scholarly journals A Novel Nomogram Combined With Radiomics Features, Age and Albuminuria to Predict the Pathological Grade of Bladder Cancer Running Title: Application Study on the Prediction Model of Bladder Cancer Pathological Grade Based on Parameters of Thin-layer Enhanced CT. Author Information

2021 ◽  
Author(s):  
Qi Zhou ◽  
Lu Ma ◽  
Haoyang Zhang ◽  
Xiaojie Ang ◽  
Can Hu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Urothelial Carcinoma ◽  
Texture Features ◽  
Comprehensive Model ◽  
Pathological Grade ◽  
Lasso Regression ◽  
Clinical Factors ◽  
Enhanced Ct

Abstract Background: Based on multi-parameter thin-slice enhanced CT texture features and related clinical indicators, a preoperative pathological grade prediction model of bladder urothelial carcinoma was established.Methods: The CT images and clinical data of 372 patients with urothelial carcinoma in our hospital from January 2015 to October 2020 were collected. 372 patients were divided into two groups: HGUC(n=190) and LGUC(n=182). All patients were divided into 10 groups on average, of which 7 were used as training group (n=259) and the remaining 3 as verification group (n=113). Then, by using 3D-Slicer software from all enhanced in patients with preoperative CT images (Arterial and Venous phase calibration chart) split out the region of interest (ROI), respectively from the tumor image data extraction based on First-order and Second-order, High-order and filtering characteristics of 1223 texture features, and use the inter/intra-class correlation coefficient(ICC > 0.75) between classes and least absolute shrinkage selection operator (LASSO) regression feature selection; Secondly, the clinical effective factors were obtained by logistic regression analysis, and the clinical predictive model was constructed. Finally, the selected clinical key indicators and radiomics features were mapped. In order to verify the predictive ability of the nomogram, conformance index (C-index), calibration curve, Receiver operator characteristic (ROC) curve and clinical decision curve analysis (DCA) were used to test the nomogram.Results: Lasso regression analysis showed that 11 radiomics features were significantly correlated with the pathological grade of bladder cancer. After comparing the four models, it is found that Logistic regression model has the best prediction ability (AUC=0.858). The results of multivariate analysis showed that age and albuminuria were independent influencing factors. A comprehensive model for predicting the pathological grade of bladder cancer (radiomics + clinical) was constructed by combining clinical independent risk factors with 11 radiomics features. Compared with clinical feature model and radiomics model, it was found that the predictive performance of imaging comprehensive model combined with clinical factors was the best (AUC=0.864).Conclusions: The radiomics model based on multi-parameter thin-layer enhanced CT, combined with clinical factors, can effectively predict high-and low-grade urothelial carcinoma.

scholarly journals Tumor Expression Profile Analysis Developed and Validated a Prognostic Model Based on Immune-Related Genes in Bladder Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Bingqi Dong ◽  
Jiaming Liang ◽  
Ding Li ◽  
Wenping Song ◽  
Shiming Zhao ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Regression Analysis ◽  
Malignant Tumors ◽  
Immune Therapy ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Lasso Regression ◽  
Potential Biomarker ◽  
Immune Related Genes

Background: Bladder cancer (BLCA) ranks 10th in incidence among malignant tumors and 6th in incidence among malignant tumors in males. With the application of immune therapy, the overall survival (OS) rate of BLCA patients has greatly improved, but the 5-year survival rate of BLCA patients is still low. Furthermore, not every BLCA patient benefits from immunotherapy, and there are a limited number of biomarkers for predicting the immunotherapy response. Therefore, novel biomarkers for predicting the immunotherapy response and prognosis of BLCA are urgently needed.Methods: The RNA sequencing (RNA-seq) data, clinical data and gene annotation files for The Cancer Genome Atlas (TCGA) BLCA cohort were extracted from the University of California, Santa Cruz (UCSC) Xena Browser. The BLCA datasets GSE31684 and GSE32894 from the Gene Expression Omnibus (GEO) database were extracted for external validation. Immune-related genes were extracted from InnateDB. Significant differentially expressed genes (DEGs) were identified using the R package “limma,” and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the DEGs were performed using R package “clusterProfiler.” Least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the signature model. The infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. The performance of the model was evaluated with receiver operating characteristic (ROC) curves and calibration curves.Results: In total, 1,040 immune-related DEGs were identified, and eight signature genes were selected to construct a model using LASSO regression analysis. The risk score of BLCA patients based on the signature model was negatively correlated with OS and the immunotherapy response. The ROC curve for OS revealed that the model had good accuracy. The calibration curve showed good agreement between the predictions and actual observations.Conclusions: Herein, we constructed an immune-related eight-gene signature that could be a potential biomarker to predict the immunotherapy response and prognosis of BLCA patients.

scholarly journals Preoperative positive voided urine cytology predicts poor clinical outcomes in patients with upper tract urothelial carcinoma undergoing nephroureterectomy

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Wen Liu ◽  
Zhankun Wang ◽  
Shuai Liu ◽  
Yu Yao ◽  
Yong Liu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Urothelial Carcinoma ◽  
Logistic Regression Analysis ◽  
Tumor Grade ◽  
Urine Cytology ◽  
Upper Tract Urothelial Carcinoma ◽  
Urinary Cytology ◽  
Upper Tract ◽  
The Impact

Abstract Background Performance of urinary cytology is recommended as the part of a standard diagnostic workup and base surveillance regimens in upper tract urothelial carcinoma (UTUC). However, the effect of positive voided urine cytology (VUC) on UTUC prognosis, compared with negative VUC, has not been fully demonstrated. This study aimed to evaluate the impact of preoperative VUC on predicting intravesical recurrence, disease recurrence, and mortality in patients with UTUC who underwent nephroureterectomy (RNU). Methods Clinicopathological information was collected from 315 UTUC patients treated with RNU. The association between VUC and oncological outcomes was analyzed using the Kaplan–Meier method with log-rank test and Cox proportional hazards regression models. Multiple logistic regression analysis was performed to identify the influence of VUC on tumor grade. Results Preoperative positive VUC, presenting in 101 patients (32%), was significantly associated with tumor multifocality (P = 0.017) and higher tumor grade (P = 0.010). On multivariable Cox regression analyses, preoperative positive VUC was an independent prognostic factor of intravesical recurrence-free survival (RFS) (hazard ratio [HR] = 2.21, 95% confidence interval [CI] 1.06–4.64; P = 0.035), RFS (HR = 1.80, 95% CI 1.08–2.99; P = 0.023), and cancer-specific survival (CSS) (HR = 1.87, 95% CI 1.10–3.18; P = 0.020), but not overall survival (HR = 1.32, 95% CI 0.80–2.18; P = 0.28). Logistic regression analysis revealed that VUC was related to high tumor grade in UTUC (odds ratio = 2.23, 95%CI 1.15–4.52). Conclusion Preoperative positive VUC significantly increases the risk of intravesical recurrence in UTUC patients undergoing RNU. In addition, positive VUC is an adverse predictor of RFS and CSS, which might be due to the association between positive VUC and high tumor grade.

scholarly journals Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas?

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Lulu Liu ◽  
Fangxiao Lu ◽  
Peipei Pang ◽  
Guoliang Shao
Keyword(s):  
Computed Tomography ◽  
Sensitivity And Specificity ◽  
Texture Features ◽  
Training Dataset ◽  
Features Selection ◽  
Clinical Factors ◽  
Combined Model ◽  
Test Dataset ◽  
Enhanced Ct ◽  
Mediastinal Cysts

Abstract Background Anterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resource waste. The purpose of this study is to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas. Methods A group of 188 patients with pathologically confirmed AMC (106 cases misdiagnosed as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the artificial intelligence kit (AK) software. A total of 180 tumour texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis, and LASSO were used to features selection and then the radiomics signature (radscore) was obtained. The combined model including radscore and independent clinical factors was developed. The model performances were evaluated on discrimination, calibration curve. Results Two radscore models were constructed from the unenhanced and enhanced phases based on the selected four and three features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3%, and 83.8% in the training dataset and 0.899, 84.6%, and 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model). Conclusions The study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.

scholarly journals Preoperative positive voided urine cytology predicts poor clinical outcomes in patients with upper tract urothelial carcinoma undergoing nephroureterectomy

2020 ◽  
Author(s):  
Wen Liu ◽  
Zhankun Wang ◽  
Shuai Liu ◽  
Yu Yao ◽  
Yong Liu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Urothelial Carcinoma ◽  
Logistic Regression Analysis ◽  
Tumor Grade ◽  
Urine Cytology ◽  
Upper Tract Urothelial Carcinoma ◽  
Urinary Cytology ◽  
Upper Tract ◽  
The Impact

Abstract Background: Performance of urinary cytology is recommended as the part of a standard diagnostic workup and base surveillance regimens in upper tract urothelial carcinoma (UTUC). However, the effect of positive voided urine cytology (VUC) on UTUC prognosis, compared with negative VUC, has not been fully demonstrated. This study aimed to evaluate the impact of preoperative VUC on predicting intravesical recurrence, disease recurrence, and mortality in patients with UTUC who underwent nephroureterectomy (RNU).Methods: Clinicopathological information was collected from 315 UTUC patients treated with RNU. The association between VUC and oncological outcomes was analyzed using the Kaplan–Meier method with log-rank test and Cox proportional hazards regression models. Multiple logistic regression analysis was performed to identify the influence of VUC on tumor grade.Results: Preoperative positive VUC, presenting in 101 patients (32%), was significantly associated with tumor multifocality (P = 0.017) and higher tumor grade (P = 0.010). On multivariable Cox regression analyses, preoperative positive VUC was an independent prognostic factor of intravesical recurrence-free survival (RFS) (hazard ratio [HR] = 2.21, 95% confidence interval [CI] 1.06–4.64; P = 0.035), RFS (HR = 1.80, 95% CI 1.08–2.99; P = 0.023), and cancer-specific survival (CSS) (HR = 1.87, 95% CI 1.10–3.18; P = 0.020), but not overall survival (HR = 1.32, 95% CI 0.80–2.18; P = 0.28). Logistic regression analysis revealed that VUC was related to high tumor grade in UTUC (odds ratio = 2.23, 95%CI 1.15–4.52).Conclusion: Preoperative positive VUC significantly increases the risk of intravesical recurrence in UTUC patients undergoing RNU. In addition, positive VUC is an adverse predictor of RFS and CSS, which might be due to the association between positive VUC and high tumor grade.

Does warfarin therapy influence the risk of bladder cancer?

2004 ◽  
Vol 91 (04) ◽  
pp. 801-805 ◽  
Author(s):  
Philip Foulis ◽  
Skai Schwartz ◽  
Thomas Mason ◽  
William Blumentals
Keyword(s):  
Bladder Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Venous Thromboembolism ◽  
Warfarin Therapy ◽  
Biological Effects ◽  
Administrative Database ◽  
Veterans Administration ◽  
Unconditional Logistic Regression ◽  
Urogenital Cancers

SummaryThere has been growing interest in studying the biological effects of certain drugs and their potential to reduce the risk of various cancers. One study reported a decrease in the incidence of urogenital cancers in a trial with patients who received warfarin for treatment of venous thromboembolism, but a limitation to this study of urogenital cancers was the very small number of bladder cancer cases that developed following warfarin therapy. The objective of the present study is to measure the association between warfarin use and bladder cancer. A total of 330 cases with bladder cancer were identified at the James A. Haley Veterans’ Administration (VA) Hospital in Tampa, Florida, using a combination of computerized pathology records and inpatient and outpatient diagnoses. Controls were randomly selected from the VA computerized administrative database and 1293 controls were included for analysis. Unconditional logistic regression analysis was performed to assess the risk of bladder cancer after adjusting for age, gender, and cigarette smoking. Among warfarin users, although there was a 27% elevation in risk, it did not differ significantly from nonusers (OR = 1.27, 95% CI = 0.85, 1.89). No durationresponse relationship was observed between anticoagulant use and risk of bladder cancer. The results suggest that warfarin does not protect against bladder cancer, at least in male smokers, the highest risk population for bladder cancer.

scholarly journals Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Hai-Yan Chen ◽  
Xue-Ying Deng ◽  
Yao Pan ◽  
Jie-Yu Chen ◽  
Yun-Ying Liu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Texture Analysis ◽  
Validation Cohort ◽  
Texture Features ◽  
Imaging Features ◽  
Cystic Neoplasms ◽  
Radiological Features ◽  
Model Combining ◽  
Enhanced Ct ◽  
Ct Texture Analysis

ObjectiveTo establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs).Materials and MethodsFifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity.ResultsFollowing multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively.ConclusionThis study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

scholarly journals Development and Validation of Prognostic Model in Transitional Bladder Cancer Based on Inflammatory Response-Associated Genes

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhiwen Xie ◽  
Jinming Cai ◽  
Wenlan Sun ◽  
Shan Hua ◽  
Xingjie Wang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Regression Analysis ◽  
Survival Analysis ◽  
Inflammatory Response ◽  
Drug Sensitivity ◽  
Risk Groups ◽  
Risk Scores ◽  
Lasso Regression

BackgroundBladder cancer is a common malignant type in the world, and over 90% are transitional cell carcinoma. While the impact of inflammatory response on cancer progression has been reported, the role of inflammatory response-associated genes (IRAGs) in transitional bladder cancer still needs to be understood.MethodsIn this study, IRAGs were download from Molecular Signature Database (MSigDB). The transcriptional expression and matched clinicopathological data were separately obtained from public databases. The TCGA-BLCA cohort was used to identify the differentially expressed IRAGs, and prognostic IRAGs were filtrated by univariate survival analysis. The intersection between them was displayed by Venn diagram. Based on least absolute shrinkage and selection operator (LASSO) regression analysis method, the TCGA-BLCA cohort was used to construct a risk signature. Survival analysis was conducted to calculate the overall survival (OS) in TCGA and GSE13507 cohort between two groups. We then conducted univariate and multivariate survival analyses to identify independently significant indicators for prognosis. Relationships between the risk scores and age, grade, stage, immune cell infiltration, immune function, and drug sensitivity were demonstrated by correlation analysis. The expression level of prognostic genes in vivo and in vitro were determined by qRT-PCR assay.ResultsComparing with normal tissues, there were 49 differentially expressed IRAGs in cancer tissues, and 12 of them were markedly related to the prognosis in TCGA cohort for transitional bladder cancer patients. Based on LASSO regression analysis, a risk model consists of 10 IRAGs was established. Comparing with high-risk groups, survival analysis showed that patients in low-risk groups were more likely to have a better survival time in TCGA and GSE13507 cohorts. Besides, the accuracy of the model in predicting prognosis is acceptable, which is demonstrated by receiver operating characteristic curve (ROC) analysis. Age, stage, and risk scores variables were identified as the independently significant indicators for survival in transitional bladder cancer. Correlation analysis represented that the risk score was identified to be significantly related to the above variables except gender variable. Moreover, the expression level of prognostic genes in vivo and in vitro was markedly upregulated for transitional bladder cancer.ConclusionsA novel model based on the 10 IRAGs that can be used to predict survival time for transitional bladder cancer. In addition, this study may provide treatment strategies according to the drug sensitivity in the future.

scholarly journals Predictors of renal infarction in patients presenting to the emergency department with flank pain: A retrospective observational study

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261054
Author(s):  
Sangun Nah ◽  
Sangsoo Han ◽  
Han Bit Kim ◽  
Sohyeon Chun ◽  
Sechan Kim ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Emergency Department ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Current Smoker ◽  
Flank Pain ◽  
Renal Infarction ◽  
Clinical Factors ◽  
Male Sex

Objectives Flank pain is a common symptom in the emergency department and can be caused by a variety of diseases. Renal infarction (RI) is a very rare disease, and many RI patients complain of flank pain. However, there is no definitive predictor of RI when patients complain of flank pain. This study aimed to identify the clinical factors for predicting RI in patients with flank pain. Methods This retrospective single-center study was conducted on patients complaining of flank pain from January 2016 to March 2020 at a South Korean tertiary care hospital. Exclusion criteria included patients who did not undergo contrast-enhanced computed tomography, age < 18 years, and trauma. Demographic and laboratory data were obtained from medical records. Logistic regression analysis was conducted to identify predictors of RI occurrence. Results In all, 2,131 patients were enrolled, and 39 (1.8%) had RI. From a multivariable logistic regression analysis, an age ≥ 65 years (odds ratio [OR], 3.249; 95% confidence interval [CI], 1.366–7.725; p = 0.008), male sex (OR, 2.846; 95% CI, 1.190–6.808; p = 0.019), atrial fibrillation (OR, 10.386; 95% CI, 3.724–28.961; p < 0.001), current smoker (OR, 10.022; 95% CI, 4.565–22.001; p < 0.001), and no hematuria (OR, 0.267; 95% CI, 0.114–0.628; p = 0.002) were significantly associated with the occurrence of RI. Conclusions Five clinical factors, i.e., age ≥ 65 years, male sex, atrial fibrillation, current smoker, and no hematuria, were significantly associated with the occurrence of RI in patients with flank pain.

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