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Hereby, we aimed to comprehensively compare different scoring systems for pediatric trauma and their ability to predict in-hospital mortality and intensive care unit (ICU) admission. The current registry-based multicenter study encompassed a comprehensive dataset of 6709 pediatric trauma patients aged ≤ 18 years from July 2016 to September 2023. To ascertain the predictive efficacy of the scoring systems, the area under the receiver operating characteristic curve (AUC) was calculated. A total of 720 individuals (10.7%) required admission to the ICU. The mortality rate was 1.1% (n = 72). The most predictive scoring system for in-hospital mortality was the adjusted trauma and injury severity score (aTRISS) (AUC = 0.982), followed by trauma and injury severity score (TRISS) (AUC = 0.980), new trauma and injury severity score (NTRISS) (AUC = 0.972), Glasgow coma scale (GCS) (AUC = 0.9546), revised trauma score (RTS) (AUC = 0.944), pre-hospital index (PHI) (AUC = 0.936), injury severity score (ISS) (AUC = 0.901), new injury severity score (NISS) (AUC = 0.900), and abbreviated injury scale (AIS) (AUC = 0.734). Given the predictive performance of the scoring systems for ICU admission, NTRISS had the highest predictive performance (AUC = 0.837), followed by aTRISS (AUC = 0.836), TRISS (AUC = 0.823), ISS (AUC = 0.807), NISS (AUC = 0.805), GCS (AUC = 0.735), RTS (AUC = 0.698), PHI (AUC = 0.662), and AIS (AUC = 0.651). In the present study, we concluded the superiority of the TRISS and its two derived counterparts, aTRISS and NTRISS, compared to other scoring systems, to efficiently discerning individuals who possess a heightened susceptibility to unfavorable consequences. The significance of these findings underscores the necessity of incorporating these metrics into the realm of clinical practice.

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BACKGROUND: Portal hypertension (PHT), primarily induced by cirrhosis, manifests severe symptoms impacting patient survival. Although transjugular intrahepatic portosystemic shunt (TIPS) is a critical intervention for managing PHT, it carries risks like hepatic encephalopathy, thus affecting patient survival prognosis. To our knowledge, existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes. Consequently, the development of an innovative modeling approach is essential to address this limitation. AIM: To develop and validate a Bayesian network (BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS. METHODS: The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed. Variables were selected using Cox and least absolute shrinkage and selection operator regression methods, and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT. RESULTS: Variable selection revealed the following as key factors impacting survival: age, ascites, hypertension, indications for TIPS, postoperative portal vein pressure (post-PVP), aspartate aminotransferase, alkaline phosphatase, total bilirubin, prealbumin, the Child-Pugh grade, and the model for end-stage liver disease (MELD) score. Based on the above-mentioned variables, a BN-based 2-year survival prognostic prediction model was constructed, which identified the following factors to be directly linked to the survival time: age, ascites, indications for TIPS, concurrent hypertension, post-PVP, the Child-Pugh grade, and the MELD score. The Bayesian information criterion was 3589.04, and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16. The model's accuracy, precision, recall, and F1 score were 0.90, 0.92, 0.97, and 0.95 respectively, with the area under the receiver operating characteristic curve being 0.72. CONCLUSION: This study successfully developed a BN-based survival prediction model with good predictive capabilities. It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.

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Certain glioma subtypes, such as glioblastoma multiforme or low-grade glioma, are common malignant intracranial tumors with high rates of relapse and malignant progression even after standard therapy. The overall survival (OS) is poor in patients with gliomas; hence, effective prognostic prediction is crucial. Herein, the present study aimed to explore the potential role of hypoxia-inducible factor 1 subunit alpha (HIF1α) in gliomas and investigate the association between HIF1α and infiltrating immune cells in gliomas. Data from The Cancer Genome Atlas were evaluated via RNA sequencing, clinicopathological, immunological checkpoint, immune infiltration and functional enrichment analyses. Validation of protein abundance was performed using paraffin-embedded samples from patients with glioma. A nomogram model was created to forecast the OS rates at 1, 3 and 5 years after cancer diagnosis. The association between OS and HIF1α expression was estimated using Kaplan-Meier survival analysis and the log-rank test. Finally, HIF1α expression was validated using western blotting, reverse transcription-quantitative PCR, Cell Counting Kit-8 and Transwell assays. The results demonstrated that HIF1α expression was significantly upregulated in gliomas compared with normal human brain glial cells. Immunohistochemistry staining demonstrated differential expression of the HIF1α protein. Moreover, glioma cell viability and migration were inhibited via HIF1α downregulation. HIF1α impacted DNA replication, cell cycling, DNA repair and the immune microenvironment in glioma. HIF1α expression was also positively associated with several types of immune cells and immunological checkpoints and with neutrophils, plasmacytoid dendritic cells and CD56bright cells. The Kaplan-Meier survival analyses further demonstrated a strong association between high HIF1α expression and poor prognosis in patients with glioma. Analysis of the receiver operating characteristic curves demonstrated that HIF1α expression accurately differentiated paired normal brain cells from tumor tissues. Collectively, these findings suggested the potential for HIF1α to be used as a novel prognostic indicator for patients with glioma and that OS prediction models may help in the future to develop effective follow-up and treatment strategies for these patients.

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RATIONALE AND OBJECTIVES: An accurate prognostic model is essential for the development of treatment strategies for gallbladder cancer (GBC). This study proposes an integrated model using clinical features, radiomics, and deep learning based on contrast-enhanced computed tomography (CT) images for survival prediction in patients with GBC after surgical resection. METHODS: A total of 167 patients with GBC who underwent surgical resection at two medical institutions were retrospectively enrolled. After obtaining the pre-treatment CT images, the tumor lesions were manually segmented, and handcrafted radiomics features were extracted. A clinical prognostic signature and radiomics signature were built using machine learning algorithms based on the optimal clinical features or handcrafted radiomics features, respectively. Subsequently, a DenseNet121 model was employed for transfer learning on the radiomics image data and as the basis for the deep learning signature. Finally, we used logistic regression on the three signatures to obtain the unified multimodal model for comprehensive interpretation and analysis. RESULTS: The integrated model performed better than the other models, exhibiting the highest area under the curve (AUC) of 0.870 in the test set, and the highest concordance index (C-index) of 0.736 in predicting patient survival rates. A Kaplan-Meier analysis demonstrated that patients in high-risk group had a lower survival probability compared to those in low-risk group (log-rank p < 0.05). CONCLUSION: The nomogram is useful for predicting the survival of patients with GBC after surgical resection, helping in the identification of high-risk patients with poor prognosis and ultimately facilitating individualized management of patients with GBC.

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In an aging society, maintaining healthy aging, preventing death, and enabling a continuation of economic activities are crucial. This study sought to develop a model for predicting survival times among community-dwelling older individuals using a deep learning method, and to identify the level of influence of various risk factors on the survival period, so that older individuals can manage their own health. This study used the Korean National Health Insurance Service claims data. We observed community-dwelling older people, aged 66 years, for 11 years and developed a survival time prediction model. Of the 189,697 individuals enrolled at baseline, 180,235 (95.0%) survived from 2009 to 2019, while 9462 (5.0%) died. Using deep-learning-based models (C statistics = 0.7011), we identified various factors impacting survival: Charlson's comorbidity index; the frailty index; long-term care benefit grade; disability grade; income level; a combination of diabetes mellitus, hypertension, and dyslipidemia; sex; smoking status; and alcohol consumption habits. In particular, Charlson's comorbidity index (SHAP value: 0.0445) and frailty index (SHAP value: 0.0443) were strong predictors of survival time. Prediction models may help researchers to identify potentially modifiable risk factors that may affect survival.

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To date, there is no clinically useful prediction model that is suitable for Japanese pediatric trauma patients. Herein, this study aimed to developed a model for predicting the survival of Japanese pediatric patients with blunt trauma and compare its validity with that of the conventional TRISS model. Patients registered in the Japan Trauma Data Bank were grouped into a derivation cohort (2009-2013) and validation cohort (2014-2018). Logistic regression analysis was performed using the derivation dataset to establish prediction models using age, injury severity, and physiology. The validity of the modified model was evaluated by the area under the receiver operating characteristic curve (AUC). Among 11 predictor models, Model 1 and Model 11 had the best performance (AUC = 0.980). The AUC of all models was lower in patients with survival probability Ps < 0.5 than in patients with Ps ≥ 0.5. The AUC of all models was lower in neonates/infants than in other age categories. Model 11 also had the best performance (AUC = 0.762 and 0.909, respectively) in patients with Ps < 0.5 and neonates/infants. The predictive ability of the newly modified models was not superior to that of the current TRISS model. Our results may be useful to develop a highly accurate prediction model based on the new predictive variables and cutoff values associated with the survival mortality of injured Japanese pediatric patients who are younger and more severely injured by using a nationwide dataset with fewer missing data and added valuables, which can be used to evaluate the age-related physiological and anatomical severity of injured patients.

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BACKGROUND: We aimed to establish an osteosarcoma prognosis prediction model based on a signature of endoplasmic reticulum stress-related genes. METHODS: Differentially expressed genes (DEGs) between osteosarcoma with and without metastasis from The Cancer Genome Atlas (TCGA) database were mapped to ERS genes retrieved from Gene Set Enrichment Analysis to select endoplasmic reticulum stress-related DEGs. Subsequently, we constructed a risk score model based on survival-related endoplasmic reticulum stress DEGs and a nomogram of independent survival prognostic factors. Based on the median risk score, we stratified the samples into high- and low-risk groups. The ability of the model was assessed by Kaplan-Meier, receiver operating characteristic curve, and functional analyses. Additionally, the expression of the identified prognostic endoplasmic reticulum stress-related DEGs was verified using real-time quantitative PCR (RT-qPCR). RESULTS: In total, 41 endoplasmic reticulum stress-related DEGs were identified in patients with osteosarcoma with metastasis. A risk score model consisting of six prognostic endoplasmic reticulum stress-related DEGs (ATP2A3, ERMP1, FBXO6, ITPR1, NFE2L2, and USP13) was established, and the Kaplan-Meier and receiver operating characteristic curves validated their performance in the training and validation datasets. Age, tumor metastasis, and the risk score model were demonstrated to be independent prognostic clinical factors for osteosarcoma and were used to establish a nomogram survival model. The nomogram model showed similar performance of one, three, and five year-survival rate to the actual survival rates. Nine immune cell types in the high-risk group were found to be significantly different from those in the low-risk group. These survival-related genes were significantly enriched in nine Kyoto Encyclopedia of Genes and Genomes pathways, including cell adhesion molecule cascades, and chemokine signaling pathways. Further, RT-qPCR results demonstrated that the consistency rate of bioinformatics analysis was approximately 83.33%, suggesting the relatively high reliability of the bioinformatics analysis. CONCLUSION: We established an osteosarcoma prediction model based on six prognostic endoplasmic reticulum stress-related DEGs that could be helpful in directing personalized treatment.

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BACKGROUND: The correlation and difference in T-cell phenotypes between peripheral blood lymphocytes (PBLs) and the tumor immune microenvironment (TIME) in patients with gastric cancer (GC) is not clear. We aimed to characterize the phenotypes of CD8+ T cells in tumor infiltrating lymphocytes (TILs) and PBLs in patients with different outcomes and to establish a useful survival prediction model. METHODS: Multiplex immunofluorescence staining and flow cytometry were used to detect the expression of inhibitory molecules (IMs) and active markers (AMs) in CD8+TILs and PBLs, respectively. The role of these parameters in the 3-year prognosis was assessed by receiver operating characteristic analysis. Then, we divided patients into two TIME clusters (TIME-A/B) and two PBL clusters (PBL-A/B) by unsupervised hierarchical clustering based on the results of multivariate analysis, and used the Kaplan-Meier method to analyze the difference in prognosis between each group. Finally, we constructed and compared three survival prediction models based on Cox regression analysis, and further validated the efficiency and accuracy in the internal and external cohorts. RESULTS: The percentage of PD-1+CD8+TILs, TIM-3+CD8+TILs, PD-L1+CD8+TILs, and PD-L1+CD8+PBLs and the density of PD-L1+CD8+TILs were independent risk factors, while the percentage of TIM-3+CD8+PBLs was an independent protective factor. The patients in the TIME-B group showed a worse 3-year overall survival (OS) (HR: 3.256, 95% CI 1.318-8.043, P = 0.006), with a higher density of PD-L1+CD8+TILs (P < 0.001) and percentage of PD-1+CD8+TILs (P = 0.017) and PD-L1+CD8+TILs (P < 0.001) compared to the TIME-A group. The patients in the PBL-B group showed higher positivity for PD-L1+CD8+PBLs (P = 0.042), LAG-3+CD8+PBLs (P < 0.001), TIM-3+CD8+PBLs (P = 0.003), PD-L1+CD4+PBLs (P = 0.001), and LAG-3+CD4+PBLs (P < 0.001) and poorer 3-year OS (HR: 0.124, 95% CI 0.017-0.929, P = 0.015) than those in the PBL-A group. In our three survival prediction models, Model 3, which was based on the percentage of TIM-3+CD8+PBLs, PD-L1+CD8+TILs and PD-1+CD8+TILs, showed the best sensitivity (0.950, 0.914), specificity (0.852, 0.857) and accuracy (κ = 0.787, P < 0.001; κ = 0.771, P < 0.001) in the internal and external cohorts, respectively. CONCLUSION: We established a comprehensive and robust survival prediction model based on the T-cell phenotype in the TIME and PBLs for GC prognosis.

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BACKGROUND: To develop a prediction model predicting in-hospital mortality of elder patients with community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU). METHODS: In this cohort study, data of 619 patients with CAP aged ≥ 65 years were obtained from the Medical Information Mart for Intensive Care III (MIMIC III) 2001-2012 database. To establish the robustness of predictor variables, the sample dataset was randomly partitioned into a training set group and a testing set group (ratio: 6.5:3.5). The predictive factors were evaluated using multivariable logistic regression, and then a prediction model was constructed. The prediction model was compared with the widely used assessments: Sequential Organ Failure Assessment (SOFA), Pneumonia Severity Index (PSI), systolic blood pressure, oxygenation, age and respiratory rate (SOAR), CURB-65 scores using positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), area under the curve (AUC) and 95% confidence interval (CI). The decision curve analysis (DCA) was used to assess the net benefit of the prediction model. Subgroup analysis based on the pathogen was developed. RESULTS: Among 402 patients in the training set, 90 (24.63%) elderly CAP patients suffered from 30-day in-hospital mortality, with the median follow-up being 8 days. Hemoglobin/platelets ratio, age, respiratory rate, international normalized ratio, ventilation use, vasopressor use, red cell distribution width/blood urea nitrogen ratio, and Glasgow coma scales were identified as the predictive factors that affect the 30-day in-hospital mortality. The AUC values of the prediction model, the SOFA, SOAR, PSI and CURB-65 scores, were 0.751 (95% CI 0.749-0.752), 0.672 (95% CI 0.670-0.674), 0.607 (95% CI 0.605-0.609), 0.538 (95% CI 0.536-0.540), and 0.645 (95% CI 0.643-0.646), respectively. DCA result demonstrated that the prediction model could provide greater clinical net benefits to CAP patients admitted to the ICU. Concerning the pathogen, the prediction model also reported better predictive performance. CONCLUSION: Our prediction model could predict the 30-day hospital mortality in elder patients with CAP and guide clinicians to identify the high-risk population.

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BACKGROUND: Predicting the survival of cancer patients provides prognostic information and therapeutic guidance. However, improved prediction models are needed for use in diagnosis and treatment. OBJECTIVE: This study aimed to identify genomic prognostic biomarkers related to colon cancer (CC) based on computational data and to develop survival prediction models. METHODS: We performed machine-learning (ML) analysis to screen pathogenic survival-related driver genes related to patient prognosis by integrating copy number variation and gene expression data. Moreover, in silico system analysis was performed to clinically assess data from ML analysis, and we identified RABGAP1L, MYH9, and DRD4 as candidate genes. These three genes and tumor stages were used to generate survival prediction models. Moreover, the genes were validated by experimental and clinical analyses, and the theranostic application of the survival prediction models was assessed. RESULTS: RABGAP1L, MYH9, and DRD4 were identified as survival-related candidate genes by ML and in silico system analysis. The survival prediction model using the expression of the three genes showed higher predictive performance when applied to predict the prognosis of CC patients. A series of functional analyses revealed that each knockdown of three genes reduced the protumor activity of CC cells. In particular, validation with an independent cohort of CC patients confirmed that the coexpression of MYH9 and DRD4 gene expression reflected poorer clinical outcomes in terms of overall survival and disease-free survival. CONCLUSIONS: Our survival prediction approach will contribute to providing information on patients and developing a therapeutic strategy for CC patients.

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It was a retrospective cohort study. Eighty maintenance hemodialysis (MHD) patients with corona virus disease 2019 (COVID-19) were enrolled, among whom 48 patients survived and 32 died. The clinical data between the survival and death groups were compared. The Cox regression model was used to analyze the risk factors of death in MHD patients with COVID-19, and a survival prediction model was constructed. The results showed that age, lesion-cumulative number of lung segments, C-reactive protein, procalcitonin, serum ferritin, interleukin-6, D-dimer, serum phosphorus, and proportions of males, diabetes and hypoxemia in the death group were higher than those in the survival group (all P<0.05). Increased age ( HR=1.039, 95% CI 1.007-1.072, P=0.017), diabetes ( HR=2.688, 95% CI 1.018-6.991, P=0.046), increased C-reactive protein ( HR=1.006, 95% CI 1.001-1.011, P=0.012), and increased serum phosphorus ( HR=1.573, 95% CI 1.015-2.438, P=0.043) were independent influencing factors of death in MHD patients with COVID-19. The survival prediction model was established based on age, diabetes, C-reactive protein and blood phosphorus. The area under the receiver operating characteristic curve of the combined model for survival time at 7-day, 14-day, and 21-day were 0.751 (95% CI 0.690-0.811), 0.768 (95% CI 0.712-0.824), and 0.780 (95% CI 0.729-0.831), respectively. The concordance index of cross- validation as internal validation was 0.797 (95% CI 0.757-0.837). Increased age, diabetes, elevated C-reactive protein and elevated blood phosphorus are independent risk factors of COVID-19 death in MHD patients, and the survival prediction model built by those factors has good efficacy.

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BACKGROUND: Gallbladder cancer is the sixth most common malignant gastrointestinal tumor. Radical surgery is currently the only effective treatment, but patient prognosis is poor, with a 5-year survival rate of only 5-10%. Establishing an effective survival prediction model for gallbladder cancer patients is crucial for disease status assessment, early intervention, and individualized treatment approaches. The existing gallbladder cancer survival prediction model uses clinical data-radiotherapy and chemotherapy, pathology, and surgical scope-but fails to utilize laboratory examination and imaging data, limiting its prediction accuracy and preventing sufficient treatment plan guidance. AIMS: The aim of this work is to propose an accurate survival prediction model, based on the deep learning 3D-DenseNet network, integrated with multimodal medical data (enhanced CT imaging, laboratory test results, and data regarding systemic treatments). METHODS: Data were collected from 195 gallbladder cancer patients at two large tertiary hospitals in Shanghai. The 3D-DenseNet network extracted deep imaging features and constructed prognostic factors, from which a multimodal survival prediction model was established, based on the Cox regression model and incorporating patients' laboratory test and systemic treatment data. RESULTS: The model had a C-index of 0.787 in predicting patients' survival rate. Moreover, the area under the curve (AUC) of predicting patients' 1-, 3-, and 5-year survival rates reached 0.827, 0.865, and 0.926, respectively. CONCLUSIONS: Compared with the monomodal model based on deep imaging features and the tumor-node-metastasis (TNM) staging system-widely used in clinical practice-our model's prediction accuracy was greatly improved, aiding the prognostic assessment of gallbladder cancer patients.

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Objective:To identify the independent risk factors affecting the prognosis of gallbladder cancer after radical resection, and to develop and validate the nomogram of predictive model.Methods:The clinical data of 147 patients with gallbladder cancer treated in the First Affiliated Hospital of Xinjiang Medical University from January 2012 to January 2022 were retrospectively analyzed, including 53 males and 94 females, aged (61.45±10.76) years old. The patients were followed up by outpatient or telephone review. The Kaplan-Meier method and log-rank test were used for survival analysis. The variables of P<0.1 in univariate analysis were included in the minimum absolute convergence and selection operator (LASSO) regression model, and the predictive factors affecting the prognosis of gallbladder cancer were screened. The predictive model was established by multivariate Cox regression analysis, and a nanogram was constructed based on the multivariate Cox regression model. The discrimination of the model was evaluated by consistency index (C index), time-dependent C index curve, receiver operating characteristic curve and area under the curve (AUC). 500 times of Bootstrap sampling were conducted for the calibration of nomogram. Results:The median survival of patients with gallbladder cancer was 22.15 months, and the 1-, 2- and 3-year cumulative survival rates were 65.99%, 46.02% and 35.73%, respectively. LASSO regression analysis showed that age, abdominal pain, degree of differentiation, T stage, N stage, serum levels of CA-199 and total bilirubin were predictive factors affecting the prognosis of gallbladder cancer (all P<0.05). The prognosis prediction model was established by multivariate Cox regression analysis. The C-index was 0.856 (95% CI: 0.823-0.887). The AUC values for 1-year and 3-year survival probabilities are 0.939 and 0.944, respectively. The calibration chart indicates that this model has a good accuracy. The decision curve analysis confirmed that the net benefit of this model is significantly higher than two extreme situations, indicating its clinical applicability and patients’ benefits. Conclusion:The nomogram for postoperative prognosis of gallbladder cancer based on age, abdominal pain, degree of differentiation, T stage, N stage, serum levels of total bilirubin and CA-199 has a high accuracy, which might affect the treatment decision-making of patients with gallbladder cancer.

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BACKGROUND: Among the most lethal cancers, pancreatic adenocarcinoma (PAAD) is an essential component of digestive system malignancies that still lacks effective diagnosis and treatment methods. As exosomes and competing endogenous RNA (ceRNA) regulatory networks in tumors go deeper, we expect to construct a ceRNA regulatory network derived from blood exosomes of PAAD patients by bioinformatics methods and develop a survival prediction model based on it. METHODS: Blood exosome sequencing data of PAAD patients and normal controls were downloaded from the exoRbase database, and the expression profiles of exosomal mRNA, lncRNA, and circRNA were differentially analyzed by R. The related mRNA, circRNA, lncRNA, and their corresponding miRNA prediction data were imported into Cytoscape software to visualize the ceRNA network. Then, we conducted GO and KEGG enrichment analysis of mRNA in the ceRNA network. Genes that express differently in pancreatic cancer tissues compared with normal tissues and associate with survival (P < 0.05) were determined as Hub genes by GEPIA. We identified optimal prognosis-related differentially expressed mRNAs (DEmRNAs) and generated a risk score model by performing univariate and multivariate Cox regression analyses. RESULTS: 205 DEmRNAs, 118 differentially expressed lncRNAs (DElncRNAs), and 98 differentially expressed circRNAs (DEcircRNAs) were screened out. We constructed the ceRNA network, and a total of 26 mRNA nodes, 7 lncRNA nodes, 6 circRNA nodes, and 16 miRNA nodes were identified. KEGG enrichment analysis showed that the DEmRNAs in the regulatory network were mainly enriched in Human papillomavirus infection, PI3K-Akt signaling pathway, Osteoclast differentiation, and ECM-receptor interaction. Next, six hub genes (S100A14, KRT8, KRT19, MAL2, MYO5B, PSCA) were determined through GEPIA. They all showed significantly increased expression in cancer tissues compared with control groups, and their high expression pointed to adverse survival. Two optimal prognostic-related DEmRNAs, MYO5B (HR = 1.41, P < 0.05) and PSCA (HR = 1.10, P < 0.05) were included to construct the survival prediction model. CONCLUSION: In this study, we successfully constructed a ceRNA regulatory network in blood exosomes from PAAD patients and developed a two-gene survival prediction model that provided new targets which shall aid in diagnosing and treating PAAD.

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OBJECTIVES: This study aimed to investigate the differentiation state and clinical significance of colorectal cancer cells, as well as to predict the immune response and prognosis of patients based on differentiation-related genes of colorectal cancer. INTRODUCTION: Colorectal cancer cells exhibit different differentiation states under the influence of the tumor microenvironment, which determines the cell fates. METHODS: We combined single-cell sequencing (scRNA-seq) data from The Cancer Genome Atlas source with extensive transcriptome data from the Gene Expression Omnibus database. We obtained colorectal cancer differentiation-related genes using cell trajectory analysis and developed a colorectal cancer differentiation-related gene based molecular typing and prognostic model to predict the immune response and prognosis of patients with colorectal cancer. RESULTS: We identified 5 distinct cell differentiation subsets and 620 colorectal cancer differentiation-related genes. Colorectal cancer differentiation-related genes were significantly associated with metabolism, angiogenesis, and immunity. We separated patients into 3 subtypes based on colorectal cancer differentiation-related gene expression in the tumor and found differences among the different subtypes in immune infiltration status, immune checkpoint gene expression, clinicopathological features, and overall survival. Immunotherapeutic interventions involving a highly expressed immune checkpoint blockade may be selectively effective in the corresponding cancer subtypes. We built a risk score prediction model (5-year AUC: .729) consisting of the 4 most important predictors of survival (TIMP1, MMP1, LGALS4, and ITLN1). Finally, we generated and validated a nomogram consisting of the risk score and clinicopathological variables. CONCLUSION: This study highlights the significance of genes involved in cell differentiation for clinical prognosis and immunotherapy in patients and provides prospective therapeutic targets for colorectal cancer.

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A survival prediction model has recently been developed to evaluate the prognosis of resected nonmetastatic pancreatic ductal adenocarcinoma based on a Cox model using two nationwide databases: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP). In this study, we applied two machine learning methods-random survival forests (RSF) and support vector machines (SVM)-for survival analysis and compared their prediction performance using the SEER and KOTUS-BP datasets. Three schemes were used for model development and evaluation. First, we utilized data from SEER for model development and used data from KOTUS-BP for external evaluation. Second, these two datasets were swapped by taking data from KOTUS-BP for model development and data from SEER for external evaluation. Finally, we mixed these two datasets half and half and utilized the mixed datasets for model development and validation. We used 9,624 patients from SEER and 3,281 patients from KOTUS-BP to construct a prediction model with seven covariates: age, sex, histologic differentiation, adjuvant treatment, resection margin status, and the American Joint Committee on Cancer 8th edition T-stage and N-stage. Comparing the three schemes, the performance of the Cox model, RSF, and SVM was better when using the mixed datasets than when using the unmixed datasets. When using the mixed datasets, the C-index, 1-year, 2-year, and 3-year time-dependent areas under the curve for the Cox model were 0.644, 0.698, 0.680, and 0.687, respectively. The Cox model performed slightly better than RSF and SVM.

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OBJECTIVE: To analyze the efficacy and complications of spinal metastasis surgery for breast cancer; to understand the survival and the influencing factors; and to verify the predictive ability of the currently used spinal metastasis cancer survival prediction scoring system on 1 year postoperative survival. METHODS: A retrospective study was conducted of 54 patients with spinal metastases from breast cancer who underwent open surgery after multidisciplinary consultation in our hospital from January 2017 to October 2020. Patient demographic-related variables, breast cancer-related variables, spinal disorder-related variables, and treatment-related variables were collected. Survival curves were plotted using the Kaplan-Meier method, 1-way tests were performed using the log-rank method for factors that might affect prognosis, and candidate variables were included in the Cox model for multifactor analysis. The Tomita score, modified Tokuhashi score, modified Bauer score, and modified Katagiri score were examined by plotting the subject operating characteristic curve and calculating the area under the curve. The area under the curve was used to test the predictive ability of the SORG (Skeletal Oncology Research Group) original version, SORG line graph version, and SORG Web version for 1-year postoperative survival in patients with spinal metastases from breast cancer. RESULTS: The average age was 51.3 ± 8.6 years in 54 patients. Twenty-one patients underwent vertebral body debulking surgery, 32 patients underwent palliative canal decompression, and 1 patient underwent vertebral en bloc resection, with an operative time of 229.3 ± 87.6 minutes and intraoperative bleeding of 1018.1 ± 931.1 mL. Postoperatively, the patient experienced significant pain relief and gradual recovery from nerve injury. Major surgical complications included cerebrospinal fluid leakage, secondary spinal cord injury, spinal tumor progression, and broken fixation. The mean survival was 32.2 months, including a 6-month survival of 90.7%, a 1-year survival of 77.8%, and a 2-year survival of 60.3%. Univariate analysis showed that preoperation with neurologic deficits, hormone-insensitive type, with brain metastases were potential risk factors for poor prognosis. Multifactorial analysis showed that hormone-insensitive type and concomitant brain metastasis were independent risk factors associated with poor prognosis. The SORG Web version had good ability to predict 1-year postoperative survival in patients with spinal metastases from breast cancer. CONCLUSIONS: Spinal metastasis from breast cancer has good surgical efficacy, low postoperative recurrence rate, and relatively long survival after surgery. Patients with hormone-insensitive type, with brain metastasis, have a poor prognosis, and SORG Web version can predict patients' 1-year survival more accurately.

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BACKGROUND: Accurate prediction of survival is crucial for both physicians and women with breast cancer to enable clinical decision making on appropriate treatments. The currently available survival prediction tools were developed based on demographic and clinical data obtained from specific populations and may underestimate or overestimate the survival of women with breast cancer in China. OBJECTIVE: This study aims to develop and validate a prognostic app to predict the overall survival of women with breast cancer in China. METHODS: Nine-year (January 2009-December 2017) clinical data of women with breast cancer who received surgery and adjuvant therapy from 2 hospitals in Xiamen were collected and matched against the death data from the Xiamen Center of Disease Control and Prevention. All samples were randomly divided (7:3 ratio) into a training set for model construction and a test set for model external validation. Multivariable Cox regression analysis was used to construct a survival prediction model. The model performance was evaluated by receiver operating characteristic (ROC) curve and Brier score. Finally, by running the survival prediction model in the app background thread, the prognostic app, called iCanPredict, was developed for women with breast cancer in China. RESULTS: A total of 1592 samples were included for data analysis. The training set comprised 1114 individuals and the test set comprised 478 individuals. Age at diagnosis, clinical stage, molecular classification, operative type, axillary lymph node dissection, chemotherapy, and endocrine therapy were incorporated into the model, where age at diagnosis (hazard ratio [HR] 1.031, 95% CI 1.011-1.051; P=.002), clinical stage (HR 3.044, 95% CI 2.347-3.928; P<.001), and endocrine therapy (HR 0.592, 95% CI 0.384-0.914; P=.02) significantly influenced the survival of women with breast cancer. The operative type (P=.81) and the other 4 variables (molecular classification [P=.91], breast reconstruction [P=.36], axillary lymph node dissection [P=.32], and chemotherapy [P=.84]) were not significant. The ROC curve of the training set showed that the model exhibited good discrimination for predicting 1- (area under the curve [AUC] 0.802, 95% CI 0.713-0.892), 5- (AUC 0.813, 95% CI 0.760-0.865), and 10-year (AUC 0.740, 95% CI 0.672-0.808) overall survival. The Brier scores at 1, 5, and 10 years after diagnosis were 0.005, 0.055, and 0.103 in the training set, respectively, and were less than 0.25, indicating good predictive ability. The test set externally validated model discrimination and calibration. In the iCanPredict app, when physicians or women input women's clinical information and their choice of surgery and adjuvant therapy, the corresponding 10-year survival prediction will be presented. CONCLUSIONS: This survival prediction model provided good model discrimination and calibration. iCanPredict is the first tool of its kind in China to provide survival predictions to women with breast cancer. iCanPredict will increase women's awareness of the similar survival rate of different surgeries and the importance of adherence to endocrine therapy, ultimately helping women to make informed decisions regarding treatment for breast cancer.

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Objective:To investigate the predictive value of clinical radiomics model based on nnU-Net for the prognosis of gallbladder carcinoma (GBC).Methods:The retrospective cohort study was conducted. The clinicopathological data of 168 patients who underwent curative-intent radical resection of GBC in the First Affiliated Hospital of Xi'an Jiaotong University from January 2012 to December 2020 were collected. There were 61 males and 107 females, aged (64±11)years. All the 168 patients who underwent preoperative enhanced computed tomography (CT) examina-tion were randomly divided into 126 cases in training set and 42 cases in test set according to the ratio of 3:1 based on random number table. For the portal venous phase images, 2 radiologists manually delineated the region of interest (ROI), and constructed a nnU-net model to automatically segment the images. The 5-fold cross-validation and Dice similarity coefficient were used to evaluate the generalization ability and predictive performance of the nnU-net model. The Python software (version 3.7.10) and Pyradiomics toolkit (version 3.0.1) were used to extract the radiomics features, the R software (version 4.1.1) was used to screen the radiomics features, and the variance method, Pearson correlation analysis, one-way COX analysis and random survival forest model were used to screen important radiomics features and calculate the Radiomics score (Radscore). X-tile software (version 3.6.1) was used to determine the best cut-off value of Radscore, and COX proportional hazard regression model was used to analyze the independent factors affecting the prognosis of patients. The training set data were imported into R software (version 4.1.1) to construct a clinical radiomics nomogram model of survival prediction for GBC. Based on the Radscore and the independent clinical factors affecting the prognosis of patients, the Radscore risk model and the clinical model for predicting the survival of GBC were constructed respectively. The C-index, calibration plot and decision curve analysis were used to evaluate the predictive ability of different survival prediction models for GBC. Observation indicators: (1) segmentation results of portal venous phase images in CT examination of GBC; (2) radiomic feature screening and Radscore calculation; (3) prognostic factors analysis of patients after curative-intent radical resection of GBC; (4) construction and evaluation of different survival prediction models for GBC. Measurement data with normal distribution were represented by Mean± SD. Count data were expressed as absolute numbers or percentages, and comparison between groups was analyzed using the chi-square test. Univariate and multivariate analyses were performed using the COX proportional hazard regression model. The postoperative overall survival rate was calculated by the life table method. Results:(1) Segmentation results of portal venous phase images in CT examination of GBC: the Dice similarity coefficient of the ROI based on the manual segmentation and nnU-Net segmentation models was 0.92±0.08 in the training set and 0.74±0.15 in the test set, respectively. (2) Radiomic feature screening and Radscore calculation: 1 502 radiomics features were finally extracted from 168 patients. A total of 13 radiomic features (3 shape features and 10 high-order features) were screened by the variance method, Pearson correlation analysis, one-way COX analysis and random survival forest model. Results of random survival forest model analysis and X-tile software analysis showed that the best cut-off values of the Radscore were 6.68 and 25.01. A total of 126 patients in the training set were divided into 41 cases of low-risk (≤6.68), 72 cases of intermediate-risk (>6.68 and <25.01), and 13 cases of high-risk (≥25.01). (3) Prognostic factors analysis of patients after curative-intent radical resection of GBC: the 1-, 2-, and 3-year overall survival rates of 168 patients were 75.8%, 54.9% and 45.7%, respectively. The results of univariate analysis showed that preopera-tive jaundice, serum CA19-9 level, Radscore risk (medium risk and high risk), extent of surgical resection, pathological T staging, pathological N staging, tumor differentiation degree (moderate differentiation and low differentiation) were related factors affecting prognosis of patients in the training set ( hazard ratio=3.28, 3.00, 3.78, 6.34, 4.48, 6.43, 3.35, 7.44, 15.11, 95% confidence interval as 1.91?5.63, 1.76?5.13, 1.76?8.09, 2.49?16.17, 2.30?8.70, 1.57?26.36, 1.96?5.73, 1.02?54.55, 2.04?112.05, P<0.05). Results of multivariate analysis showed that preoperative jaundice, serum CA19-9 level, Radscore risk as high risk and pathological N staging were independent influencing factors for prognosis of patients in the training set ( hazard ratio=2.22, 2.02, 2.89, 2.07, 95% confidence interval as 1.20?4.11, 1.11?3.68, 1.04?8.01, 1.15?3.73, P<0.05). (4) Construction and evaluation of different survival prediction models for GBC. Clinical radiomics model, Radscore risk model and clinical model were established based on the independent influencing factors for prognosis, the C-index of which was 0.775, 0.651 and 0.747 in the training set, and 0.759, 0.633, 0.739 in the test set, respectively. The calibration plots showed that the Radscore risk model, clinical model and clinical radiomics model had good predictive ability for prognosis of patients. The decision curve analysis showed that the prognostic predictive ability of the clinical radiomics model was better than that of the Radscore risk and clinical models. Conclusion:The clinical radiomics model based on the nnU-Net has a good predictive performance for prognosis of GBC.

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Gallbladder carcinoma (GBC) is the most common malignancy of the biliary tract, radical resection is the only effective treatment for GBC at present. However, the postoperative effect is still poor. Therefore, identifying the key prognostic factors and establishing an individual and accurate survival prediction model for GBC are critical to prognosis assessment, treatment options and clinical decision support in patients with GBC. The prediction value of current commonly used TNM staging system is limited. Cox regression model is the most commonly used classical survival analysis method, but it is difficult to establish the association between prognostic variables. Nomogram and machine learning techniques including Bayesian network have been used to establish survival prediction model of GBC in recent years, which representing a certain degree of advancement, however, the model precision and clinical application still need to be further verified. The establishment of more accurate survival prediction models for GBC based on machine learning algorithm from Chinese multicenter large sample database to guide the clinical decision-making is the main research direction in the future.

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