Prognostic CT Features and Prediction Model of Patients With Primary Hepatocellular Carcinomas Undergoing Partial Hepatectomy

2021 ◽  
Author(s):  
Cuiping Zhou ◽  
Xiaohua Ban ◽  
Huijun Hu ◽  
Qiuxia Yang ◽  
Rong Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Clinical Outcomes ◽  
Partial Hepatectomy ◽  
Logistic Regression Model ◽  
Univariate Analysis ◽  
Binary Logistic Regression ◽  
Roc Curve Analysis ◽  
Hepatocellular Carcinomas ◽  
Ct Features

Abstract Background: Hepatocellular carcinoma (HCC) is the most common primary malignant tumor in the liver. Partial hepatectomy is one of the most effective therapies for HCC but suffer from the high recurrence rate. At present, the studies of association between clinical outcomes and CT features of patients with HCCs undergoing partial hepatectomy are still limited. The purpose of this study is to determine the predictive CT features and establish a model for predicting relapse or metastasis in patients with primary hepatocellular carcinomas (HCCs) undergoing partial hepatectomy.Methods: The clinical data and CT features of 112 patients with histopathologically confirmed primary HCCs were retrospectively reviewed. The clinical outcomes were categorized into two groups according to whether relapse or metastasis occurred within 2 years after partial hepatectomy. The association between clinical outcomes and CT features including tumour size, margin, shape, vascular invasion (VI), arterial phase hyperenhancement, washout appearance, capsule appearance, satellite lesion, involvement segment, cirrhosis, peritumoral enhancement and necrosis was analyzed using univariate analysis and binary logistic regression. Then establish logistic regression model, followed by receiver operating characteristic (ROC) curve analysis.Results: CT features including tumor size, margin, shape, VI, washout appearance, satellite lesion, involvement segment, peritumoral enhancement and necrosis were associated with clinical outcomes, as determined by univariate analysis (P<0.05). Only tumor margin and VI remained independent risk factors in binary logistic regression analysis (OR=6.41 and 10.92 respectively). The logistic regression model was logit(p)=-1.55+1.86 margin +2.39 VI. ROC curve analysis showed that the area under curve of the obtained logistic regression model was 0.887(95% CI：0.827-0.947).Conclusion: Patients with ill-defined margin or VI of HCCs were independent risk predictors of poor clinical outcome after partial hepatectomy. The model as logit(p)= -1.55+1.86 margin +2.39 VI was a good predictor of the clinical outcomes.

scholarly journals Establishing a diagnostic scale of subacute thyroiditis without radioisotope scanning

2020 ◽  
Author(s):  
Zhouyi Xiong ◽  
Chunying Luo ◽  
Li Wang ◽  
Bin Xiong ◽  
Jianneng Wu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Roc Curve ◽  
Logistic Regression Model ◽  
Univariate Analysis ◽  
Binary Logistic Regression ◽  
Subacute Thyroiditis ◽  
Youden Index ◽  
Control Group ◽  
Radioisotope Scanning

Abstract Background: Radioisotope scanning is important to diagnose subacute thyroiditis(SAT), but it’s not always available. So we aim to establish a diagnostic scale for SAT without radioisotope scanning.Methods: The suspected SAT patients hospitalized in Yuebei people's Hospital from January 2012 to December 2016 were selected and divided into study group and control group according to whether they were diagnosed as SAT. The clinical indexes of two groups were collected and the diagnostic scale of SAT was established by using binary logistic regression analysis. The effectiveness of the scale was evaluated by ROC curve. Results: Of 309 patients, 58.25% of patients were confirmed with SAT and the remaining 41.75% of patients were not diagnosed with SAT. After univariate analysis, variables which were considered statistically different(P<0. 05) between the two groups were selected as independent variables and the diagnosis of SAT was taken as dependent variable in the binary logistic regression model. The logistic regression model consisted of 4 variables, each was thyroid tenderness, firm on palpation, increased ESR and elevated thyroid hormone level. The P value of Omnibus tests was≤0. 001 and the Nagelkerke R Square was 0. 915. The diagnostic scoring scale was established with these four variables according to their regression coeffient. The area under the ROC curve for this diagnostic scale was 0. 991(95% confidence interval, 0. 982-0.999). The highest Youden index was 0. 912, the corresponding cut-off point was 7. Internally validation shows a sensitivity of 92. 78% and a specificity of 98.45% of our scale. Conclusions: We established and validated a diagnostic scale for SAT without the need for radioisotope scanning for the first time. It has good application in institutions that do not have radioisotope machines or among pregnant and lactating women.

scholarly journals Establishing a diagnostic scale of subacute thyroiditis without radioisotope scanning

2020 ◽  
Author(s):  
Zhouyi Xiong ◽  
Chunying Luo ◽  
Li Wang ◽  
Bin Xiong ◽  
Jianneng Wu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Roc Curve ◽  
Logistic Regression Model ◽  
Univariate Analysis ◽  
Binary Logistic Regression ◽  
Subacute Thyroiditis ◽  
Youden Index ◽  
Control Group ◽  
Radioisotope Scanning

Abstract Background: Radioisotope scanning is important to diagnose subacute thyroiditis(SAT),but it’s not always available.So we aim to establish a diagnostic scale for SAT without radioisotope scanning. Methods: The suspected SAT patients hospitalized in Yuebei people's Hospital from January 2012 to December 2016 were selected and divided into study group and control group according to whether they were diagnosed as SAT. The clinical indexes of two groups were collected and the diagnostic scale of SAT was established by using binary logistic regression analysis.The effectiveness of the scale was evaluated by ROC curve. Results: Of 261 patients,69% of patients were confirmed with SAT and the remaining 31% of patients were not diagnosed with SAT. After univariate analysis,variables which were considered statistically different( P <0.05) between the two groups were selected as independent variables and the diagnosis of SAT was taken as dependent variable in the binary logistic regression model. The logistic regression model consisted of 4 variables, each was thyroid tenderness,firm on palpation, increased ESR and hyperthyroidism.The P value of Omnibus tests was ≤0.001 and the Nagelkerke R Square was 0.894. The diagnostic scoring scale was established with these four variables according to their regression coeffient.The area under the ROC curve for this diagnostic scale was 0.989(95% confidence interval, 0.978-1.0).The highest Youden index was 0.908,the corresponding cut-off point was 5.5.Internally validation shows a sensitivity of 93.33% and a specificity of 97.53% of our scale. Conclusions: We established and validated a diagnostic scale for SAT without the need for radioisotope scanning for the first time.It has good application in institutions that do not have radioisotope machines or among pregnant and lactating women.

scholarly journals Predictive CT features for the diagnosis of primary pulmonary mucoepidermoid carcinoma: comparison with squamous cell carcinomas and adenocarcinomas

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaohua Ban ◽  
Xinping Shen ◽  
Huijun Hu ◽  
Rong Zhang ◽  
Chuanmiao Xie ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Roc Curve ◽  
Multinomial Logistic Regression ◽  
Univariate Analysis ◽  
Ct Imaging ◽  
Squamous Cell Carcinomas ◽  
Curve Analysis ◽  
Imaging Features ◽  
Roc Curve Analysis ◽  
Ct Features

Abstract Background To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). Materials and methods CT imaging features of 37 patients with primary PMECs, 76 with squamous cell carcinomas (SCCs) and 78 with adenocarcinomas were retrospectively reviewed. The difference of CT features among the PMECs, SCCs and adenocarcinomas was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. Results CT imaging features including tumor size, location, margin, shape, necrosis and degree of enhancement were significant different among the PMECs, SCCs and adenocarcinomas, as determined by univariate analysis (P < 0.05). Only lesion location, shape, margin and degree of enhancement remained independent factors in multinomial logistic regression analysis. ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.805 (95%CI: 0.704–0.906). Conclusion The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI PEROLEHAN KREDIT OLEH PENGUSAHA MIKRO

2018 ◽  
Vol 4 (3) ◽  
Author(s):  
Abdul Azis Safii ◽  
Tri Suwarno
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Large Scale ◽  
Logistic Regression Model ◽  
Banking Sector ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Access To Credit ◽  
The Poor ◽  
Micro Enterprises

Abstract: The number of micro-entrepreneurs and the dominant number of micro enterprises compared to medium and large-scale enterprises in Indonesia are not balanced by the provision of access to credit and venture capital for micro businesses. This resulted in a micro-sector sector identical to the poor being vulnerable to exploitation by moneylenders who exploit the difficulties of micro entrepreneurs accessing credit from the banking sector. This study examines the factors that determine the accessibility of credit by micro entrepreneur in Bojonegoro regency. A total sum of 270 micro entrepreneurs who have applied for banking loan were sampled from the study area. With an binary logistic regression model the research resulting that education, skill on entrepreneur, and monthly net profits generated by the microenterprise are significant in determining the accessibility of microcredit. Keywords: micro entrepreneur, microcredit, credit accessibility Abstrak: Perkembangan jumlah pengusaha mikro serta dominannya jumlah usaha mikro dibandingkan dengan usaha menengah dan usaha besar di Indonesia, tidak diimbingi dengan penyediaan akses kredit dan modal usaha bagi para pelaku usaha mikro. Hal tersebut mengakibatkan sektor usaha mikro yang identik dengan masyarakat miskin rentan dieksploitasi oleh rentenir yang memanfaatkan sulitnya para pengusaha mikro mengakses kredit dari sektor perbankan. Penelitian ini menggunakan data primer yang di ambil langsung dari pengusaha mikro dengan teknik kuesioner. Analisis data dengan metode binary logistic regression mendapatkan hasil variabel yang berpengaruh signifikan terhadap akses kredit para pengusaha mikro adalah variabel usia pengusaha, laba bersih usaha tiap bulan, dan jumlah karyawan yang di pekerjakan. Kata kunci : usaha mikro, microcredit, akses kredit

scholarly journals Early Plasma Osmolality Levels and Clinical Outcomes in Children Admitted to the Pediatric Intensive Care Unit: A Single-Center Cohort Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Huabin Wang ◽  
Zhongyuan He ◽  
Jiahong Li ◽  
Chao Lin ◽  
Huan Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Regression Model ◽  
Clinical Outcomes ◽  
Poor Prognosis ◽  
Logistic Regression Model ◽  
Plasma Osmolality ◽  
Pediatric Intensive Care ◽  
Linear Spline

Objective: Identifying high-risk children with a poor prognosis in pediatric intensive care units (PICUs) is critical. The aim of this study was to assess the predictive value of early plasma osmolality levels in determining the clinical outcomes of children in PICUs.Methods: We retrospectively assessed critically ill children in a pediatric intensive care database. The locally weighted-regression scatter-plot smoothing (LOWESS) method was used to explore the approximate relationship between plasma osmolality and in-hospital mortality. Linear spline functions and stepwise expansion models were applied in conjunction with a multivariate logistic regression to further analyze this relationship. A subgroup analysis by age and complications was performed.Results: In total, 5,620 pediatric patients were included in this study. An approximately “U”-shaped relationship between plasma osmolality and mortality was detected using LOWESS. In the logistic regression model using a linear spline function, plasma osmolality ≥ 290 mmol/L was significantly associated with in-hospital mortality [odds ratio (OR) 1.020, 95% confidence interval (CI) 1.010–1.031], while plasma osmolality &lt;290 mmol/L was not significantly associated with in-hospital mortality (OR 0.990, 95% CI 0.966–1.014). In the logistic regression model with plasma osmolality as a tri-categorical variable, only high osmolality was significantly associated with in-hospital mortality (OR 1.90, 95% CI 1.38–2.64), whereas low osmolality was not associated with in-hospital mortality (OR 1.28, 95% CI 0.84–1.94). The interactions between plasma osmolality and age or complications were not significant.Conclusion: High osmolality, rather than low osmolality, can predict a poor prognosis in children in PICUs.

scholarly journals ESTIMATION OF THE BINARY LOGISTIC REGRESSION MODEL PARAMETER USING BOOTSTRAP RE-SAMPLING

2018 ◽  
Vol 48 (3) ◽  
pp. 199-204 ◽  
Author(s):  
R. LI ◽  
J. ZHOU ◽  
L. WANG
Keyword(s):  
Logistic Regression ◽  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Parametric Bootstrap ◽  
Binary Logistic Regression Model ◽  
Bayesian Bootstrap ◽  
Non Parametric

In this paper, the non-parametric bootstrap and non-parametric Bayesian bootstrap methods are applied for parameter estimation in the binary logistic regression model. A real data study and a simulation study are conducted to compare the Nonparametric bootstrap, Non-parametric Bayesian bootstrap and the maximum likelihood methods. Study results shows that three methods are all effective ways for parameter estimation in the binary logistic regression model. In small sample case, the non-parametric Bayesian bootstrap method performs relatively better than the non-parametric bootstrap and the maximum likelihood method for parameter estimation in the binary logistic regression model.

Die Flexion der Indefinita jemand und niemand

2021 ◽  
Vol 49 (2) ◽  
pp. 209-243
Author(s):  
Linnéa Weitkamp
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Influencing Factors ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Corpus Study ◽  
Frequent Use ◽  
Current Variation ◽  
Reference Corpus

Abstract This article investigates the inflection of the German indefinite pronouns jemand and niemand in the accusative and dative. The pronouns are used both with inflectional suffix (jemanden/jemandem, niemanden/niemandem) and without (jemand, niemand) and are thus an example of current variation in contemporary German. The grammars take an unusually liberal stance and describe both forms as correct, partially even with preference to the uninflected form. A corpus study which examines conceptually written data of the DeReKo (German reference corpus) and conceptually oral data of the DECOW16B (German web corpus), shows that over 90 % of occurrences are inflected. But almost 10 % of uninflected forms show that these formations are no arbitrary errors either. To find out what influences the presence or absence of the inflectional ending, a binary logistic regression model was calculated. The following factors proved to be significant influencing factors for inflection: the degree of formality (DeReKo vs. DECOW16B), the lexeme (jemand vs. niemand), the case (acc vs. dat), government by preposition vs. government by verb and the following nominalized adjective (jemand anderen). With regard to the different inflectional suffixes, the frequent use of -en in the dative stood out in particular. Although this form is classified as erroneous in all grammars, almost 30 % of the dative occurrences in informal DECOW16B data are formed in this way.

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