Significance of metastatic lymph nodes ratio in overall survival for patients with resected nonsmall cell lung cancer: a retrospective cohort study.

OBJECTIVE: The tumor, node and metastasis stage is widely applied to classify lung cancer and is the foundation of clinical decisions. However, increasing studies have pointed out that this staging system is not precise enough for the N status. In this study, we aim to build a convenient survival prediction model that incorporates the current items of lymph node status. METHODS: We performed a retrospective cohort study and collected the data from resectable nonsmall cell lung cancer (NSCLC) (IA-IIIB) patients from the Surveillance, Epidemiology, and End Results database (2006-2015). The x-tile program was applied to calculate the optimal threshold of metastatic lymph node ratio (MLNR). Then, independent prognostic factors were determined by multivariable Cox regression analysis and enrolled to build a nomogram model. The calibration curve as well as the Concordance Index (C-index) were selected to evaluate the nomogram. Finally, patients were grouped based on their specified risk points and divided into three risk levels. The prognostic value of MLNR and examined lymph node numbers (ELNs) were presented in subgroups. RESULTS TOTALLY,: 40853 NSCLC patients after surgery were finally enrolled and analyzed. Age, metastatic lymph node ratio, histology type, adjuvant treatment and American Joint Committee on Cancer 8th T stage were deemed as independent prognostic parameters after multivariable Cox regression analysis. A nomogram was built using those variables, and its efficiency in predicting patients' survival was better than the conventional American Joint Committee on Cancer stage system after evaluation. Our new model has a significantly higher concordance Index (C-index) (training set, 0.683 v 0.641, respectively; P < 0.01; testing set, 0.676 v 0.638, respectively; P < 0.05). Similarly, the calibration curve shows the nomogram was in better accordance with the actual observations in both cohorts. Then, after risk stratification, we found that MLNR is more reliable than ELNs in predicting overall survival. CONCLUSION: We developed a nomogram model for NSCLC patients after surgery. This novel and useful tool outperforms the widely used tumor, node and metastasis staging system and could benefit clinicians in treatment options and cancer control.

Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods.

BACKGROUND: Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare. METHODS: The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility. RESULTS: For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214-0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort). CONCLUSION: We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.

A nomogram for predicting hemorrhagic shock in pediatric patients with multiple trauma.

The timely detection and management of hemorrhagic shock hold paramount importance in clinical practice. This study was designed to establish a nomogram that may facilitate early identification of hemorrhagic shock in pediatric patients with multiple-trauma. A retrospective study was conducted utilizing a cohort comprising 325 pediatric patients diagnosed with multiple-trauma, who received treatment at the Children's Hospital, Zhejiang University School of Medicine, Zhejiang, China. For external validation, an additional cohort of 144 patients from a children's hospital in Taizhou was included. The model's predictor selection was optimized through the application of the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Subsequently, a prediction nomogram was constructed using multivariable logistic regression analysis. The performance and clinical utility of the developed model were comprehensively assessed utilizing various statistical metrics, including Harrell's Concordance Index (C-index), receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA). Multivariate logistic regression analysis identified systolic blood pressure (ΔSBP), platelet count, activated partial thromboplastin time (APTT), and injury severity score (ISS) as independent predictors for hemorrhagic shock. The nomogram constructed using these predictors demonstrated robust predictive capabilities, as evidenced by an impressive area under the curve (AUC) value of 0.963. The model's goodness-of-fit was assessed using the Hosmer-Lemeshow test (χ2 = 10.023, P = 0.209). Furthermore, decision curve analysis revealed significantly improved net benefits with the model. External validation further confirmed the reliability of the proposed predictive nomogram. This study successfully developed a nomogram for predicting the occurrence of hemorrhagic shock in pediatric patients with multiple trauma. This nomogram may serve as an accurate and effective tool for timely and efficient management of children with multiple trauma.

Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China.

BACKGROUND: Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators. METHODS: Patients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients. RESULTS: A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis. CONCLUSION: The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients.

Risk factor analysis and establishment of a nomogram model to predict blood loss during total knee arthroplasty.

PURPOSE: The risk factors for excessive blood loss and transfusion during total knee arthroplasty (TKA) remain unclear. The present study aimed to determine the risk factors for excessive blood loss and establish a predictive model for postoperative blood transfusion. METHODS: This retrospective study included 329 patients received TKA, who were randomly assigned to a training set (n = 229) or a test set (n = 100). Univariate and multivariate linear regression analyses were used to determine risk factors for excessive blood loss. Univariate and multivariate logistic regression analyses were used to determine risk factors for blood transfusion. R software was used to establish the prediction model. The accuracy and stability of the models were evaluated using calibration curves, consistency indices, and receiver operating characteristic (ROC) curve analysis. RESULTS: Risk factors for excessive blood loss included timing of using a tourniquet, the use of drainage, preoperative ESR, fibrinogen, HCT, ALB, and free fatty acid levels. Predictors in the nomogram included timing of using a tourniquet, the use of drainage, the use of TXA, preoperative ESR, HCT, and albumin levels. The area under the ROC curve was 0.855 (95% CI, 0.800 to 0.910) for the training set and 0.824 (95% CI, 0.740 to 0.909) for the test set. The consistency index values for the training and test sets were 0.855 and 0.824, respectively. CONCLUSIONS: Risk factors for excessive blood loss during and after TKA were determined, and a satisfactory and reliable nomogram model was designed to predict the risk for postoperative blood transfusion.

Exploring the prognostic significance of immunogenic cell death-related genes as risk biomarkers in cervical cancer.

BACKGROUND: Immunogenic cell death (ICD) is a process in which dying cells stimulate an immune response. It is a regulated form of cell death that can remodel the tumor microenvironment (TME) and activate the immune system, making immunotherapy more effective. This work was designed to identify prognostic gene features associated with ICD in cervical cancer (CC). METHODS: Based on CC datasets and a set of ICD-related genes obtained from public databases, we first filtered out ICD-related genes unrelated to CC survival using univariate analysis. Subsequently, LASSO regression and multivariate Cox regression analysis were employed to develop prognostic feature genes based on ICD. For the construction and validation of the model, eight genes (CXCL1, IL1B, TNF, YKT6, PDIA3, ROCK1, CXCR3, and CLEC9A) were chosen. A nomogram was created to forecast the prognosis of CC individuals, and Kaplan-Meier curves were utilized to explore the survival disparities among different risk groups of CC individuals. RESULTS: ssGSEA analysis was employed to investigate immune differences between two risk groups, revealing that the low-risk group exhibited elevated levels of immune cell infiltration, enhanced activation of immune function, and a higher immunophenoscore compared with the other group, which highlighted the relevance of ICD to TME. CONCLUSION: We constructed a prognostic model based on genetic biomarkers of ICD for prognostic prediction of CC patients. Our model demonstrated excellent discriminative and calibration capabilities, providing a valuable tool for prognostic prediction and assessing the potential efficacy of immunotherapy in CC.

Prediction of clinical risk assessment and survival in chronic obstructive pulmonary disease with pulmonary hypertension.

BACKGROUND: Patients with pulmonary hypertension (PH) and chronic obstructive pulmonary disease (COPD) have an increased risk of disease exacerbation and decreased survival. We aimed to develop and validate a non-invasive nomogram for predicting COPD associated with severe PH and a prognostic nomogram for patients with COPD and concurrent PH (COPD-PH). METHODS: This study included 535 patients with COPD-PH from six hospitals. A multivariate logistic regression analysis was used to analyse the risk factors for severe PH in patients with COPD and a multivariate Cox regression was used for the prognostic factors of COPD-PH. Performance was assessed using calibration, the area under the receiver operating characteristic curve and decision analysis curves. Kaplan-Meier curves were used for a survival analysis. The nomograms were developed as online network software. RESULTS: Tricuspid regurgitation velocity, right ventricular diameter, N-terminal pro-brain natriuretic peptide (NT-proBNP), the red blood cell count, New York Heart Association functional class and sex were non-invasive independent variables of severe PH in patients with COPD. These variables were used to construct a risk assessment nomogram with good discrimination. NT-proBNP, mean pulmonary arterial pressure, partial pressure of arterial oxygen, the platelet count and albumin were independent prognostic factors for COPD-PH and were used to create a predictive nomogram of overall survival rates. CONCLUSIONS: The proposed nomograms based on a large sample size of patients with COPD-PH could be used as non-invasive clinical tools to enhance the risk assessment of severe PH in patients with COPD and for the prognosis of COPD-PH. Additionally, the online network has the potential to provide artificial intelligence-assisted diagnosis and treatment. HIGHLIGHTS: A multicentre study with a large sample of chronic obstructive pulmonary disease (COPD) patients diagnosed with PH through right heart catheterisation. A non-invasive online clinical tool for assessing severe pulmonary hypertension (PH) in COPD. The first risk assessment tool was established for Chinese patients with COPD-PH.

Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score.

Introduction: This study aimed to develop a prognostic nomogram for predicting the recurrence-free survival (RFS) of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients with low preoperative platelet-albumin-bilirubin (PALBI) scores after transarterial chemoembolization (TACE) combined with local ablation treatment. Methods: We gathered clinical data from 632 HBV-related HCC patients who received the combination treatment at Beijing You'an Hospital, affiliated with Capital Medical University, from January 2014 to January 2020. The patients were divided into two groups based on their PALBI scores: low PALBI group (n=247) and high PALBI group (n=385). The low PALBI group was then divided into two cohorts: training cohort (n=172) and validation cohort (n=75). We utilized eXtreme Gradient Boosting (XGBoost), random survival forest (RSF), and multivariate Cox analysis to pinpoint the risk factors for RFS. Then, we developed a nomogram based on the screened factors and assessed its risk stratification capabilities and predictive performance. Results: The study finally identified age, aspartate aminotransferase (AST), and prothrombin time activity (PTA) as key predictors. The three variables were included to develop the nomogram for predicting the 1-, 3-, and 5-year RFS of HCC patients. We confirmed the nomogram's ability to effectively discern high and low risk patients, as evidenced by Kaplan-Meier curves. We further corroborated the excellent discrimination, consistency, and clinical utility of the nomogram through assessments using the C-index, area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Conclusion: Our study successfully constructed a robust nomogram, effectively predicting 1-, 3-, and 5-year RFS for HBV-related HCC patients with low preoperative PALBI scores after TACE combined with local ablation therapy.

Predictive nomogram of high-risk patients with active tuberculosis in latent tuberculosis infection.

INTRODUCTION: The absence of predictive models for early latent tuberculosis infection (LTBI) progression persists. This study aimed to create a screening model to identify high-risk LTBI patients prome to active tuberculosis (ATB) reactivation. METHODOLOGY: Patients with confirmed ATB were enrolled alongside LTBI individuals as a reference, with relevant clinical data gathered. LASSO regression cross-validation reduced data dimensionality. A nomogram was developed using multiple logistic regression, internally validated with Bootstrap resampling. Evaluation included C-index, receiver operating characteristic (ROC) curve, and calibration curves, with clinical utility assessed through decision curve analysis. RESULTS: The final nomogram incorporated serum albumin (OR = 1.337, p = 0.046), CD4+ (OR = 1.010, p = 0.004), and CD64 index (OR = 0.009, p = 0.020). The model achieved a C-index of 0.964, an area under the ROC curve of 0.962 (95% CI: 0.926-0.997), sensitivity of 0.971, and specificity of 0.910. Internal validation showed a mean absolute error of 0.013 and 86.4% identification accuracy. The decision curve indicated substantial net benefit at a risk threshold exceeding 10% (1: 9). CONCLUSIONS: This study established a biologically-rooted nomogram for high-risk LTBI patients prone to ATB reactivation, offering strong predictability, concordance, and clinical value. It serves as a personalized risk assessment tool, accurately identifying patients necessitating priority prophylactic treatment, complementing existing host risk factors effectively.

Development of a predictive nomogram for intermediate-risk differentiated thyroid cancer patients after fixed 3.7GBq (100mCi) radioiodine remnant ablation.

Objectives: The objective of this study was to develop a predictive nomogram for intermediate-risk differentiated thyroid cancer (DTC) patients after fixed 3.7GBq (100mCi) radioiodine remnant ablation (RRA). Methods: Data from 265 patients who underwent total thyroidectomy with central lymph node dissection (CND) and received RRA treatment at a single institution between January 2018 and March 2023 were analyzed. Patients with certain exclusion criteria were excluded. Univariate and multivariate logistic regression analyses were performed to identify risk factors for a non-excellent response (non-ER) to RRA. A nomogram was developed based on the risk factors, and its performance was validated using the Bootstrap method with 1,000 resamplings. A web-based dynamic calculator was developed for convenient application of the nomogram. Results: The study included 265 patients with intermediate-risk DTC. Significant differences were found between the ER group and the non-ER group in terms of CLNM>5, Hashimoto's thyroiditis, sTg level, TgAb level (P < 0.05). CLNM>5 and sTg level were identified as independent risk factors for non-ER in multivariate analysis. The nomogram showed high accuracy, with an area under the curve (AUC) of 0.833 (95% CI = 0.770-0.895). The nomogram's predicted probabilities aligned closely with actual clinical outcomes. Conclusions: This study developed a predictive nomogram for intermediate-risk DTC patients after fixed 3.7GBq (100mCi) RRA. The nomogram incorporates CLNM>5 and sTg levels as risk factors for a non-ER response to RRA. The nomogram and web-based calculator can assist in treatment decision-making and improve the precision of prognosis information. Further research and validation are needed.

Establishment and validation of nomogram model for predicting major adverse cardiac events in patients with acute ST-segment elevation myocardial infarction based on glycosylated hemoglobin A1c to apolipoprotein A1 ratio: An observational study.

The objective of the current study is to assess the usefulness of HbA1cAp ratio in predicting in-hospital major adverse cardiac events (MACEs) among acute ST-segment elevation myocardial infarction (STEMI) patients that have undergone percutaneous coronary intervention (PCI). Further, the study aims to construct a ratio nomogram for prediction with this ratio. The training cohort comprised of 511 STEMI patients who underwent emergency PCI at the Huaibei Miners' General Hospital between January 2019 and May 2023. Simultaneously, 384 patients treated with the same strategy in First People's Hospital of Hefei formed the validation cohort during the study period. LASSO regression was used to screen predictors of nonzero coefficients, multivariate logistic regression was used to analyze the independent factors of in-hospital MACE in STEMI patients after PCI, and nomogram models and validation were established. The LASSO regression analysis demonstrated that systolic blood pressure, diastolic blood pressure, D-dimer, urea, and glycosylated hemoglobin A1c (HbA1c)/apolipoprotein A1 (ApoA1) were significant predictors with nonzero coefficients. Multivariate logistic regression analysis was further conducted to identify systolic blood pressure, D-dimer, urea, and HbA1c/ApoA1 as independent factors associated with in-hospital MACE after PCI in STEMI patients. Based on these findings, a nomogram model was developed and validated, with the C-index in the training set at 0.77 (95% CI: 0.723-0.817), and the C-index in the validation set at 0.788 (95% CI: 0.734-0.841), indicating excellent discrimination accuracy. The calibration curves and clinical decision curves also demonstrated the good performance of the nomogram models. In patients with STEMI who underwent PCI, it was noted that a higher HbA1c of the ApoA1 ratio is significantly associated with in-hospital MACE. In addition, a nomogram is constructed having considered the above-mentioned risk factors to provide predictive information on in-hospital MACE occurrence in these patients. In particular, this tool is of great value to the clinical practitioners in determination of patients with a high risk.

CDCA gene family promotes progression and prognosis in lung adenocarcinoma.

BACKGROUND: The cell division cycle-associated (CDCA) family participates in the cell cycle, and the dysregulation of its expression is associated with the development of several types of cancers. However, the roles of CDCAs in lung adenocarcinomas (LUAD) have not been investigated in systematic research. METHODS: Using data retrieved from The Cancer Genome Atlas (TCGA), the expression of CDCAs in LUAD and normal tissues was compared, and survival analysis was performed using the data. Also, the correlation between clinical characteristics and the expression of CDCAs was assessed. Using data from cBioPortal, we investigated genetic alterations in CDCAs and their prognostic implications. Immunohistochemical analyses were performed to validate our findings from TCGA data. Following this, we created a risk score model to develop a nomogram. We also performed gene set enrichment analyses (GSEA), gene ontology, and KEGG pathway analysis. We used Timer to analyze the correlation between immune cell infiltration, tumor purity, and expression data. RESULTS: Our results indicated that all CDCAs were expressed at high levels in LUAD; this could be associated with poor overall survival, as indicated in TCGA data. Univariate and multivariate Cox analyses revealed that CDCA4/5 could serve as independent risk factors. The results of immunohistochemical analyses confirmed our results. Based on the estimation of expression levels, clinical characteristics, alterations, and immune infiltration, the low-risk group of CDCA4/5 had a better prognosis than the high-risk group. Immune therapy is also a potential treatment option. CONCLUSION: In conclusion, our findings indicate that CDCAs play important roles in LUAD, and CDCA4/5 can serve as diagnostic and prognostic biomarkers and therapeutic targets in LUAD.

Development and validation of nomograms to predict survival of primary cervical lymphoma: A surveillance, epidemiology, and end results (SEER) database analysis.

Due to the rarity of primary cervical lymphoma (PCL), the long-term survival of patients with cervical lymphoma and factors influencing survival are unknown. This study aimed to compare the survivals of patients with PCL and those with other cervical tumors and construct a clinical prediction model to assess the prognosis of patients with PCL. Patients with PCL from the Surveillance, Epidemiology, and End Results database were allocated randomly in a 7:3 ratio to the training and validation sets. Cox proportional hazard and Fine-Gray models were used to verify independent factors influencing overall survival (OS) and disease-specific survival (DSS), and nomograms were constructed. Receiver operating characteristic curve analysis and decision curve analysis (DCA) were used to test the performance and clinical utility of the models, respectively. We included 206 patients with PCL. The areas under the curves (AUCs) and DCA showed that all models had clinical benefits; The models constructed in this study had a predictive performance for patients with PCL. It can guide clinicians to rationalize the treatment plan for patients.

Risk factors and clinical prediction models for osteoporosis in pre-dialysis chronic kidney disease patients.

BACKGROUND: Osteoporosis in pre-dialysis chronic kidney disease (CKD) patients has been overlooked, and the risk factors of osteoporosis in these patients have not been adequately studied. OBJECTIVE: To identify risk factors for osteoporosis in pre-dialysis CKD patients and develop predictive models to estimate the likelihood of osteoporosis. METHODS: Dual-energy X-ray absorptiometry was used to measure bone mineral density, and clinical examination results were collected from 326 pre-dialysis CKD patients. Binary logistic regression was employed to explore the risk factors associated with osteoporosis and develop predictive models. RESULTS: In this cohort, 53.4% (n = 174) were male, 46.6% (n = 152) were female, and 21.8% (n = 71) were diagnosed with osteoporosis. Among those diagnosed with osteoporosis, 67.6% (n = 48) were female and 32.4% (n = 23) were male. Older age and low 25-(OH)-Vitamin D levels were identified as risk factors for osteoporosis in males. For females, older age, being underweight, higher bone alkaline phosphatase (NBAP), and advanced CKD (G5) were significant risk factors, while higher iPTH was protective. Older age, being underweight, and higher NBAP were risk factors for osteoporosis in the G1-4 subgroup. In the G5 subgroup, older age and higher NBAP increased the risk, while high 25-(OH)-Vitamin D or iPTH had protective effects. Nomogram models were developed to assess osteoporosis risk in pre-dialysis patients based on gender and renal function stage. CONCLUSION: Risk factors for osteoporosis vary by gender and renal function stages. The nomogram clinical prediction models we constructed may aid in the rapid screening of patients at high risk of osteoporosis.

Ultrasound-based comparative analysis and nomogram development for predicting triple-negative and non-triple-negative breast cancer: a 4-year institutional study in Quanzhou First Hospital.

OBJECTIVE: The objective of this study was to compare ultrasound features and establish a predictive nomogram for distinguishing between triple-negative breast cancer (TNBC) and non-TNBC. DESIGN: A retrospective cohort study. SETTING: This study was conducted at Quanzhou First Hospital, a grade A tertiary hospital in Quanzhou, China, with the research data set covering the period from September 2019 to August 2023. PARTICIPANTS: The study included a total of 205 female patients with confirmed TNBC and 574 female patients with non-TNBC, who were randomly divided into a training set and a validation set at a ratio of 7:3. MAIN OUTCOME MEASURES: All patients underwent ultrasound examination and received a confirmatory pathological diagnosis. Nodules were classified according to the Breast Imaging-Reporting and Data System standard. Subsequently, the study conducted a comparative analysis of clinical characteristics and ultrasonic features. RESULTS: A statistically significant difference was observed in multiple clinical and ultrasonic features between TNBC and non-TNBC. Specifically, in the logistic regression analysis conducted on the training set, indicators such as posterior echo, lesion size, presence of clinical symptoms, margin characteristics, internal blood flow signals, halo and microcalcification were found to be statistically significant (p<0.05). These significant indicators were then effectively incorporated into a static and dynamic nomogram model, demonstrating high predictive performance in distinguishing TNBC from non-TNBC. CONCLUSION: The results of our study demonstrated that ultrasound features can be valuable in distinguishing between TNBC and non-TNBC. The presence of posterior echo, size, clinical symptoms, margin, internal flow, halo and microcalcification was identified as predictive factors for this differentiation. Microcalcification, hyperechoic halo, internal flow and clinical symptoms emerged as the strongest predictive factors, indicating their potential as reliable indicators for identifying TNBC and non-TNBC.

Development, validation and visualization of a web-based nomogram for predicting risk of new-onset diabetes after percutaneous coronary intervention.

Simple and practical tools for screening high-risk new-onset diabetes after percutaneous coronary intervention (PCI) (NODAP) are urgently needed to improve post-PCI prognosis. We aimed to evaluate the risk factors for NODAP and develop an online prediction tool using conventional variables based on a multicenter database. China evidence-based Chinese medicine database consisted of 249, 987 patients from 4 hospitals in mainland China. Patients ≥ 18 years with implanted coronary stents for acute coronary syndromes and did not have diabetes before PCI were enrolled in this study. According to the occurrence of new-onset diabetes mellitus after PCI, the patients were divided into NODAP and Non-NODAP. After least absolute shrinkage and selection operator regression and logistic regression, the model features were selected and then the nomogram was developed and plotted. Model performance was evaluated by the receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test and decision curve analysis. The nomogram was also externally validated at a different hospital. Subsequently, we developed an online visualization tool and a corresponding risk stratification system to predict the risk of developing NODAP after PCI based on the model. A total of 2698 patients after PCI (1255 NODAP and 1443 non-NODAP) were included in the final analysis based on the multicenter database. Five predictors were identified after screening: fasting plasma glucose, low-density lipoprotein cholesterol, hypertension, family history of diabetes and use of diuretics. And then we developed a web-based nomogram ( https://mr.cscps.com.cn/wscoringtool/index.html ) incorporating the above conventional factors for predicting patients at high risk for NODAP. The nomogram showed good discrimination, calibration and clinical utility and could accurately stratify patients into different NODAP risks. We developed a simple and practical web-based nomogram based on multicenter database to screen for NODAP risk, which can assist clinicians in accurately identifying patients at high risk of NODAP and developing post-PCI management strategies to improved patient prognosis.

Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy.

BACKGROUND: To integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT). METHODS: Total of 143 EC patients in the authors' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated. RESULTS: Models with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively. CONCLUSION: CT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT.

Integrated analysis of the role of PR/SET domain 14 in gastric cancer.

BACKGROUND: Gastric cancer is one of the most common tumors worldwide, and most patients are deprived of treatment options when diagnosed at advanced stages. PRDM14 has carcinogenic potential in breast and non-small cell lung cancer. however, its role in gastric cancer has not been elucidated. METHODS: We aimed to elucidate the expression of PRDM14 using pan-cancer analysis. We monitored the expression of PRDM14 in cells and patients using quantitative polymerase chain reaction, western blotting, and immunohistochemistry. We observed that cell phenotypes and regulatory genes were influenced by PRDM14 by silencing PRDM14. We evaluated and validated the value of the PRDM14-derived prognostic model. Finally, we predicted the relationship between PRDM14 and small-molecule drug responses using the Connectivity Map and The Genomics of Drug Sensitivity in Cancer databases. RESULTS: PRDM14 was significantly overexpressed in gastric cancer, which identified in cell lines and patients' tissues. Silencing the expression of PRDM14 resulted in apoptosis promotion, cell cycle arrest, and inhibition of the growth and migration of GC cells. Functional analysis revealed that PRDM14 acts in epigenetic regulation and modulates multiple DNA methyltransferases or transcription factors. The PRDM14-derived differentially expressed gene prognostic model was validated to reliably predict the patient prognosis. Nomograms (age, sex, and PRDM14-risk score) were used to quantify the probability of survival. PRDM14 was positively correlated with sensitivity to small-molecule drugs such as TPCA-1, PF-56,227, mirin, and linsitinib. CONCLUSIONS: Collectively, our findings suggest that PRDM14 is a positive regulator of gastric cancer progression. Therefore, it may be a potential therapeutic target for gastric cancer.

Dual-Energy Computed Tomography Parameters Combined With Inflammatory Indicators Predict Cervical Lymph Node Metastasis in Papillary Thyroid Cancer.

BACKGROUND AND OBJECTIVE: Cervical lymph node metastasis (CLNM) is considered a marker of papillar Fethicy thyroid cancer (PTC) progression and has a potential impact on the prognosis of PTC. The purpose of this study was to screen for predictors of CLNM in PTC and to construct a predictive model to guide the surgical approach in patients with PTC. METHODS: This is a retrospective study. Preoperative dual-energy computed tomography images of 114 patients with pathologically confirmed PTC between July 2019 and April 2023 were retrospectively analyzed. The dual-energy computed tomography parameters [iodine concentration (IC), normalized iodine concentration (NIC), the slope of energy spectrum curve (λHU)] of the venous stage cancer foci were measured and calculated. The independent influencing factors for predicting CLNM were determined by univariate and multivariate logistic regression analysis, and the prediction models were constructed. The clinical benefits of the model were evaluated using decision curves, calibration curves, and receiver operating characteristic curves. RESULTS: The statistical results show that NIC, derived neutrophil-to-lymphocyte ratio (dNLR), prognostic nutritional index (PNI), gender, and tumor diameter were independent predictors of CLNM in PTC. The AUC of the nomogram was .898 (95% CI: .829-.966), and the calibration curve and decision curve showed that the prediction model had good predictive effect and clinical benefit, respectively. CONCLUSION: The nomogram constructed based on dual-energy CT parameters and inflammatory prognostic indicators has high clinical value in predicting CLNM in PTC patients.

Comparison of clinical and MRI features of brain metastases between ALK+ and ALK- NSCLC.

BACKGROUND: Non-small-cell lung cancer (NSCLC) is the primary cause of brain metastases (BM). This study aimed to investigate differences in clinical and magnetic resonance imaging (MRI) features of BM between anaplastic lymphoma kinase (ALK) gene fusion (ALK+) and ALK wild-type (ALK-) NSCLC, and to preliminarily assess the efficacy of radiotherapy for treating BM. METHODS: A retrospective analysis included 101 epidermal growth factor receptor (EGFR)- NSCLC patients with BM: 41 with ALK gene fusion and 60 being ALK-. The brain MRI and clinical features were compared between different ALK status using the multivariate analysis, and a nomogram was constructed to predict ALK gene fusion. Fifty-six patients who did not undergo cerebral surgery and had complete pre- and post- treatment data were further divided based on whether they received radiotherapy. Log-rank test was used to compare the short-term effect of treatment between the two groups under different genotypes. RESULTS: ALK+ BM exhibited decreased peritumoral brain edema size, lower peritumoral brain edema index (PBEI), and a more homogeneous contrast enhancement pattern compared to ALK- BM. Age (OR = 1.04; 95%CI: 1.02-1.06), time to BM (OR = 1.50; 95% CI: 1.04-2.14), PBEI (OR = 1.26; 95% CI: 0.97-1.62), smoking status (smoking index >400 vs. non-smoking status: OR = 1.42; 95% CI: 0.99-2.04) and contrast enhancement pattern (OR = 1.89; 95% CI: 1.28-2.78) were associated with ALK gene fusion. A nomogram based on these variables demonstrated acceptable predictive efficiency (AUC = 0.844). In the ALK+ group, patients who received radiotherapy did not show increased disease control rate (DCR) or progression-free survival (PFS). In contrast, in the ALK- group, those who received radiotherapy had improved objective response rate (ORR), DCR, and PFS compared to those who were only treated with systemic therapy. CONCLUSIONS: The clinical and MRI features of BM can indicate the status of ALK in NSCLC. In the ALK- group, patients who received radiotherapy showed higher ORR, DCR, and PFS compared to those who did not.