Least Absolute Shrinkage And Selection Operator Research Articles

Radiomics analysis of dual-layer spectral-detector CT-derived iodine maps for predicting tumor deposits in colorectal cancer.

To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC). A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs - (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model's efficacy. The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035). Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies. The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making. Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge. The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs. The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.

Risk Factors and Predictive Nomogram for Survival in Elderly Patients with Brain Glioma.

To determine the factors that contribute to the survival of elderly individuals diagnosed with brain glioma and develop a prognostic nomogram. Data from elderly individuals (age ≥65 years) histologically diagnosed with brain glioma were sourced from the Surveillance, Epidemiology, and End Results (SEER) database. The dataset was randomly divided into a training cohort and an internal validation cohort at a 6:4 ratio. Additionally, data obtained from Tangdu Hospital constituted an external validation cohort for the study. The identification of independent prognostic factors was achieved through the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis, enabling the construction of a nomogram. Model performance was evaluated using C-index, ROC curves, calibration plot and decision curve analysis (DCA). A cohort of 20 483 elderly glioma patients was selected from the SEER database. Five prognostic factors (age, marital status, histological type, stage, and treatment) were found to significantly impact overall survival (OS) and cancer-specific survival (CSS), with tumor location emerging as a sixth variable independently linked to CSS. Subsequently, nomogram models were developed to predict the probabilities of survival at 6, 12, and 24 months. The assessment findings from the validation queue indicate a that the model exhibited strong performance. Our nomograms serve as valuable prognostic tools for assessing the survival probability of elderly glioma patients. They can potentially assist in risk stratification and clinical decision-making.

Identifying the Pattern Characteristics of Anoikis-Related Genes in Keloid.

Objective: Anoikis is a kind of programmed cell death that is triggered when cells lose contact with each other or with the matrix. However, the potential value of anoikis-related genes (ARGs) in keloid (KD) has not been investigated. Approach: We downloaded three keloid fibroblast (KF) RNA sequencing (RNA-seq) datasets from the Gene Expression Omnibus (GEO) and obtained 338 ARGs from a search of the GeneCards database and PubMed articles. Weighted correlation network analysis was used to construct the coexpression network and obtain the KF-related ARGs. The LASSO-Cox method was used to screen the hub ARGs and construct the best prediction model. Then, GEO single-cell sequencing datasets were used to verify the expression of hub genes. We used whole RNA-seq for gene-level validation and the correlation between KD immune infiltration and anoikis. Results: Our study comprehensively analyzed the role of ARGs in KD for the first time. The least absolute shrinkage and selection operator (LASSO) regression analysis identified six hub ARGs (HIF1A, SEMA7A, SESN1, CASP3, LAMA3, and SIK2). A large number of miRNAs participate in the regulation of hub ARGs. In addition, correlation analysis revealed that ARGs were significantly correlated with the infiltration levels of multiple immune cells in patients with KD. Innovation: We explored the expression characteristics of ARGs in KD, which is extremely important for determining the molecular pathways and mechanisms underlying KD. Conclusions: This study provides a useful reference for revealing the characteristics of ARGs in the pathogenesis of KD. The identified hub genes may provide potential therapeutic targets for patients. This study provides new ideas for individualized therapy and immunotherapy.

Study on the Prediction of Liver Injury in Acute Pancreatitis Patients by Radiomic Model Based on Contrast-Enhanced Computed Tomography.

to predict liver injury in acute pancreatitis (AP) patients by establishing a radiomics model based on contrast-enhanced computed tomography (CECT). a total of 1223 radiomic features were extracted from late arterial-phase pancreatic CECT images of 209 AP patients (146 in the training cohort and 63 in the test cohort), and the optimal radiomic features retained after dimensionality reduction by least absolute shrinkage and selection operator (LASSO) were used to construct a radiomic model through logistic regression analysis. In addition, clinical features were collected to develop a clinical model, and a joint model was established by combining the best radiomic features and clinical features to evaluate the practicality and application value of the radiomic models, clinical model and combined model. four potential features were selected from the pancreatic parenchyma to construct the radiomic model, and the area under the receiver operating characteristic curve (AUC) of the radiomic model was significantly greater than that of the clinical model for both the training cohort (0.993 vs. 0.653, p = 0.000) and test cohort (0.910 vs. 0.574, p = 0.000). The joint model had a greater AUC than the radiomics model for both the training cohort (0.997 vs. 0.993, p = 0.357) and test cohort (0.925 vs. 0.910, p = 0.302). the radiomic model based on CECT has good performance in predicting liver injury in AP patients and can guide clinical decision-making and improve the prognosis of patients with AP.</p>.

Analysis of barriers associated with emergency medical service activation in patients with acute stroke and acute myocardial infarction from Zhongjiang County of Sichuan Province in China

ObjectivesThe purpose of this study was to investigate the preferred modes of transportation to the hospital among patients with acute stroke and acute myocardial infarction (AMI), as well as to identify the factors that influence the utilization of ambulances.MethodsWe conducted a cross-sectional study, including patients who were diagnosed with acute stroke and AMI, at the people’s hospital of Zhongjiang, from September 30th, 2022 to August 30th, 2023. All patients were divided into emergency medical service (EMS)-activation group and self-transportation group. Chi-square and t-tests were utilized to discern differences between groups at baseline. To screen relevant variables, we employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis using R package glmnet. Subsequently, we performed a logistic regression analysis to identify predictors of EMS activation according the results of LASSO regression.Resultswe collected 929 valid questionnaires. 26.16% of the patients required the services of EMS. 90.9% of individuals have not received any formal first aid education. 42.1% of them reported that they had no understanding of cardiovascular and cerebrovascular diseases. Diagnosed as AMI (OR 0.22, 95%CI 0.06 to 0.88) or acute cerebral infarction (OR 0.26, 0.10 to 0.68), the distance between the patient and the nearest 120 network hospital when the patient had these symptoms (OR 0.97, 0.94 to 0.99), the patient’s son or daughter was there when the patient was symptomatic (OR 0.58, 0.37 to 0.94), the patient (OR 0.19, 0.05 to 0.72) and the patient’s partner (wife or husband) (OR 0.36, 0.16 to 0.85) had decided that the patient needed further medical help, Among patients who did not seek immediate help after symptom onset, thinking that the symptoms will disappear spontaneously (OR 0.34, 0.13 to 0.92) or not wanting to disturb others (OR 0.06, 0.01 to 0.66) or believing that they are not important symptoms (OR 0.15, 0.05 to 0.42) were factors independently associated with less ambulance use. Age (OR 1.02, 1.00 to 1.04), Stroke patients have experienced symptoms of disturbance of consciousness or convulsions (OR 2.99, 1.72 to 5.2) were independent factors associated with increased ambulance use.ConclusionThere is still ambulance underutilization among patients with acute stroke and AMI in county territory of China. Moreover, it is needed to raise the level of first aid education and awareness about EMS. Additionally, private clinic doctors and the public should gain adequate understanding of the severity of acute stroke and AMI, as well as their common symptoms, the crucial importance of prompt medical intervention. Finally, we propose that all township hospitals should be integrated into the 120 emergency networks and equipped with emergency first aid capabilities, pre-hospital care, and transportation abilities.

A novel CAF-cancer cell crosstalk-related gene prognostic index based on machine learning: prognostic significance and prediction of therapeutic response in head and neck squamous cell carcinoma

BackgroundCancer-associated fibroblast (CAF)-cancer cell crosstalk (CCCT) plays an important role in tumor microenvironment shaping and immunotherapy response. Current prognostic indexes are insufficient to accurately assess immunotherapy response in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to develop a CCCT-related gene prognostic index (CCRGPI) for assessing the prognosis and response to immune checkpoint inhibitor (ICI) therapy of HNSCC patients.MethodsTwo cellular models, the fibroblast-cancer cell indirect coculture (FCICC) model, and the fibroblast-cancer cell organoid (FC-organoid) model, were constructed to visualize the crosstalk between fibroblasts and cancer cells. Based on a HNSCC scRNA-seq dataset, the R package CellChat was used to perform cell communication analysis to identify gene pairs involved in CCCT. Least absolute shrinkage and selection operator (LASSO) regression was then applied to further refine the selection of these gene pairs. The selected gene pairs were subsequently subjected to stepwise regression to develop CCRGPI. We further performed a comprehensive analysis to determine the molecular and immune characteristics, and prognosis associated with ICI therapy in different CCRGPI subgroups. Finally, the connectivity map (CMap) analysis and molecular docking were used to screen potential therapeutic drugs.ResultsFCICC and FC-organoid models showed that cancer cells promoted the activation of fibroblasts into CAFs, that CAFs enhanced the invasion of cancer cells, and that CCCT was somewhat heterogeneous. The CCRGPI was developed based on 4 gene pairs: IGF1-IGF1R, LGALS9-CD44, SEMA5A-PLXNA1, and TNXB-SDC1. Furthermore, a high CCRGPI score was identified as an adverse prognostic factor for overall survival (OS). Additionally, a high CCRGPI was positively correlated with the activation of the P53 pathway, a high TP53 mutation rate, and decreased benefit from ICI therapy but was inversely associated with the abundance of various immune cells, such as CD4+ T cells, CD8+ T cells, and B cells. Moreover, Ganetespib was identified as a potential drug for HNSCC combination therapy.ConclusionsThe CCRGPI is reliable for predicting the prognosis and immunotherapy response of HSNCC patients and may be useful for guiding the individualized treatment of HNSCC patients.

Development of a Predictive Nomogram for Sepsis in Patients with Urolithiasis-Related Obstructive Pyelonephritis

Background and Objectives: In patients with urolithiasis-related obstructive pyelonephritis (UROP), sepsis represents a critical and concerning complication that can substantially increase the mortality rate. This study aimed to identify the risk factors for sepsis in UROP patients and to develop a predictive nomogram model. Materials and Methods: We analyzed data from 148 patients who met the UROP criteria and were admitted to Chang Gung Memorial Hospital between 1 January 2016 and 31 December 2021. The primary outcome evaluated was the incidence of sepsis, as defined by the most recent Sepsis-3 guidelines. To identify potential risk factors for sepsis, we employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression technique. Subsequently, we utilized multivariable logistic regression to construct the predictive model. Results: There was a total of 102 non-sepsis cases and 46 sepsis cases. Risk factors for sepsis in multivariable analysis were a history of diabetes mellitus (DM) (OR = 4.24, p = 0.007), shock index (SI) (×10−1) (OR = 1.55, p &lt; 0.001), C-reactive protein (CRP) (mg/dL) (OR = 1.08, p = 0.005), and neutrophil to lymphocyte ratio (NLR) (×10) (OR = 1.58, p = 0.007). The nomogram exhibited an area under the receiver operating characteristic curve of 0.890 (95% CI 0.830–0.949). Conclusions: Our study demonstrated that patients with UROP who have DM, higher SI, higher NLR, and elevated CRP levels are significantly more likely to develop sepsis. These insights may aid in risk stratification, and it is imperative that clinicians promptly initiate treatment for those identified as high risk.

Radiomics analysis combining gray-scale ultrasound and mammography for differentiating breast adenosis from invasive ductal carcinoma

ObjectivesTo explore the utility of gray-scale ultrasound (GSUS) and mammography (MG) for radiomic analysis in distinguishing between breast adenosis and invasive ductal carcinoma (IDC).MethodsData from 147 female patients with pathologically confirmed breast lesions (breast adenosis: 61 patients; IDC: 86 patients) between January 2018 and December 2022 were retrospectively collected. A training cohort of 113 patients (breast adenosis: 50 patients; IDC: 63 patients) diagnosed from January 2018 to December 2021 and a time-independent test cohort of 34 patients (breast adenosis: 11 patients; IDC: 23 patients) diagnosed from January 2022 to December 2022 were included. Radiomic features of lesions were extracted from MG and GSUS images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the most discriminant features, followed by logistic regression (LR) to construct clinical and radiomic models, as well as a combined model merging radiomic and clinical features. Model performance was assessed using receiver operating characteristic (ROC) analysis.ResultsIn the training cohort, the area under the curve (AUC) for radiomic models based on MG features, GSUS features, and their combination were 0.974, 0.936, and 0.991, respectively. In the test cohort, the AUCs were 0.885, 0.876, and 0.949, respectively. The combined model, incorporating clinical and all radiomic features, and the MG plus GSUS radiomics model were found to exhibit significantly higher AUCs than the clinical model in both the training cohort and test cohort (p&amp;lt;0.05). No significant differences were observed between the combined model and the MG plus GSUS radiomics model in the training cohort and test cohort (p&amp;gt;0.05).ConclusionThe effectiveness of radiomic features derived from GSUS and MG in distinguishing between breast adenosis and IDC is demonstrated. Superior discriminatory efficacy is shown by the combined model, integrating both modalities.

Development and validation of a nomogram to predict mortality of patients with DIC in ICU

BackgroundDisseminated intravascular coagulation (DIC) is a devastating condition, which always cause poor outcome of critically ill patients in intensive care unit. Studies concerning short-term mortality prediction in DIC patients is scarce. This study aimed to identify risk factors contributing to DIC mortality and construct a predictive nomogram.MethodsA total of 676 overt DIC patients were included. A Cox proportional hazards regression model was developed based on covariates identified using least absolute shrinkage and selection operator (LASSO) regression. The prediction performance was independently evaluated in the MIMIC-III and MIMIC-IV Clinical Database, as well as the 908th Hospital Database (908thH). Model performance was independently assessed using MIMIC-III, MIMIC-IV, and the 908th Hospital Clinical Database.ResultsThe Cox model incorporated variables identified by Lasso regression including heart failure, sepsis, height, SBP, lactate levels, HCT, PLT, INR, AST, and norepinephrine use. The model effectively stratified patients into different mortality risk groups, with a C-index of &amp;gt;0.65 across the MIMIC-III, MIMIC-IV, and 908th Hospital databases. The calibration curves of the model at 7 and 28 days demonstrated that the prediction performance was good. And then, a nomogram was developed to facilitate result visualization. Decision curve analysis indicated superior net benefits of the nomogram.ConclusionThis study provides a predictive nomogram for short-term overt DIC mortality risk based on a Lasso-Cox regression model, offering individualized and reliable mortality risk predictions.

Development of a nomogram for predicting malnutrition in elderly hospitalized cancer patients: a cross-sectional study in China

ObjectivesThe Patient-Generated Subjective Global Assessment (PG-SGA) serves as a specialized nutritional assessment instrument designed for cancer patients. Despite its specificity, the complexity and time requirements of this tool, along with the necessity for administration by trained professionals, limit its practicality in clinical settings. Our objective is to identify a straightforward, efficient, and dependable nutritional assessment tool to promote broader adoption in clinical practice.MethodsThis study encompassed a total of 450 patients diagnosed with cancer. Of these, 315 individuals constituted the training set, and the remaining 135 were allocated to the external validation set. The model variables were identified through the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. Binary logistic regression outcomes facilitated the development of a nomogram, offering a visual depiction of the predicted probabilities. The predictive accuracy of the nomogram model was evaluated by calculating the area under the Receiver Operating Characteristic (ROC) curve.ResultsThe LASSO method detected four variables that were included in the final prediction model: age, serum albumin levels (ALB), body mass index (BMI), and activities of daily living (ADL). The area under the curve (AUC) for this prediction model was 0.905. Both the internal and external calibration curves for malnutrition showed that the predictive nomogram model was highly accurate.ConclusionThe study has developed a prediction model that demonstrates remarkable accuracy in forecasting malnutrition. Furthermore, it presents a streamlined nutritional assessment tool aimed at swiftly identifying cancer patients at nutritional risk, thereby facilitating oncologists in delivering targeted nutritional support to these individuals.

Cellular Senescence-Related Long Non-coding RNA Signatures Predict Prognosis in Juvenile Osteosarcoma

AbstractOsteosarcoma is the most common malignant bone tumor and is frequently diagnosed in juvenile. Cellular senescence is a fundamental hallmark of osteosarcoma and plays a vital role in the initiation and progression of aging and tumorigenesis. Long non-coding RNAs (lncRNAs) are implicated in tumorigenesis. In this study, six cellular senescence-related lncRNAs with independent prognostic significance in juvenile osteosarcoma patients were identified through univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis. Prognostic significance was further confirmed by Kaplan–Meier (KM) survival curves, co-expression interaction networks, and sankey diagrams. A prognostic model of cellular senescence-related genes in juvenile osteosarcoma patients was then constructed using multivariate Cox regression analysis based on these six genes. High- and low-risk groups were identified according to the median risk score calculated by the prognostic model. The favorable prognostic significance of this model was demonstrated through survival curves, receiver operating characteristic (ROC) curves, distribution scatter plots and lncRNA expression heatmaps. Furthermore, cellular senescence-related lncRNAs were validated by enrichment analysis, immunological correlation analysis, m6A correlation analysis, and drug sensitivity correlation analysis. These findings are important for improving the prognosis of juvenile osteosarcoma patients and understanding the mechanisms underlying cellular senescence in juvenile osteosarcoma development.

Machine learning classification of new firearm injury encounters in the St Louis region: 2010-2020.

To improve firearm injury encounter classification (new vs follow-up) using machine learning (ML) and compare our ML model to other common approaches. This retrospective study used data from the St Louis region-wide hospital-based violence intervention program data repository (2010-2020). We randomly selected 500 patients with a firearm injury diagnosis for inclusion, with 808 total firearm injury encounters split (70/30) for training and testing. We trained a least absolute shrinkage and selection operator (LASSO) regression model with the following predictors: admission type, time between firearm injury visits, number of prior firearm injury emergency department (ED) visits, encounter type (ED or other), and diagnostic codes. Our gold standard for new firearm injury encounter classification was manual chart review. We then used our test data to compare the performance of our ML model to other commonly used approaches (proxy measures of ED visits and time between firearm injury encounters, and diagnostic code encounter type designation [initial vs subsequent or sequela]). Performance metrics included area under the curve (AUC), sensitivity, and specificity with 95% confidence intervals (CIs). The ML model had excellent discrimination (0.92, 0.88-0.96) with high sensitivity (0.95, 0.90-0.98) and specificity (0.89, 0.81-0.95). AUC was significantly higher than time-based outcomes, sensitivity was slightly (but not significantly) lower than other approaches, and specificity was higher than all other methods. ML successfully delineated new firearm injury encounters, outperforming other approaches in ruling out encounters for follow-up. ML can be used to identify new firearm injury encounters and may be particularly useful in studies assessing re-injuries.

SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification

A common and natural physiological response of the human body is cough, which tries to push air and other wastage thoroughly from the airways. Due to environmental factors, allergic responses, pollution or some diseases, cough occurs. A cough can be either dry or wet depending on the amount of mucus produced. A characteristic feature of the cough is the sound, which is a quacking sound mostly. Human cough sounds can be monitored continuously, and so, cough sound classification has attracted a lot of interest in the research community in the last decade. In this research, three systematic conglomerated models (SCMs) are proposed for audio cough signal classification. The first conglomerated technique utilizes the concept of robust models like the Cross-Correlation Function (CCF) and Partial Cross-Correlation Function (PCCF) model, Least Absolute Shrinkage and Selection Operator (LASSO) model, elastic net regularization model with Gabor dictionary analysis and efficient ensemble machine learning techniques, the second technique utilizes the concept of stacked conditional autoencoders (SAEs) and the third technique utilizes the concept of using some efficient feature extraction schemes like Tunable Q Wavelet Transform (TQWT), sparse TQWT, Maximal Information Coefficient (MIC), Distance Correlation Coefficient (DCC) and some feature selection techniques like the Binary Tunicate Swarm Algorithm (BTSA), aggregation functions (AFs), factor analysis (FA), explanatory factor analysis (EFA) classified with machine learning classifiers, kernel extreme learning machine (KELM), arc-cosine ELM, Rat Swarm Optimization (RSO)-based KELM, etc. The techniques are utilized on publicly available datasets, and the results show that the highest classification accuracy of 98.99% was obtained when sparse TQWT with AF was implemented with an arc-cosine ELM classifier.

A diagnostic model for Parkinson’s disease based on circadian rhythm-related genes

BackgroundCircadian rhythm (CR) disturbance is intricately associated with Parkinson’s disease (PD). However, the involvement of CR-related mechanisms in the pathogenesis and progression of PD remains elusive.MethodsA total of 141 PD patients and 113 healthy participants completed CR-related clinical examinations in this study. To further investigate the CR-related mechanisms in PD, we obtained datasets (GSE7621, GSE20141, GSE20292) from the Gene Expression Omnibus database to identify differentially expressed genes between PD patients and healthy controls and further selected CR-related genes (CRRGs). Subsequently, the least absolute shrinkage and selection operator (LASSO) followed by logistic algorithms were employed to identify the hub genes and construct a diagnostic model. The predictive performance was evaluated by area under the curve (AUC), calibration curve, and decision curve analyses in the training set and external validation sets. Finally, RT‒qPCR and Western blotting were conducted to verify the expression of these hub genes in blood samples. In addition, Pearson correlation analysis was utilized to validate the association between expression of hub genes and circadian rhythm function.ResultsOur clinical observational study revealed that even early-stage PD patients exhibited a higher likelihood of experiencing sleep disturbances, nocturnal hypertension, reverse-dipper blood pressure, and reduced heart rate variability compared to healthy controls. Furthermore, 4 CR-related hub genes (AGTR1, CALR, BRM14, and XPA) were identified and subsequently incorporated as candidate biomarkers to construct a diagnostic model. The model showed satisfactory diagnostic performance in the training set (AUC = 0.941), an external validation set GSE20295 (AUC = 0.842), and our clinical centre set (AUC = 0.805). Additionally, the up-regulation of CALR, BRM14 and the down-regulation of AGTR1, XPA were associated with circadian rhythm disruption.ConclusionCR disturbance seems to occur in the early stage of PD. The diagnostic model based on CR-related genes demonstrated robust diagnostic efficacy, offering novel insights for future clinical diagnosis of PD and providing a foundation for further exploration into the role of CR-related mechanisms in the progression of PD.

Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning

Background and aimsThe recurrence of papillary thyroid carcinoma (PTC) is not unusual and associated with risk of death. This study is aimed to construct a nomogram that combines clinicopathological characteristics and ultrasound radiomics signatures to predict the recurrence in PTC.MethodsA total of 554 patients with PTC who underwent ultrasound imaging before total thyroidectomy were included. Among them, 79 experienced at least one recurrence. Then 388 were divided into the training cohort and 166 into the validation cohort. The radiomics features were extracted from the region of interest (ROI) we manually drew on the tumor image. The feature selection was conducted using Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. And multivariate Cox regression analysis was used to build the combined nomogram using radiomics signatures and significant clinicopathological characteristics. The efficiency of the nomogram was evaluated by receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was used to analyze the recurrence-free survival (RFS) in different radiomics scores (Rad-scores) and risk scores.ResultsThe combined nomogram demonstrated the best performance and achieved an area under the curve (AUC) of 0.851 (95% CI: 0.788 to 0.913) in comparison to that of the radiomics signature and the clinical model in the training cohort at 3 years. In the validation cohort, the combined nomogram (AUC = 0.885, 95% CI: 0.805 to 0.930) also performed better. The calibration curves and DCA verified the clinical usefulness of combined nomogram. And the Kaplan-Meier analysis showed that in the training cohort, the cumulative RFS in patients with higher Rad-score was significantly lower than that in patients with lower Rad-score (92.0% vs. 71.9%, log rank P < 0.001), and the cumulative RFS in patients with higher risk score was significantly lower than that in patients with lower risk score (97.5% vs. 73.5%, log rank P < 0.001). In the validation cohort, patients with a higher Rad-score and a higher risk score also had a significantly lower RFS.ConclusionWe proposed a nomogram combining clinicopathological variables and ultrasound radiomics signatures with excellent performance for recurrence prediction in PTC patients.

Prognostic Assessmentin Patients With Primary Diffuse Large B-Cell Lymphoma of the Central Nervous System Using MRI-Based Radiomics.

Primary central nervous system lymphoma (PCNSL) carries a poor prognosis. Radiomics may hold potential value in prognostic assessment. To develop and validate an MRI-based radiomics model and combine it with clinical factors to assess progression-free survival (PFS) and overall survival (OS) of patients with PCNSL. Retrospective and prospective. Three hundred seventy-nine patients (179 female, 53 ± 7 years) from 2014 to 2022. T2/fluid-attenuated inversion recovery, contrast-enhanced T1WI and diffusion-weighted echo-planar imaging sequences on 3.0 T. Radiomics features were extracted from enhanced tumor regions on preoperative multi-sequence MRI. Using a least absolute shrinkage and selection operator (LASSO) Cox regression model to select radiomic signatures in training cohort (N = 169). Cox proportional hazards models were constructed for clinical, radiomics, and combined models, with internal (N = 72) and external (N = 32) cohorts validating model performance. Chi-squared, Mann-Whitney, Kaplan-Meier, log-rank, LASSO, Cox, decision curve analysis, time-dependent Receiver Operating Characteristic, area under the curve (AUC), and likelihood ratio test. P-value <0.05 was considered significant. Follow-up duration was 28.79 ± 22.59 months (median: 25). High-risk patients, determined by the median radiomics score, showed significantly lower survival rates than low-risk patients. Compared with NCCN-IPI, conventional imaging and clinical models, the combined model achieved the highest C-index for both PFS (0.660 internal, 0.802 external) and OS (0.733 internal, 0.781 external) in validation. Net benefit was greater with radiomics than with clinical alone. The combined model exhibited performance with AUCs of 0.680, 0.752, and 0.830 for predicting 1-year, 3-year, and 5-year PFS, and 0.770, 0.789, and 0.863 for OS in internal validation, with PFS AUCs of 0.860 and 0.826 and OS AUCs of 0.859 and 0.748 for 1-year and 3-year survival in external validation. Incorporating a multi-sequence MR-based radiomics model into clinical models enhances the assess accuracy for the prognosis of PCNSL. 4 TECHNICAL EFFICACY: Stage 2.

Population-Specific gene expression profiles in prostate cancer: insights from Weighted Gene Co-expression Network Analysis (WGCNA)

This study investigates the genetic factors contributing to the disparity in prostate cancer incidence and progression among African American men (AAM) compared to European American men (EAM). The research focuses on employing Weighted Gene Co-expression Network Analysis (WGCNA) on public microarray data obtained from prostate cancer patients. The study employed WGCNA to identify clusters of genes with correlated expression patterns, which were then analyzed for their connection to population backgrounds. Additionally, pathway enrichment analysis was conducted to understand the significance of the identified gene modules in prostate cancer pathways. The Least Absolute Shrinkage and Selection Operator (LASSO) and Correlation-based Feature Selection (CFS) methods were utilized for selection of biomarker genes. The results revealed 353 differentially expressed genes (DEGs) between AAM and EAM. Six significant gene expression modules were identified through WGCNA, showing varying degrees of correlation with prostate cancer. LASSO and CFS methods pinpointed critical genes, as well as six common genes between both approaches, which are indicative of their vital role in the disease. The XGBoost classifier validated these findings, achieving satisfactory prediction accuracy. Genes such as APRT, CCL2, BEX2, MGC26963, and PLAU were identified as key genes significantly associated with cancer progression. In conclusion, the research underlines the importance of incorporating AAM and EAM population diversity in genomic studies, particularly in cancer research. In addition, the study highlights the effectiveness of integrating machine learning techniques with gene expression analysis as a robust methodology for identifying critical genes in cancer research.

Development and validation of a nomogram for premature coronary artery disease patients in Guangzhou

BackgroundData regarding risk factors for premature coronary artery disease (PCAD) is scarce given that few research focus on it. This study aimed to develop and validate a clinical nomogram for PCAD patients in Guangzhou. MethodsWe recruited 108 PCAD patients (female ≤65 years old and male ≤55 years old) and 96 healthy controls from Sun Yat-sen Memorial Hospital of Sun Yat-sen University between 01/01/2021 and 31/12/2022. Twenty potentially relevant indicators of PCAD were extracted. Next, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection. The nomogram was developed based on the selected variables visually. ResultsIndependent risk factors, including body mass index (BMI), history of PCAD, glucose, Apolipoprotein A1(ApoA1), high density lipoprotein 2-cholesterol (HDL2-C), total cholesterol and triglyceride, were identified by LASSO and logistic regression analysis. The nomogram showed accurate discrimination (area under the receiver operator characteristic curve, ROC, 87.45 %, 95 % CI: 82.58 %-92.32 %). Decision curve analysis (DCA) suggested that the nomogram was clinical beneficial. HDL2, one risk factor, was isolated by a two-step discontinuous density-gradient ultracentrifugation method. And HDL2 from PCAD patients exhibited less 3H-cholesterol efflux (22.17 % vs 26.64 %, P < 0.05) and less delivery of NBD-cholesterol detecting by confocal microscope compared with healthy controls. ConclusionsIn conclusion, the seven-factor nomogram can achieve a reasonable relationship with PCAD, and a large cohort were needed to enhance the credibility and effectiveness of our model in future.

Prediction of diabetic retinopathy among type 2 diabetic patients in University of Gondar comprehensive specialized hospital, 2006–2021: A prognostic model

BackgroundThere has been a paucity of evidence for the development of a prediction model for diabetic retinopathy (DR) in Ethiopia. Predicting the risk of developing DR based on the patient’s demographic, clinical, and behavioral data is helpful in resource-limited areas where regular screening for DR is not available and to guide practitioners estimate the future risk of their patients. MethodsA retrospective follow-up study was conducted at the University of Gondar (UoG) Comprehensive Specialized Hospital from January 2006 to May 2021 among 856 patients with type 2 diabetes (T2DM). Variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. The data were validated by 10 fold cross validation. Four ML techniques (naïve Bayes, K-nearest neighbor, decision tree, and logistic regression) were employed. The performance of each algorithm was measured, and logistic regression was a well-performing algorithm. After multivariable logistic regression and model reduction, a nomogram was developed to predict the individual risk of DR. ResultsThe results showed that logistic regression was the best algorithm for predicting DR with an area under the curve of 92%, sensitivity of 87%, specificity of 83%, precision of 84%, F1-score of 85%, and accuracy of 85%. The logistic regression model selected seven predictors: total cholesterol, duration of diabetes, glycemic control, adherence to anti-diabetic medications, other microvascular complications of diabetes, sex, and hypertension. A nomogram was developed and deployed as a web-based application. A decision curve analysis showed that the model was useful in clinical practice and was better than treating all or none of the patients. ConclusionsThe model has excellent performance and a better net benefit to be utilized in clinical practice to show the future probability of having DR. Identifying those with a higher risk of DR helps in the early identification and intervention of DR.

Development and evaluation of interpretable machine learning regressors for predicting femoral neck bone mineral density in elderly men using NHANES data.

Osteoporotic femoral neck fractures (OFNFs) pose a significant orthopedic challenge in the elderly population, accounting for up to 40% of all osteoporotic fractures and leading to considerable health deterioration and increased mortality. In addressing the critical need for early identification of osteoporosis through routine screening of femoral neck bone mineral density (FNBMD), this study developed a user-friendly prediction model aimed at men aged 50 years and older, a demographic often overlooked in osteoporosis screening. Utilizing data from the National Health and Nutrition Examination Survey (NHANES), the study involved outlier detection and handling, missing value imputation via the K nearest neighbor (KNN) algorithm, and data normalization and encoding. The dataset was split into training and test sets with a 7:3 ratio, followed by feature screening through the least absolute shrinkage and selection operator (LASSO) and the Boruta algorithm. Eight different machine learning algorithms were then employed to construct predictive models, with their performance evaluated through a comprehensive metric suite. The random forest regressor (RFR) emerged as the most effective model, characterized by key predictors such as age, body mass index (BMI), poverty income ratio (PIR), serum calcium, and race, achieving a coefficient of determination (R²) of 0.218 and maintaining robustness in sensitivity analyses. Notably, excluding race from the model resulted in sustained high performance, underscoring the model's adaptability. Interpretations using Shapley additive explanations (SHAP) highlighted the influence of each feature on FNBMD. These findings indicate that our predictive model effectively aids in the early detection of osteoporosis, potentially reducing the incidence of OFNFs in this high-risk population.