scholarly journals Radiomics-Based Distinction of s-HCC and Precancerous Lesion Based on Precontrast MRI

2021 ◽  
Author(s):  
JiaWen Luo ◽  
Kun Guo ◽  
XiaoNing Gao ◽  
Hao Liu ◽  
Yue Xiang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Sensitivity And Specificity ◽  
Precancerous Lesion ◽  
Test Group ◽  
Training Group ◽  
Accurate Diagnosis ◽  
Fat Suppression ◽  
Model Based

Abstract Background: To assess the feasibility of radiomics based on precontrast MRI for the distinguish of s-HCC and pre-HCC.Method: We retrospectively analyzed 146 nodules from 78 patients, with pathological confirmed. Each nodule was segment on precontrast MRI sequence(TIWI and fat-suppression T2WI), retrospectively. 1223radiomics features were extracted and the optimal 10 features were selected by LASSO to establish the logistic regression radiomics model. Result: The AUC, sensitivity and specificity of the training group and test group were 0.757 (95% CI 0.638 -0.853), 83.02% , 66.67% and 0.789 (95% CI 0.643-0.895), 88.89% and 80.00%, respectively. The AUC, sensitivity and specificity of the training group and test group were 0.903 (95% CI 0.807-0.962), 86.79% , 86.67% and 0.778 (95% CI 0.632-0.887), 75.00%, 80.00%, respectively. Delong test has proved that, the diagnositic performances of radiomics model based on T2WI were higher than that of radiomics model based on T1WI (p = 0.0379).Conclusion: Radiomics model can classify s-HCC and pre-HCC based on precontrast MRI. And may serve as an adjunct tool for accurate diagnosis of s-HCC.

scholarly journals A Prognostic Model Based on Six Metabolism-Related Genes in Colorectal Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yuan-Lin Sun ◽  
Yang Zhang ◽  
Yu-Chen Guo ◽  
Zi-Hao Yang ◽  
Yue-Chao Xu
Keyword(s):  
Colorectal Cancer ◽  
Prognostic Model ◽  
Statistical Significance ◽  
Test Group ◽  
Training Group ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Clinical Samples ◽  
Ppi Network ◽  
Model Based

An increasing number of studies have shown that abnormal metabolism processes are closely correlated with the genesis and progression of colorectal cancer (CRC). In this study, we systematically explored the prognostic value of metabolism-related genes (MRGs) for CRC patients. A total of 289 differentially expressed MRGs were screened based on The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB), and 72 differentially expressed transcription factors (TFs) were obtained from TCGA and the Cistrome Project database. The clinical samples obtained from TCGA were randomly divided at a ratio of 7 : 3 to obtain the training group (n=306) and the test group (n=128). After univariate and multivariate Cox regression analyses, we constructed a prognostic model based on 6 MRGs (AOC2, ENPP2, ADA, GPD1L, ACADL, and CPT2). Kaplan–Meier survival analysis of the training group, validation group, and overall samples proved that the model had statistical significance in predicting the outcomes of patients. Independent prognosis analysis suggested that this risk score might serve as an independent prognosis factor for CRC patients. Moreover, we combined the prognostic model and the clinical characteristics in a nomogram to predict the overall survival of CRC patients. Furthermore, gene set enrichment analysis (GSEA) was conducted to identify the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in the high- and low-risk groups, which might provide novel therapeutic targets for CRC patients. We discovered through the protein-protein interaction (PPI) network and TF-MRG regulatory network that 7 hub genes were retrieved from the PPI network and 4 kinds of differentially expressed TFs (NR3C1, MYH11, MAF, and CBX7) positively regulated 4 prognosis-associated MRGs (GSTM5, PTGIS, ENPP2, and P4HA3).

scholarly journals Urine Cellular DNA Point Mutation and Methylation as Potential Biomarkers for the Detection of Urothelial Carcinoma

2021 ◽  
Author(s):  
Junjie Zhang ◽  
Ran Xu ◽  
Qiang Lu ◽  
Zhenzhou Xu ◽  
Jianye Liu ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Sensitivity And Specificity ◽  
External Validation ◽  
Point Mutations ◽  
Test Group ◽  
Training Group ◽  
The Novel ◽  
Clinical Trial Registry ◽  
Urine Sediment ◽  
Cellular Dna

Abstract BackgroundPreviously, our team identified a seven-gene mutation panel in urine sediment to discriminate UBC from benign urological diseases. In the present study, we aimed to validate the panel in an expanded and close to natural population cohort of hematuria. Also, we tried to optimize the panel by incorporating methylation biomarkers. We performed external validation to investigate the robustness and stability of the novel panel.MethodsPatients with urothelial carcinomas and controls were prospectively recruited in clinical trial ChiCTR2000029980. The mutation panel was validated in the expanded cohort(n=333) from Hunan multicenter. Several UBC-specific methylation biomarkers were identified by comprehensive analyses of a series of TCGA, GEO and an independent cohorts, and examined in the expanded cohort. Random Forest algorithm was used to construct an optimal panel. External validation of the optimal panel was carried out in Beijing single center cohort(n=89). NGS technique was used to analyze the DNA point mutations and MS-PCR for methylation.ResultsThe AUC, sensitivity and specificity of the mutation panel in expanded cohort were 0.81, 0.67 and 0.90, respectively. After screening, only cg16966315, cg17945976 and cg24720571 were left for further analysis. The optimal panel consisted of cg24720571 and 8 point mutations, including TERT.chr5_1295228 G_A (TERT 228), FGFR3.chr4_1803568 C_G (FGFR3 568), TERT.chr5_1295250 G_A (TERT 250), FGFR3.chr4_1806099 A_G (FGFR3 099), PIK3CA.chr3_178936091 G_A (PIK3CA 091), PIK3CA.chr3_178952085 A_G (PIK3CA 085), PIK3CA.chr3_178936082 G_A (PIK3CA 082), HRAS.chr11_533874 T_C (HRAS 874). The AUC, sensitivity and specificity of the optimal panel in training group were 0.89, 0.84 and 0.79, respectively, and in test group were 0.95, 0.91 and 0.95, respectively. In the external validation, the AUC, sensitivity and specificity were 0.98, 0.93 and 0.93, respectively.ConclusionsThe optimal panel was obviously superior to previous mutation panel and showed a highly specific and robust performance. The optimal panel may be used as a replaceable approach for early detection of UC.Trial registrationThis research was registered in Chinese Clinical Trial Registry(ChiCTR2000029980).

scholarly journals Urine Cellular DNA Point Mutation and Methylation as Potential Biomarkers for Early Detection of Urothelial Carcinoma

2021 ◽  
Author(s):  
Junjie Zhang ◽  
Ran Xu ◽  
qiang lu ◽  
zhenzhou Xu ◽  
jianye Liu ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Early Detection ◽  
Sensitivity And Specificity ◽  
External Validation ◽  
Point Mutations ◽  
Test Group ◽  
Training Group ◽  
Clinical Trial Registry ◽  
Urine Sediment ◽  
Cellular Dna

Abstract Background: Previously, our team identified a seven-gene mutation panel in urine sediment to discriminate UBC from benign urological diseases. In the present study, we aimed to validate the panel in an expanded and close to natural population cohort of hematuria. Also, we tried to optimize the panel by incorporating methylation biomarkers. We performed external validation to investigate the robustness and stability of the novel panel. Methods: Patients with urothelial carcinomas and controls were prospectively recruited in clinical trial ChiCTR2000029980. The mutation panel was validated in the expanded cohort(n=333) from Hunan multicenter. Several UBC-specific methylation biomarkers were identified by comprehensive analyses of a series of TCGA, GEO and an independent cohorts, and examined in the expanded cohort. Random Forest algorithm was used to construct an optimal panel. External validation of the optimal panel was carried out in Beijing single center cohort(n=89). NGS technique was used to analyze the DNA point mutations and MS-PCR for methylation.Results: The AUC, sensitivity and specificity of the mutation panel in expanded cohort were 0.81, 0.67 and 0.90, respectively. After screening, only cg16966315, cg17945976 and cg24720571 were left for further analysis. The optimal panel consisted of cg24720571 and 8 point mutations, including TERT 228(G_A), FGFR3 568(C_T), TERT 250(G_A), FGFR3 099(A_G), PIK3CA 091(G_A), PIK3CA 085(A_G), PIK3CA 082 (G_A) and HRAS 874(T_C). The AUC, sensitivity and specificity of the optimal panel in training group were 0.89, 0.84 and 0.79, respectively, and in test group were 0.95, 0.91 and 0.95, respectively. In the external validation, the AUC, sensitivity and specificity were 0.98, 0.93 and 0.93, respectively.Conclusions: The optimal panel was obviously superior to previous mutation panel and showed a highly specific and robust performance. The optimal panel may be used as a replaceable approach for early detection of UC.Trial registration: This research was registered in Chinese Clinical Trial Registry(ChiCTR2000029980).

Multivariable Logistic Regression Equation to Predict Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Dong Cheng ◽  
Qinghua Li ◽  
Jingbo Zhu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Specific Antigen ◽  
Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

An Optimized Logistic Regression Model Based on the Maximum Entropy Estimation Under the Hesitant Fuzzy Environment

2021 ◽  
pp. 1-25
Author(s):  
Chenyang Song ◽  
Liguo Wang ◽  
Zeshui Xu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Maximum Entropy ◽  
Logistic Regression Model ◽  
Uncertain Information ◽  
Entropy Estimation ◽  
Model Based ◽  
Fuzzy Environment ◽  
Maximum Entropy Estimation ◽  
Specific Implementation

The logistic regression model is one of the most widely used classification models. In some practical situations, few samples and massive uncertain information bring more challenges to the application of the traditional logistic regression. This paper takes advantages of the hesitant fuzzy set (HFS) in depicting uncertain information and develops the logistic regression model under hesitant fuzzy environment. Considering the complexity and uncertainty in the application of this logistic regression, the concept of hesitant fuzzy information flow (HFIF) and the correlation coefficient between HFSs are introduced to determine the main factors. In order to better manage situations with small samples, a new optimized method based on the maximum entropy estimation is also proposed to determine the parameters. Then the Levenberg–Marquardt Algorithm (LMA) under hesitant fuzzy environment is developed to solve the parameter estimation problem with fewer samples and uncertain information in the logistic regression model. A specific implementation process for the optimized logistic regression model based on the maximum entropy estimation under the hesitant fuzzy environment is also provided. Moreover, we apply the proposed model to the prediction problem of Emergency Extreme Air Pollution Event (EEAPE). A comparative analysis and a sensitivity analysis are further conducted to illustrate the advantages of the optimized logistic regression model under hesitant fuzzy environment.

scholarly journals Clinical Significance of CYFRA21-1, AFP, CA-153, CEA, and CA-199 in the Diagnosis of Lung Cancer Ocular Metastasis in Hypertension Population

2021 ◽  
Vol 8 ◽  
Author(s):  
Jing Tang ◽  
Qian-Min Ge ◽  
Rong Huang ◽  
Hui-Ye Shu ◽  
Ting Su ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Logistic Regression ◽  
Sensitivity And Specificity ◽  
Cancer Metastasis ◽  
Binary Logistic Regression ◽  
Diagnostic Value ◽  
Ca 125 ◽  
Pathological Type ◽  
Independent Risk Factors

Purpose: To detect lung metastases, we conducted a retrospective study to improve patient prognosis.Methods: Hypertension patients with ocular metastases (OM group; n = 58) and without metastases (NM group; n = 1,217) were selected from individuals with lung cancer admitted to our hospital from April 2005 to October 2019. The clinical characteristics were compared by Student's t-test and chi-square test. Independent risk factors were identified by binary logistic regression, and their diagnostic value evaluated by receiver operating characteristic curve analysis.Results: Age and sex did not differ significantly between OM and NM groups; There were significant differences in pathological type and treatment. Adenocarcinoma was the main pathological type in the OM group (67.24%), while squamous cell carcinoma was the largest proportion (46.43%) in the NM group, followed by adenocarcinoma (34.10%). The OM group were treated with chemotherapy (55.17%), while the NM group received both chemotherapy (39.93%) and surgical treatment (37.06%). Significant differences were detected in the concentrations of cancer antigen (CA)−125, CA-199, CA-153, alpha fetoprotein (AFP), carcinoembryonic antigen (CEA), cytokeratin fraction 21-1 (CYFRA21-1), total prostate-specific antigen, alkaline phosphatase, and hemoglobin (Student's t-test). Binary logistic regression analysis indicated that CA-199, CA-153, AFP, CEA, and CYRFA21-1 were independent risk factors for lung cancer metastasis. AFP (98.3%) and CEA (89.3%) exhibited the highest sensitivity and specificity, respectively, while CYRFA21-1 had the highest area under the ROC curve value (0.875), with sensitivity and specificity values of 77.6 and 87.0%, respectively. Hence, CYFRA21-1 had the best diagnostic value.

scholarly journals Development and Validation of a Nine-Redox-Related Long Noncoding RNA Signature in Renal Clear Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-30
Author(s):  
Xia Qi-Dong ◽  
Xun Yang ◽  
Jun-Lin Lu ◽  
Chen-Qian Liu ◽  
Jian-Xuan Sun ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Clear Cell ◽  
Test Group ◽  
Training Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature

Background. Redox plays an essential role in the pathogeneses and progression of tumors, which could be regulated by long noncoding RNA (lncRNA). We aimed to develop and verify a novel redox-related lncRNA-based prognostic signature for clear cell renal cell carcinoma (ccRCC). Materials and Methods. A total of 530 ccRCC patients from The Cancer Genome Atlas (TCGA) were included in this study. All the samples were randomly split into training and test group at a 1 : 1 ratio. Then, we screened differentially expressed redox-related lncRNAs and constructed a novel prognostic signature from the training group using the least absolute shrinkage and selection operation (LASSO) and COX regression. Next, to verify the accuracy of the signature, we conducted risk and survival analysis, as well as the construction of ROC curve, nomogram, and calibration curves in the training group, test group, and all samples. Finally, the redox gene-redox-related lncRNA interaction network was constructed, and gene set enrichment analysis (GSEA) was performed to investigate the status of redox-related functions between high/low-risk groups. Results. A nine-redox-related lncRNA signature consisted of AC025580.3, COLCA1, AC027601.2, DLEU2, AC004918.3, AP006621.2, AL031670.1, SPINT1-AS1, and LAMA5-AS1 was significantly associated with overall survival in ccRCC patients. The signature proved efficient, and thus, a nomogram was successfully assembled. In addition, the GSEA results demonstrated that two major redox-related functions were enhanced in the high-risk group ccRCC patients. Conclusions. Our findings robustly demonstrate that the nine-redox-related lncRNA signature could serve as an efficient prognostic indicator for ccRCC.

scholarly journals Fear of COVID-19 predicts vaccination willingness 14 months later

2021 ◽  
Author(s):  
Gaëtan Mertens ◽  
Paul Lodder ◽  
Tom Smeets ◽  
Stefanie Duijndam
Keyword(s):  
Logistic Regression ◽  
Longitudinal Study ◽  
Infection Control ◽  
Sensitivity And Specificity ◽  
Demographic Variables ◽  
Substantial Portion ◽  
Self Rated Health ◽  
Health Agencies ◽  
Previous Infection ◽  
Potential Factors

Vaccines are an important tool for governments and health agencies to contain and curb the Coronavirus Disease-19 (COVID-19) pandemic. However, despite their effectiveness and safeness, a substantial portion of the population worldwide is hesitant to get vaccinated. In the current study, we examined whether fear of COVID-19 predicts vaccination willingness. In a longitudinal study (N = 938), fear for COVID-19 was assessed in April 2020 and vaccination willingness was measured in June 2021. Approximately 11% of our sample indicated that they were not willing to get vaccinated. Results of a logistic regression showed that increased fear of COVID-19 predicts vaccination willingness 14 months later, even when controlling for several anxious personality traits, infection control perceptions, risks for loved ones, self-rated health, previous infection, media use, and demographic variables. These results show that fear of COVID-19 is a relevant construct to consider for predicting and possibly influencing vaccination willingness. Nonetheless, sensitivity and specificity of fear of COVID-19 to predict vaccination willingness were quite low and only became slightly better when fear of COVID-19 was measured concurrently. This indicates that other potential factors, such as perceived risks of the vaccines, probably also play a role in explaining vaccination willingness.

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