Efficacy of logistic regression model based on multiparametric ultrasound in assessment of cervical lymphadenopathy - a retrospective study

2021 ◽  
Author(s):  
Dongyan Cai ◽  
Size Wu
Keyword(s):  
Logistic Regression ◽  
Lymph Nodes ◽  
Regression Model ◽  
Predictive Value ◽  
Regression Models ◽  
Predictive Accuracy ◽  
Duplex Ultrasound ◽  
Cervical Lymph Nodes ◽  
Sensitivity Specificity ◽  
Multiparametric Ultrasound

Objectives: To investigate whether a multiparametric ultrasound (MPUS) diagnostic model improves differential diagnosis of benign and malignant cervical lymph nodes. Methods: MPUS evaluation was performed on 87 lesions in 86 patients, and related characteristics and parameters of the patients and lesions were studied and logistic regression models based on the MPUS characteristics of cervical lymph nodes were built. A receiver operating characteristic curve and area under the curve (AUC) were built for the evaluation of diagnostic performances. Results: Of the 87 lesions in 86 patients, there were 31 benign and 56 malignant lesions. Regression models for Duplex ultrasound and MPUS were established. The Duplex ultrasound regression model showed a sensitivity, specificity, positive predictive value and negative predictive value of 94.4, 61.3, 86.3 and 80.9%, respectively. The predictive accuracy was 82.4%, and the AUC was 0.861. The MPUS regression model showed a sensitivity, specificity, positive predictive value and negative predictive value of 98.1, 61.3, 81.5 and 95.0%, respectively. The predictive accuracy was 84.7%, and the AUC was 0.894. The differences in AUCs between the Duplex ultrasound model and MPUS model, ultrasound model and ultrasonic elastography (UE), and Duplex ultrasound and UE were not significant (all p > 0.05); the differences in AUCs between the MPUS model and Duplex ultrasound, Duplex ultrasound model and Duplex ultrasound, and MPUS model and UE were significant (all p < 0.05). Conclusions: The Duplex ultrasound and MPUS models achieve significantly higher diagnostic performance for differentiating between benign and malignant cervical lymph nodes.

Role of Clinical Palpation in Evaluation of Cervical Lymph Node in Oral Squamous Cell Carcinoma

2013 ◽  
Vol 1 (2) ◽  
pp. 02-06
Author(s):  
SM Anwar Sadat ◽  
Sufia Nasrin Rita ◽  
Shoma Banik ◽  
Md Nazmul Hasan Khandker ◽  
Md Mahfuz Hossain ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Lymph Node ◽  
Oral Squamous Cell Carcinoma ◽  
Lymph Nodes ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Predictive Value ◽  
Cervical Lymph Node ◽  
Betel Quid ◽  
Cervical Lymph Nodes

A cross sectional study of 29 cases of oral squamous cell carcinoma with or without  cervical lymph node metastasis was done among Bangladeshi patients from January 2006 to December 2007. Majority of the study subjects (34.5%) belonged to the age group of 40-49 years. 58.6% of the study subjects were male, while remaining 41.4% of them were female. 51.7% of the lesions were located in the alveolar ridge where the other common sites were buccal mucosa (27.6%) and retro molar area (13.8%). Half of the study subjects (51.7%) were habituated to betel quid chewing followed by 37.9% and 10.3% were habituated to smoking and betel quid-smoking respectively. Grade I lesions was most prevalent (75.9%) in the study subjects.  Majority of cases presented with Stage IV lesions (55.2%). The sensitivity, specificity, positive predictive value, negative predictive value & accuracy of clinical palpation method for determining metastatic cervical lymph nodes were 93.33%, 64.29%, 73.68%, 90% and 79.3% respectively. Careful and repeated clinical palpation plays important role in evaluation of cervical lymph nodes though several modern techniques may help additionally in the management of oral cancer.DOI: http://dx.doi.org/10.3329/updcj.v1i2.13978 Update Dent. Coll. j. 2011: 1(2): 02-06

scholarly journals Analysis models for variables associated with breastfeeding duration

2013 ◽  
Vol 31 (3) ◽  
pp. 306-314 ◽  
Author(s):  
Edson Theodoro dos S. Neto ◽  
Eliana Zandonade ◽  
Adauto Oliveira Emmerich
Keyword(s):  
Logistic Regression ◽  
Protective Factors ◽  
Regression Model ◽  
Odds Ratio ◽  
Regression Models ◽  
Adjusted Odds Ratio ◽  
Cox Regression ◽  
Bottle Feeding ◽  
Breastfeeding Duration ◽  
Factors Associated

OBJECTIVE To analyze the factors associated with breastfeeding duration by two statistical models. METHODS A population-based cohort study was conducted with 86 mothers and newborns from two areas primary covered by the National Health System, with high rates of infant mortality in Vitória, Espírito Santo, Brazil. During 30 months, 67 (78%) children and mothers were visited seven times at home by trained interviewers, who filled out survey forms. Data on food and sucking habits, socioeconomic and maternal characteristics were collected. Variables were analyzed by Cox regression models, considering duration of breastfeeding as the dependent variable, and logistic regression (dependent variables, was the presence of a breastfeeding child in different post-natal ages). RESULTS In the logistic regression model, the pacifier sucking (adjusted Odds Ratio: 3.4; 95%CI 1.2-9.55) and bottle feeding (adjusted Odds Ratio: 4.4; 95%CI 1.6-12.1) increased the chance of weaning a child before one year of age. Variables associated to breastfeeding duration in the Cox regression model were: pacifier sucking (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.3) and bottle feeding (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.5). However, protective factors (maternal age and family income) differed between both models. CONCLUSIONS Risk and protective factors associated with cessation of breastfeeding may be analyzed by different models of statistical regression. Cox Regression Models are adequate to analyze such factors in longitudinal studies.

scholarly journals Identification of the most specific markers to differentiate primary pulmonary carcinoma from metastatic gastrointestinal carcinoma to the lung

2022 ◽  
Vol 17 (1) ◽  
Author(s):  
Bachar Alabdullah ◽  
Amir Hadji-Ashrafy
Keyword(s):  
Gastrointestinal Tract ◽  
Predictive Value ◽  
Regression Models ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Multivariate Models ◽  
Gastrointestinal Carcinoma ◽  
Lung Tumours ◽  
Napsin A ◽  
Sensitivity Specificity

Abstract Background A number of biomarkers have the potential of differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract, however, a standardised panel for that purpose does not exist yet. We aimed to identify the smallest panel that is most sensitive and specific at differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract. Methods A total of 170 samples were collected, including 140 primary and 30 non-primary lung tumours and staining for CK7, Napsin-A, TTF1, CK20, CDX2, and SATB2 was performed via tissue microarray. The data was then analysed using univariate regression models and a combination of multivariate regression models and Receiver Operating Characteristic (ROC) curves. Results Univariate regression models confirmed the 6 biomarkers’ ability to independently predict the primary outcome (p < 0.001). Multivariate models of 2-biomarker combinations identified 11 combinations with statistically significant odds ratios (ORs) (p < 0.05), of which TTF1/CDX2 had the highest area under the curve (AUC) (0.983, 0.960–1.000 95% CI). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 75.7, 100, 100, and 37.5% respectively. Multivariate models of 3-biomarker combinations identified 4 combinations with statistically significant ORs (p < 0.05), of which CK7/CK20/SATB2 had the highest AUC (0.965, 0.930–1.000 95% CI). The sensitivity, specificity, PPV, and NPV were 85.1, 100, 100, and 41.7% respectively. Multivariate models of 4-biomarker combinations did not identify any combinations with statistically significant ORs (p < 0.05). Conclusions The analysis identified the combination of CK7/CK20/SATB2 to be the smallest panel with the highest sensitivity (85.1%) and specificity (100%) for predicting tumour origin with an ROC AUC of 0.965 (p < 0.001; SE: 0.018, 0.930–1.000 95% CI).

Relevance of Brain Regions’ Eloquence Assessment in Patients With a Large Ischemic Core Treated With Mechanical Thrombectomy

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012863
Author(s):  
Basile Kerleroux ◽  
Joseph Benzakoun ◽  
Kévin Janot ◽  
Cyril Dargazanli ◽  
Dimitri Daly Eraya ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Mechanical Thrombectomy ◽  
Predictive Accuracy ◽  
Unmet Need ◽  
C Statistic ◽  
Ischemic Core ◽  
Functional Relevance

ObjectiveIndividualized patient selection for mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large ischemic core (LIC) at baseline is an unmet need.We tested the hypothesis, that assessing the functional relevance of both the infarcted and hypo-perfused brain tissue, would improve the selection framework of patients with LIC for MT.MethodsMulticenter, retrospective, study of adult with LIC (ischemic core volume > 70ml on MR-DWI), with MRI perfusion, treated with MT or best medical management (BMM).Primary outcome was 3-month modified-Rankin-Scale (mRS), favourable if 0-3. Global and regional-eloquence-based core-perfusion mismatch ratios were derived. The predictive accuracy for clinical outcome of eloquent regions involvement was compared in multivariable and bootstrap-random-forest models.ResultsA total of 138 patients with baseline LIC were included (MT n=96 or BMM n=42; mean age±SD, 72.4±14.4years; 34.1% females; mRS=0-3: 45.1%). Mean core and critically-hypo-perfused volume were 100.4ml±36.3ml and 157.6±56.2ml respectively and did not differ between groups. Models considering the functional relevance of the infarct location showed a better accuracy for the prediction of mRS=0-3 with a c-Statistic of 0.76 and 0.83 for logistic regression model and bootstrap-random-forest testing sets respectively. In these models, the interaction between treatment effect of MT and the mismatch was significant (p=0.04). In comparison in the logistic regression model disregarding functional eloquence the c-Statistic was 0.67 and the interaction between MT and the mismatch was insignificant.ConclusionConsidering functional eloquence of hypo-perfused tissue in patients with a large infarct core at baseline allows for a more precise estimation of treatment expected benefit.

Data Mining Medical Information

2011 ◽  
pp. 92-107
Author(s):  
Thomas Chesney ◽  
Kay Penny ◽  
Peter Oakley ◽  
Simon Davies ◽  
David Chesney ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Medical Information ◽  
Predictive Accuracy ◽  
Outcome Analysis ◽  
Audit Data ◽  
Patient Will ◽  
Artificial Neural Network Ann ◽  
Dissemination Of Results

Trauma audit is intended to develop effective care for injured patients through process and outcome analysis, and dissemination of results. The system records injury details such as the patient’s sex and age, the mechanism of the injury, various measures of the severity of the injury, initial management and subsequent management interventions, and the outcome of the treatment including whether the patient lived or died. Ten years’ worth of trauma audit data from one hospital are modelled as an Artificial Neural Network (ANN) in order to compare the results with a more traditional logistic regression analysis. The output was set to be the probability that a patient will die. The ANN models and the logistic regression model achieve roughly the same predictive accuracy, although the ANNs are more difficult to interpret than the logistic regression model, and neither logistic regression nor the ANNs are particularly good at predicting death. For these reasons, ANNs are not seen as an appropriate tool to analyse trauma audit data. Results do suggest, however, the usefulness of using both traditional and non-traditional analysis techniques together and of including as many factors in the analysis as possible.

Data Mining Medical Information

2011 ◽  
pp. 2218-2231
Author(s):  
Thomas Chesney ◽  
Kay Penny ◽  
Peter Oakley ◽  
Simon Davies ◽  
David Chesney ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Medical Information ◽  
Predictive Accuracy ◽  
Outcome Analysis ◽  
Audit Data ◽  
Patient Will ◽  
Artificial Neural Network Ann ◽  
Dissemination Of Results

Trauma audit is intended to develop effective care for injured patients through process and outcome analysis, and dissemination of results. The system records injury details such as the patient’s sex and age, the mechanism of the injury, various measures of the severity of the injury, initial management and subsequent management interventions, and the outcome of the treatment including whether the patient lived or died. Ten years’ worth of trauma audit data from one hospital are modelled as an Artificial Neural Network (ANN) in order to compare the results with a more traditional logistic regression analysis. The output was set to be the probability that a patient will die. The ANN models and the logistic regression model achieve roughly the same predictive accuracy, although the ANNs are more difficult to interpret than the logistic regression model, and neither logistic regression nor the ANNs are particularly good at predicting death. For these reasons, ANNs are not seen as an appropriate tool to analyse trauma audit data. Results do suggest, however, the usefulness of using both traditional and non-traditional analysis techniques together and of including as many factors in the analysis as possible.

scholarly journals Aplicación de modelos de regresión logística en metodología observacional: modalidades de competición en la iniciación al fútbol.

2015 ◽  
Vol 32 (1) ◽  
pp. 288 ◽  
Author(s):  
Daniel Lapresa ◽  
Javier Arana ◽  
M.Teresa Anguera ◽  
J.Ignacio Pérez-Castellanos ◽  
Mario Amatria
Keyword(s):  
Physical Activity ◽  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Multiple Logistic Regression ◽  
Observational Methodology ◽  
Logistic Regression Models ◽  
Game Format ◽  
Simple Logistic

This study shows how simple and multiple logistic regression can be used in observational methodology and more specifically, in the fields of physical activity and sport. We demonstrate this in a study designed to determine whether three-a-side futsal or five-a-side futsal is more suited to the needs and potential of children aged 6-to-8 years. We constructed a multiple logistic regression model to analyze use of space (depth of play) and three simple logistic regression models to determine which game format is more likely to potentiate effective technical and tactical performance.

Ordinal regression model and the linear regression model were superior to the logistic regression models

2006 ◽  
Vol 59 (5) ◽  
pp. 448-456 ◽  
Author(s):  
Colleen M. Norris ◽  
William A. Ghali ◽  
L. Duncan Saunders ◽  
Rollin Brant ◽  
Diane Galbraith ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Ordinal Regression ◽  
Logistic Regression Models ◽  
Ordinal Regression Model

scholarly journals “A Four-Leaf Clover” Sign for Rapid Identification of Coagulase-Negative Staphylococci by Gram Staining From Positive Blood Culture: A Cross-Sectional Study

2020 ◽  
Author(s):  
Takahiro Matsuo ◽  
Kuniyoshi Hayashi ◽  
Aki Sakurai ◽  
Masumi Suzuki Shimizu ◽  
Masaya Morimoto ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Blood Culture ◽  
Positive Predictive Value ◽  
Positive Blood Culture ◽  
Predictive Value ◽  
Blood Cultures ◽  
Coagulase Negative Staphylococci ◽  
Gram Staining ◽  
Sensitivity Specificity

Abstract Background: Coagulase-negative staphylococci (CoNS) are one of the most common contaminant microorganisms isolated from blood cultures. Few studies exploring the use of Gram staining to distinguish between Staphylococcus aureus (SA) and CoNS have been reported. Here, this study aimed to explore whether morphological features of Gram staining could identify SA or CoNS.Methods: This study was conducted at St. Luke’s International Hospital from November 2016 to September 2017. The positive blood cultures for which the Gram staining showed gram-positive cocci (GPC) in clusters were included in our study. The direct smear of Gram staining obtained from positive blood culture bottles were examined within 24 hours of positivity. We have identified and characterized the following two signs: “four-leaf clover (FLC)” if 4 GPC gathered like a planar four-leaf clover and “grapes” if the GPC gathered like grapes in a three-dimensional form. The number of fields with FLC and grapes signs in 10 fields per slide with ×1,000 power was counted, and the results in a total of 20 fields with ×1,000 power were combined. We performed a logistic regression analysis to assess whether these signs could serve as factors distinguishing between SA and CoNS. The predictive ability of these signs was evaluated based on the sensitivity, specificity, positive predictive value, and negative predictive value for CoNS via receiver operating curve analysis.Results: In total, 106 blood cultures for which Gram staining showed GPC in clusters were examined; 46 (43%) were SA, and 60 (57%) were CoNS samples. The result of multivariate logistic regression analysis showed that the FLC sign was a statistically significant marker of CoNS with an odds ratio of 1.31 (95 % confidential interval (CI): 1.07–1.61, p<0.05). In aerobic bottles, sensitivity, specificity, positive predictive value, and negative predictive value for CoNS were 0.67, 0.91, 0.92, and 0.65, respectively, and the value of area under the curve was 0.79 (95% CI: 0.67–0.91).Conclusions: To our knowledge, this is the first study to show that the FLC could be a rapid and useful indicator to identify CoNS in aerobic bottles. Thus, the presence of FLC sings could help clinicians to suspect the possibility of CoNS before the final identification by cultures.

scholarly journals Validation Study of Logistic Regression in Classifying Primary Vitreoretinal Lymphoma Vs. Uveitis By IL-6 and IL-10 Levels

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2971-2971
Author(s):  
David Kuo ◽  
Maggie Wei ◽  
Jared Knickelbein ◽  
Karen Armbrust ◽  
Ian Yeung ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Conflicts Of Interest ◽  
Training Data ◽  
Youden Index ◽  
Primary Vitreoretinal Lymphoma ◽  
Cytokine Analysis ◽  
Vitreoretinal Lymphoma ◽  
Sensitivity Specificity

Abstract Background/Aim: The aim of this study was to assess the utility of intraocular IL-10 and IL-6 analysis by logistic regression in classifying primary vitreoretinal lymphoma (PVRL) vs. uveitis using a logistic regression model trained on a single-center retrospective cohort as well as the previously published ISOLD score compared against the IL-10/IL-6 ratio. Methods: Patient diagnoses of PVRL vs. uveitis and associated aqueous and/or vitreous IL-6 and IL-10 levels were retrospectively collected. From this data, cytokine levels were compared between diagnoses with the Mann-Whitney U test and a logistic regression model was developed to classify PVRL vs. uveitis from aqueous and vitreous IL-6 and IL-10 by nested cross-validation. ROC curves were plotted and AUCs were calculated for the IL-10/IL-6 ratio, ISOLD score, and our logistic regression model. Optimal cut-offs for each classifier were determined by the maximal Youden index; and sensitivity, specificity, PPV, and NPV were determined for each cut-off. Results: 79 lymphoma (10 aqueous, 69 vitreous) and 84 uveitis patients (19 aqueous, 65 vitreous) between 10/5/1999 and 9/16/2015 were included in the study. IL-6 was higher in uveitis vs. lymphoma patients while IL-10 was higher in lymphoma vs. uveitis patients (p <0.01 for all comparisons). For vitreous samples, our logistic regression model achieved an AUC of 98.3%, while ISOLD achieved an AUC of 97.8%, and the IL-10/IL-6 ratio achieved an AUC of 96.3%. The optimal cut-offs for our logistic regression model, ISOLD, and the IL-10/IL-6 ratio achieved sensitivity/specificity of 92.7%/100%, 94.2%/96.9%, 94.2%/95.3% respectively, corresponding to PPV/NPV of 100%/92.9%, 97%/94%, and 95.6%/93.9% respectively. For aqueous samples, all three classifiers achieved 100% AUC with 100% sensitivity/specificity. Odds ratios of PVRL vs. uveitis were 0.981 (aqueous) and 0.992 (vitreous) for IL-6 and 1.030 (aqueous) and 1.060 (vitreous) for IL-10 according to our logistic regression model. Conclusion: In this study, logistic regression, as demonstrated by our model and the ISOLD score, showed strong classification performance and generalizability with high sensitivity and specificity. These results, in addition to logistic regression's ability to further improve with more training data suggest a promising step forward in intraocular cytokine analysis for the early diagnosis of primary vitreoretinal lymphoma. Additional validation studies, especially with cohorts that have proven challenging for the IL-10/IL-6 ratio, would further elucidate the strengths and weakness of this approach. Disclosures No relevant conflicts of interest to declare.

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