prothrombin time, elevated aspartate aminotransferase, and decreased albumin are high-risk factors for severe pneumonia in patients with positive anti-HBc combined with COVID-19 infection. The AUC of the predictive model constructed using these six factors is 0.785, with a 95% confidence interval of (0.709-0.862). The HL test, performed using the calibration curve, yielded a p-value of 0.868. The application of this diagnostic curve will increase the net benefit when the threshold probability is between 5% and 75%. Conclusions: The constructed nomogram can be used to predict the risk of patients with positive anti-HBc combined with COVID-19 infection progressing to severe pneumonia, based on routine blood parameters, liver function, coagulation, lactate dehydrogenase levels, and the patient's underlying disease. The predictive model demonstrates good discrimination, calibration, and clinical utility.