Factors predicting the return of spontaneous circulation rate of cardiopulmonary resuscitation in China: Development and evaluation of predictive nomogram.

Background: This study aimed to construct and internally validate a probability of the return of spontaneous circulation (ROSC) rate nomogram in a Chinese population of patients with cardiac arrest (CA). Methods: Patients with CA receiving standard cardiopulmonary resuscitation (CPR) were studied retrospectively. The minor absolute shrinkage and selection operator (LASSO) regression analysis and multivariable logistic regression evaluated various demographic and clinicopathological characteristics. A predictive nomogram was constructed and evaluated for accuracy and reliability using C-index, the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis (DCA). Results: A cohort of 508 patients who had experienced CA and received standard CPR was randomly divided into training (70 %, n = 356) and validation groups (30 %, n = 152) for the study. LASSO regression analysis and multivariable logistic regression revealed that thirteen variables, such as age, CPR start time, Electric defibrillation, Epinephrine, Sodium bicarbonate (NaHCO3), CPR Compression duration, The postoperative prothrombin (PT) time, Lactate (Lac), Cardiac troponin (cTn), Potassium (K+), D-dimer, Hypertension (HBP), and Diabetes mellitus (DM), were found to be independent predictors of the ROSC rate of CPR. The nomogram model showed exceptional discrimination, with a C-index of 0.933 (95 % confidence interval: 0.882-0.984). Even in the internal validation, a remarkable C-index value of 0.926 (95 % confidence interval: 0.875-0.977) was still obtained. The accuracy and reliability of the model were also verified by the AUC of 0.923 in the training group and 0.926 in the validation group. The calibration curve showed the model agreed with the actual results. DCA suggested that the predictive nomogram had clinical utility. Conclusions: A predictive nomogram model was successfully established and proved to identify the influencing factors of the ROSC rate in patients with CA. During cardiopulmonary resuscitation, adjusting the emergency treatment based on the influence factors on ROSC rate is suggested to improve the treatment rate of patients with CA.

Predictive value of peri-chemotherapy hematological parameters for febrile neutropenia in patients with cancer.

Objective: The aim of this study was to compare hematological parameters pre- and early post-chemotherapy, and evaluate their values for predicting febrile neutropenia (FN). Methods: Patients diagnosed with malignant solid tumors receiving chemotherapy were included. Blood cell counts peri-chemotherapy and clinical information were retrieved from the hospital information system. We used the least absolute shrinkage and selection operator (LASSO) method for variable selection and fitted selected variables to a logistic model. We assessed the performance of the prediction model by the area under the ROC curve. Results: The study population consisted of 4,130 patients with common solid tumors receiving a three-week chemotherapy regimen in Sichuan Cancer Hospital from February 2019 to March 2022. In the FN group, change percentage of neutrophil count decreased less (-0.02, CI: -0.88 to 3.48 vs. -0.04, CI: -0.83 to 2.24). Among hematological parameters, lower post-chemotherapy lymphocyte count (OR 0.942, CI: 0.934-0.949), change percentage of platelet (OR 0.965, CI: 0.955-0.975) and higher change percentage of post-chemotherapy neutrophil count (OR 1.015, CI: 1.011-1.018), and pre-chemotherapy NLR (OR 1.002, CI: 1.002-1.002) predicted an increased risk of FN. These factors improved the predicting model based on clinical factors alone. The AUC of the combination model was 0.8275. Conclusion: Peri-chemotherapy hematological markers improve the prediction of FN.

Establishing a nomogram for predicting necrotizing enterocolitis in premature infants: A retrospective multicenter cohort study.

BACKGROUND: To construct a nomogram for predicting necrotizing enterocolitis (NEC) in preterm infants. METHODS: A total of 4,724 preterm infants who were admitted into 8 hospitals between April 2019 and September 2020 were initially enrolled this retrospective multicenter cohort study. Finally, 1,092 eligible cases were divided into training set and test set based on a 7:3 ratio. A univariate logistic regression analysis was performed to compare the variables between the two groups. Stepwise backward regression, LASSO regression, and Boruta feature selection were utilized in the multivariate analysis to identify independent risk factors. Then a nomogram model was constructed based on the identified risk factors. RESULTS: Risk factors for NEC included gestational diabetes mellitus, gestational age, small for gestational age, patent ductus arteriosus, septicemia, red blood cell transfusion, intravenous immunoglobulin, severe feeding intolerance, and absence of breastfeeding. The nomogram model developed based on these factors showed well discriminative ability. Calibration and decision curve analysis curves confirmed the good consistency and clinical utility of the model. CONCLUSIONS: We developed a nomogram model with strong discriminative ability, consistency, and clinical utility for predicting NEC. This model could be valuable for the early prediction of preterm infants at risk of developing NEC.

[Risk Factors and a Prediction Model for Frequent Acute Exacerbations of Chronic Obstructive Pulmonary Disease].

Objective To identify the risk factors of patients with frequent acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and construct a prediction model based on the clinical data,providing a theoretical basis for the clinical prevention and treatment. Methods A total of 25 638 COPD patients admitted to the Department of Respiratory and Critical Care Medicine,the Third People's Hospital of Chengdu from January 1,2013 to May 1,2023 were selected.Among them,11 315 patients were included according to the inclusion and exclusion criteria,and their clinical characteristics were analyzed.Multivariate Logistic regression was carried out to identify the risk factors for frequent AECOPD.A nomogram model was utilized to quantify the risk of acute exacerbation,and the performance of the prediction model was assessed based on the area under the receiver operating characteristic (ROC) curve. Results In the patients with frequent AECOPD,male percentage (P<0.001),age (P<0.001),urban residence (P<0.001),smoking (P<0.001),length of stay (P<0.001),total cost (P<0.001),antibiotic cost (P<0.001),diabetes (P=0.003),respiratory failure (P<0.001),heart disease (P<0.001),application of systemic glucocorticoids (P<0.001),white blood cell count (P<0.001),neutrophil percentage (P<0.001),C-reactive protein (P<0.001),total cholesterol (P<0.001),and brain natriuretic peptide (BNP) (P<0.001) were all higher than those in the patients with infrequent AECOPD.Multivariate Logistic regression analysis revealed that age,urban residence,smoking,diabetes,heart disease,Pseudomonas aeruginosa infection,application of systemic glucocorticoids,antibiotics,respiratory failure,and elevated white blood cell count,total cholesterol,and BNP were independent risk factors for hospitalization due to frequent AECOPD.A nomogram model of hospitalization due to frequent AECOPD was constructed according to risk factors.The ROC curve was established to evaluate the performance of the model,which showed the area under the ROC curve of 0.899 (95%CI=0.892-0.905),the sensitivity of 85.30%,and the specificity of 79.80%. Conclusion Frequent AECOPD is associated with smoking,heart disease,application of systemic glucocorticoids,Pseudomonas aeruginosa infection,age,low body mass index,and elevated BNP.Predicting the risks of hospitalization due to frequent AECOPD by the established model can provide theoretical support for the treatment and risk factor management of the patients.

Bi-phase CT radiomics nomogram for the preoperative prediction of pylorus lymph node metastasis in non-pyloric gastric cancer patients.

BACKGROUND: The expansion of function-preserving surgery became possible due to a more profound understanding of gastric cancer (GC), and T1N + or T2N + gastric cancer patients might be potential beneficiaries. However, ways to evaluate the possibility of function-preserving pylorus surgery are still unknown. METHODS: A total of 288 patients at Renji Hospital and 58 patients at Huadong Hospital, pathologically diagnosed with gastric cancer staging at T1 and T2 with tumors located in the upper two-thirds of the stomach, were retrospectively enrolled from March 2015 to October 2022. Tumor regions of interest (ROIs) were manually delineated on bi-phase CT images, and a nomogram was built and evaluated. RESULTS: The radiomic features distributed differently between positive and negative pLNm groups. Two radiomic signatures (RS1 and RS2) and one clinical signature were constructed. The radiomic signatures exhibited good performance for discriminating pLNm status in the test set. The three signatures were then combined into an integrated nomogram (IN). The IN showed good discrimination of pLNm in the Renji cohort (AUC 0.918) and the Huadong cohort (AUC 0.649). The verification models showed high values. CONCLUSION: For GC patients with T1 and T2 tumors located in the upper two-thirds of the stomach, a nomogram was successfully built for predicting pylorus lymph node metastasis, which would guide the surgical indication extension of conservative gastrectomies.

Application of random survival forest to establish a nomogram combining clinlabomics-score and clinical data for predicting brain metastasis in primary lung cancer.

PURPOSE: To establish a nomogram for predicting brain metastasis (BM) in primary lung cancer at 12, 18, and 24 months after initial diagnosis. METHODS: In this study, we included 428 patients who were diagnosed with primary lung cancer at Harbin Medical University Cancer Hospital between January 2020 and January 2022. The endpoint event was BM. The patients were randomly categorized into two groups in a 7:3 ratio: training (n = 299) and validation (n = 129) sets. Least absolute shrinkage and selection operator was utilized to analyze the laboratory test results in the training set. Furthermore, clinlabomics-score was determined using regression coefficients. Then, clinlabomics-score was combined with clinical data to construct a nomogram using random survival forest (RSF) and Cox multivariate regression. Then, various methods were used to evaluate the performance of the nomogram. RESULTS: Five independent predictive factors (pathological type, diameter, lymph node metastasis, non-lymph node metastasis and clinlabomics-score) were used to construct the nomogram. In the validation set, the bootstrap C-index was 0.7672 (95% CI 0.7092-0.8037), 12-month AUC was 0.787 (95% CI 0.708-0.865), 18-month AUC was 0.809 (95% CI 0.735-0.884), and 24-month AUC was 0.858 (95% CI 0.792-0.924). In addition, the calibration curve, decision curve analysis and Kaplan-Meier curves revealed a good performance of the nomogram. CONCLUSIONS: Finally, we constructed and validated a nomogram to predict BM risk in primary lung cancer. Our nomogram can identify patients at high risk of BM and provide a reference for clinical decision-making at different disease time points.

Radiomics nomogram for predicting chemo-immunotherapy efficiency in advanced non-small cell lung cancer.

This study aimed to explore potential radiomics biomarkers in predicting the efficiency of chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC). Eligible patients were prospectively assigned to receive chemo-immunotherapy, and were divided into a primary cohort (n = 138) and an internal validation cohort (n = 58). Additionally, a separative dataset was used as an external validation cohort (n = 60). Radiomics signatures were extracted and selected from the primary tumor sites from chest CT images. A multivariate logistic regression analysis was conducted to identify the independent clinical predictors. Subsequently, a radiomics nomogram model for predicting the efficiency of chemo-immunotherapy was conducted by integrating the selected radiomics signatures and the independent clinical predictors. The receiver operating characteristic (ROC) curves demonstrated that the radiomics model, the clinical model, and the radiomics nomogram model achieved areas under the curve (AUCs) of 0.85 (95% confidence interval [CI] 0.78-0.92), 0.76 (95% CI 0.68-0.84), and 0.89 (95% CI 0.84-0.94), respectively, in the primary cohort. In the internal validation cohort, the corresponding AUCs were 0.93 (95% CI 0.86-1.00), 0.79 (95% CI 0.68-0.91), and 0.96 (95% CI 0.90-1.00) respectively. Moreover, in the external validation cohort, the AUCs were 0.84 (95% CI 0.72-0.96), 0.75 (95% CI 0.62-0.87), and 0.86 (95% CI 0.75-0.96), respectively. In conclusion, the radiomics nomogram provides a convenient model for predicting the effect of chemo-immunotherapy in advanced NSCLC patients.

A nomogram for predicting the risk of temporomandibular disorders in university students.

OBJECTIVES: Temporomandibular disorders (TMDs) have a relatively high prevalence among university students. This study aimed to identify independent risk factors for TMD in university students and develop an effective risk prediction model. METHODS: This study included 1,122 university students from four universities in Changchun City, Jilin Province, as subjects. Predictive factors were screened by using the least absolute shrinkage and selection operator (LASSO) regression and the machine learning Boruta algorithm in the training cohort. A multifactorial logistic regression analysis was used to construct a TMD risk prediction model. Internal validation of the model was conducted via bootstrap resampling, and an external validation cohort comprised 205 university students undergoing oral examinations at the Stomatological Hospital of Jilin University. RESULTS: The prevalence of TMD among university students was 44.30%. Ten predictive factors were included in the model, comprising gender, facial cold stimulation, unilateral chewing, biting hard or resilient foods, clenching teeth, grinding teeth, excessive mouth opening, malocclusion, stress, and anxiety. The model demonstrated good predictive ability with area under the receiver operating characteristic curve (AUC) values of 0.853, 0.838, and 0.821 in the training cohort, internal validation cohort, and external validation cohort, respectively. The calibration curves demonstrated that the predicted results were consistent with the actual results, and the decision curve analysis (DCA) indicated the model's high clinical utility. CONCLUSIONS: An online nomogram of TMD in university students with good predictive performance was constructed, which can effectively predict the risk of TMD in university students. The model provides a useful tool for the early identification and treatment of TMDs in university students, helping clinicians to predict the probability of TMDs in each patient, thus providing more personalized and accurate treatment decisions for patients.

Risk stratification models incorporating oxidative stress factors to predict survival and recurrence in patients with gastric cancer after radical gastrectomy: A real-world multicenter study.

BACKGROUND: Oxidative stress significantly influences the development and progression of gastric cancer (GC). It remains unreported whether incorporating oxidative stress factors into nomograms can improve the predictive accuracy for survival and recurrence risk in GC patients. METHODS: 3498 GC patients who underwent radical gastrectomy between 2009 and 2017 were enrolled and randomly divided into training cohort (TC) and internal validation cohort (IVC). Cox regression analysis model was used to evaluate six preoperative oxidative stress indicators to formulate the Systemic oxidative stress Score (SOSS). Two nomograms based on SOSS was constructed by multivariate Cox regression and validated using 322 patients from another two hospitals. RESULTS: A total of 3820 patients were included. The SOSS, composed of three preoperative indicators-fibrinogen, albumin, and cholesterol-was an independent prognostic factor for both overall survival (OS) and disease-free survival (DFS). The two nomograms based on SOSS showed a significantly higher AUC than the pTNM stage (OS: 0.830 vs. 0.778, DFS: 0.824 vs. 0.775, all P < 0.001) and were validated in the IVC and EVC (all P < 0.001). The local recurrence rate, peritoneal recurrence rate, distant recurrence rate and multiple recurrence rate in high-risk group were significantly higher than those in low-risk group (P < 0.05). CONCLUSIONS: The two novel nomograms based on SOSS which was a combination score of three preoperative blood indicators, demonstrated outstanding predictive abilities for both survival and recurrence in GC patients with different risk groups, which may potentially improve survival through perioperatively active intervention strategies and individualized postoperatively close surveillance.

A novel nomogram based on the number of positive lymph nodes can predict the overall survival of patients with pancreatic head cancer after radical surgery.

BACKGROUND: This study aimed to construct a novel nomogram based on the number of positive lymph nodes to predict the overall survival of patients with pancreatic head cancer after radical surgery. MATERIALS AND METHODS: 2271 and 973 patients in the SEER Database were included in the development set and validation set, respectively. The primary clinical endpoint was OS (overall survival). Univariate and multivariate Cox regression analyses were used to screen independent risk factors of OS, and then independent risk factors were used to construct a novel nomogram. The C-index, calibration curves, and decision analysis curves were used to evaluate the predictive power of the nomogram in the development and validation sets. RESULTS: After multivariate Cox regression analysis, the independent risk factors for OS included age, tumor extent, chemotherapy, tumor size, LN (lymph nodes) examined, and LN positive. A nomogram was constructed by using independent risk factors for OS. The C-index of the nomogram for OS was 0.652 [(95% confidence interval (CI): 0.639-0.666)] and 0.661 (95%CI: 0.641-0.680) in the development and validation sets, respectively. The calibration curves and decision analysis curves proved that the nomogram had good predictive ability. CONCLUSIONS: The nomogram based on the number of positive LN can effectively predict the overall survival of patients with pancreatic head cancer after surgery.

Development and validation of nomograms for predicting cardiovascular disease risk in patients with prediabetes and diabetes.

This study aimed to develop and validate distinct nomogram models for assessing CVD risk in individuals with prediabetes and diabetes. In a cross-sectional study design, we examined data from 2294 prediabetes and 1037 diabetics who participated in the National Health and Nutrition Examination Survey, which was conducted in the United States of America between 2007 and 2018. The dataset was randomly divided into training and validation cohorts at a ratio of 0.75-0.25. The Boruta feature selection method was used in the training cohort to identify optimal predictors for CVD diagnosis. A web-based dynamic nomogram was developed using the selected features, which were validated in the validation cohort. The Hosmer-Lemeshow test was performed to assess the nomogram's stability and performance. Receiver operating characteristics and calibration curves were used to assess the effectiveness of the nomogram. The clinical applicability of the nomogram was evaluated using decision curve analysis and clinical impact curves. In the prediabetes cohort, the CVD risk prediction nomogram included nine risk factors: age, smoking status, platelet/lymphocyte ratio, platelet count, white blood cell count, red cell distribution width, lactate dehydrogenase level, sleep disorder, and hypertension. In the diabetes cohort, the CVD risk prediction nomogram included eleven risk factors: age, material status, smoking status, systemic inflammatory response index, neutrophil-to-lymphocyte ratio, red cell distribution width, lactate dehydrogenase, high-density lipoprotein cholesterol, sleep disorder, hypertension, and physical activity. The nomogram models developed in this study have good predictive and discriminant utility for predicting CVD risk in patients with prediabetes and diabetes.

Leveraging diverse cellular stress patterns for predicting clinical outcomes and therapeutic responses in patients with multiple myeloma.

Tumour microenvironment harbours diverse stress factors that affect the progression of multiple myeloma (MM), and the survival of MM cells heavily relies on crucial stress pathways. However, the impact of cellular stress on clinical prognosis of MM patients remains largely unknown. This study aimed to provide a cell stress-related model for survival and treatment prediction in MM. We incorporated five cell stress patterns including heat, oxidative, hypoxic, genotoxic, and endoplasmic reticulum stresses, to develop a comprehensive cellular stress index (CSI). Then we systematically analysed the effects of CSI on survival outcomes, clinical characteristics, immune microenvironment, and treatment sensitivity in MM. Molecular subtypes were identified using consensus clustering analysis based on CSI gene profiles. Moreover, a prognostic nomogram incorporating CSI was constructed and validated to aid in personalised risk stratification. After screening from five stress models, a CSI signature containing nine genes was established by Cox regression analyses and validated in three independent datasets. High CSI was significantly correlated with cell division pathways and poor clinical prognosis. Two distinct MM subtypes were identified through unsupervised clustering, showing significant differences in prognostic outcomes. The nomogram that combined CSI with clinical features exhibited good predictive performances in both training and validation cohorts. Meanwhile, CSI was closely associated with immune cell infiltration level and immune checkpoint gene expression. Therapeutically, patients with high CSI were more sensitive to bortezomib and antimitotic agents, while their response to immunotherapy was less favourable. Furthermore, in vitro experiments using cell lines and clinical samples verified the expression and function of key genes from CSI. The CSI signature could be a clinically applicable indicator of disease evaluation, demonstrating potential in predicting prognosis and guiding therapy for patients with MM.

Development of a nomogram for fetal Evans Index.

PURPOSE: This study aims to investigate the fetal Evans Index and establish a nomogram for fetuses without any additional fetal anomalies detected during the prenatal period. METHODS: We conducted our research at Ankara City Hospital, including 894 patients who were admitted and evaluated between gestational weeks 16-40. These patients had no fetal anomalies detected in subsequent gestational weeks. Descriptive data, such as age, gravidity, parity, and body mass index (BMI), were recorded. Gestational week and Evans Index (mean, median, standard deviation, minimum, maximum, and percentile) were also documented. The Evans index was calculated as the ratio between the maximal width of the frontal horns and the maximal width of the inner diameter of the cranium. RESULTS: We evaluated 894 fetuses in pregnant women had no fetal anomalies detected throughout the pregnancy. The evaluation took place at different gestational weeks, and a nomogram for the Evans Index was created. CONCLUSIONS: It is relevant for clinicians and researchers to be aware of the range of fetal Evans Index values across different gestational weeks as a prognostic criterion.

A prognostic nomogram for patients with HR+ mucinous breast carcinoma based on the SEER database and a Chinese cohort study.

Purpose: The study aimed to develop a nomogram model for individual prognosis prediction in patients with hormone receptors positive (HR+) mucinous breast carcinoma (MBC) and assess the value of neoadjuvant chemotherapy (NAC) in this context. Methods: A total of 6,850 HR+ MBC patients from the SEER database were identified and randomly (in a 7:3 ratio) divided into training cohorts and internal validation cohorts. 77 patients were enrolled from the Chongqing University Cancer Hospital as the external validation cohort. Independent risk factors affecting overall survival (OS) were selected using univariate and multivariate Cox regression analysis, and nomogram models were constructed and validated. A propensity score matching (PSM) approach was used in the exploration of the value of NAC versus adjuvant chemocherapy (AC) for long-term prognosis in HR+ MBC patients. Results: Multivariate Cox regression analysis showed 8 independent prognostic factors: age, race, marital status, tumor size, distant metastasis, surgery, radiotherapy, and chemotherapy. The constructed nomogram model based on these 8 factors exhibited good consistency and accuracy. In the training group, internal validation group and external validation group, the high-risk groups demonstrated worse OS (p<0.0001). Subgroup analysis revealed that NAC had no impact on OS (p = 0.18), or cancer specific survival (CSS) (p = 0.26) compared with AC after PSM. Conclusions: The established nomogram model provides an accurate prognostic prediction for HR+ MBC patients. NAC does not confer long-term survival benefits compared to AC. These findings provide a novel approach for prognostic prediction and clinical practice.

Development and Validation of a Nomogram Model for Accurately Predicting Depression in Maintenance Hemodialysis Patients: A Multicenter Cross-Sectional Study in China.

Purpose: Depression is a major concern in maintenance hemodialysis. However, given the elusive nature of its risk factors and the redundant nature of existing assessment forms for judging depression, further research is necessary. Therefore, this study was devoted to exploring the risk factors for depression in maintenance hemodialysis patients and to developing and validating a predictive model for assessing depression in maintenance hemodialysis patients. Patients and Methods: This cross-sectional study was conducted from May 2022 to December 2022, and we recruited maintenance hemodialysis patients from a multicentre hemodialysis centre. Risk factors were identified by Lasso regression analysis and a Nomogram model was developed to predict depressed patients on maintenance hemodialysis. The predictive accuracy of the model was assessed by ROC curves, area under the ROC (AUC), consistency index (C-index), and calibration curves, and its applicability in clinical practice was evaluated using decision curves (DCA). Results: A total of 175 maintenance hemodialysis patients were included in this study, and cases were randomised into a training set of 148 and a validation set of 27 (split ratio 8.5:1.5), with a depression prevalence of 29.1%. Based on age, employment, albumin, and blood uric acid, a predictive map of depression was created, and in the training set, the nomogram had an AUC of 0.7918, a sensitivity of 61.9%, and a specificity of 89.2%. In the validation set, the nomogram had an AUC of 0.810, a sensitivity of 100%, and a specificity of 61.1%. The bootstrap-based internal validation showed a c-index of 0.792, while the calibration curve showed a strong correlation between actual and predicted depression risk. Decision curve analysis (DCA) results indicated that the predictive model was clinically useful. Conclusion: The nomogram constructed in this study can be used to identify depression conditions in vulnerable groups quickly, practically and reliably.

Integrating clinical and biochemical markers: a novel nomogram for predicting lacunes in cerebral small vessel disease.

Background: Lacunes, a characteristic feature of cerebral small vessel disease (CSVD), are critical public health concerns, especially in the aging population. Traditional neuroimaging techniques often fall short in early lacune detection, prompting the need for more precise predictive models. Methods: In this retrospective study, 587 patients from the Neurology Department of the Affiliated Hospital of Hebei University who underwent cranial MRI were assessed. A nomogram for predicting lacune incidence was developed using LASSO regression and binary logistic regression analysis for variable selection. The nomogram's performance was quantitatively assessed using AUC-ROC, calibration plots, and decision curve analysis (DCA) in both training (n = 412) and testing (n = 175) cohorts. Results: Independent predictors identified included age, gender, history of stroke, carotid atherosclerosis, hypertension, creatinine, and homocysteine levels. The nomogram showed an AUC-ROC of 0.814 (95% CI: 0.791-0.870) for the training set and 0.805 (95% CI: 0.782-0.843) for the testing set. Calibration and DCA corroborated the model's clinical value. Conclusion: This study introduces a clinically useful nomogram, derived from binary logistic regression, that significantly enhances the prediction of lacunes in patients undergoing brain MRI for various indications, potentially advancing early diagnosis and intervention. While promising, its retrospective design and single-center context are limitations that warrant further research, including multi-center validation.

A Prognostic Risk Signature of Two Autophagy-Related Genes for Predicting Triple-Negative Breast Cancer Outcomes.

Background: Triple-negative breast cancer (TNBC) is recognized as the most aggressive molecular subtype of breast cancer. Recent studies have highlighted the complex role of autophagy in the pathogenesis of TNBC. Methods: In this study, we evaluated 18,330 genes, including 1111 autophagy-related genes, (ARGs), across 579 TNBC samples from online databases. Differentially expressed ARGs in TNBC were identified using high-throughput RNA-seq data from the Cancer Genome Atlas (TCGA). Prognostic factors were examined through Cox regression and multivariate Cox analyses, with predictive efficacy assessed using receiver operating characteristic (ROC) curves. A nomogram integrating the risk signature with clinicopathological factors, such as TNM stage, was developed. Immunohistochemical analysis of clinical samples was also conducted. Results: EIF4EBP1 and NPAS3 were significantly correlated with prognostic outcomes in patients with TNBC. Multivariate Cox regression analysis demonstrated that the expression levels of these two genes were accurate predictors of disease progression in TNBC samples from TCGA and the GSE31519 dataset. The efficacy of this predictive model was validated using ROC curve analysis and calibration plots, confirming its ability to accurately estimate the 1-, 2-, and 3-year survival rates for individuals with TNBC. Additionally, EIF4EBP1 and NPAS3 expression influenced drug sensitivity in TNBC cell lines, with notably lower NPAS3 expression in TNBC tissues, particularly in Stage III cases. This study is the first to report NPAS3 expression in patients with TNBC. Conclusion: The autophagy-related genes EIF4EBP1 and NPAS3 may serve as independent prognostic factors for individuals with TNBC.

Ranking of Modifiable Lifestyle Risk Factors for Breast Cancer in Saudi Women: Population Attributable Risk and Nomogram.

Purpose: Breast cancer is the most common cancer among women in the Saudi Arabia, and over 50% of the cases are detected at a late stage. This study aimed to estimate population attributable risk percentage (PAR%) of modifiable lifestyle risk factors for breast cancer in Saudi Arabia. Patients and Methods: A secondary analysis of previously published papers was performed . Relative risks (RR) and odds ratios (OR) were obtained from published international epidemiological studies, and the prevalence of each risk factor in Saudi Arabia was obtained from various sources (eg, national surveys and published literature) to calculate PAR%. A nomogram was used to visually translate the RRs/ORs and their prevalence into PAR% using a practical tool. Results: Seven modifiable lifestyle risk factors for breast cancer were identified in Saudi Arabia. The identified risk factors included lack of physical activity (sedentary lifestyle), oral contraception (current use), obesity (postmenopausal), hormone replacement therapy (current use), passive smoking, age at first birth (≥ 35 years), and tobacco smoking (current or daily smoking). The PAR% for these risk factors ranged from 0.5% for tobacco smoking to 23.1% for a lack of physical activity. Few modifiable lifestyle risk factors were excluded from this study, due to limited nor unavailable data in Saudi Arabia (eg, alcohol consumption, breastfeeding patterns and childbearing patterns, obesity according to menopausal status, and night-shift work). Conclusion: Physical inactivity has the most significant modifiable health impact and is a major risk factor for breast cancer. Removing this risk factor would reduce the prevalence of breast cancer in the Saudi population by 23%. There is an immense need to prioritize cancer control strategies based on local needs, current data on cancer risk factors, and the disease burden.

Biological function and potential application of PANoptosis-related genes in colorectal carcinogenesis.

PANoptosis induces programmed cell death (PCD) through extensive crosstalk and is associated with development of cancer. However, the functional mechanisms, clinical significance, and potential applications of PANoptosis-related genes (PRGs) in colorectal cancer (CRC) have not been fully elucidated. Functional enrichment of key PRGs was analyzed based on databases, and relationships between key PRGs and the immune microenvironment, immune cell infiltration, chemotherapy drug sensitivity, tumor progression genes, single-cell cellular subgroups, signal transduction pathways, transcription factor regulation, and miRNA regulatory networks were systematically explored. This study identified 5 key PRGs associated with CRC: BCL10, CDKN2A, DAPK1, PYGM and TIMP1. Then, RT-PCR was used to verify expression of these genes in CRC cells and tissues. Clinical significance and prognostic value of key genes were further verified by multiple datasets. Analyses of the immune microenvironment, immune cell infiltration, chemotherapy drug sensitivity, tumor progression genes, single-cell cellular subgroups, and signal transduction pathways suggest a close relationship between these key genes and development of CRC. In addition, a novel prognostic nomogram model for CRC was successfully constructed by combining important clinical indicators and the key genes. In conclusion, our findings offer new insights for understanding the pathogenesis of CRC, predicting CRC prognosis, and identifying multiple therapeutic targets for future CRC therapy.

Radiomics of multi-parametric MRI for the prediction of lung metastasis in soft-tissue sarcoma: a feasibility study.

PURPOSE: To investigate the value of multi-parametric MRI-based radiomics for preoperative prediction of lung metastases from soft tissue sarcoma (STS). METHODS: In total, 122 patients with clinicopathologically confirmed STS who underwent pretreatment T1-weighted contrast-enhanced (T1-CE) and T2-weighted fat-suppressed (T2FS) MRI scans were enrolled between Jul. 2017 and Mar. 2021. Radiomics signatures were established by calculating and selecting radiomics features from the two sequences. Clinical independent predictors were evaluated by statistical analysis. The radiomics nomogram was constructed from margin and radiomics features by multivariable logistic regression. Finally, the study used receiver operating characteristic (ROC) and calibration curves to evaluate performance of radiomics models. Decision curve analyses (DCA) were performed to evaluate clinical usefulness of the models. RESULTS: The margin was considered as an independent predictor (p < 0.05). A total of 4 MRI features were selected and used to develop the radiomics signature. By incorporating the margin and radiomics signature, the developed nomogram showed the best prediction performance in the training (AUCs, margin vs. radiomics signature vs. nomogram, 0.609 vs. 0.909 vs. 0.910) and validation (AUCs, margin vs. radiomics signature vs. nomogram, 0.666 vs. 0.841 vs. 0.894) cohorts. DCA indicated potential usefulness of the nomogram model. CONCLUSIONS: This feasibility study evaluated predictive values of multi-parametric MRI for the prediction of lung metastasis, and proposed a nomogram model to potentially facilitate the individualized treatment decision-making for STSs.