Regional function-structure relationships in lungs of an elastase murine model of emphysema

Changes in lung function and structure were studied using hyperpolarized 3He MRI in an elastase-induced murine model of emphysema. The combined analysis of the apparent diffusion coefficient (ADC) and fractional ventilation ( R) were used to distinguish emphysematous changes and also to develop a model for classifying sections of the lung into diseased and normal. Twelve healthy male BALB/c mice (26 ± 2 g) were randomized into healthy and elastase-induced mice and studied ∼8–11 wk after model induction. ADC and R were measured at a submillimeter planar resolution. Chord length ( L x) data were analyzed from histology samples from the corresponding imaged slices. Logistic regression was applied to estimate the probability that an imaged pixel came from a diseased animal, and bootstrap methods (1,000 samples) were used to compare the regression results for the morphological and imaging results. Multivariate ANOVA (MANOVA) was used to analyze transformed ADC (ADCBC), and R ( RBC) data and also to control for the experiment-wide error rate. MANOVA and ANOVA showed that elastase induced a statistically measureable change in the average transformed L x and ADCBC but not in the average RBC. Marginal mean analysis demonstrated that ADCBC was on average 0.19 [95% confidence interval (CI): 0.16, 0.22] higher in the emphysema group, whereas RBC was on average 0.05 (95% CI: 0.04, 0.06) lower. Logistic regression supported the hypothesis that ADCBC and RBC, together, were better at differentiating normal from diseased tissue than either measurement alone. The odds ratios for ADCBC and RBC were 7.73 (95% CI: 5.23, 11.42) and 9.14 × 10−5 (95% CI: 3.33 × 10−5, 25.06 × 10−5), respectively. Using a 50% probability cutoff, this model classified 70.6% of pixels correctly. The sensitivity and specificity of this model at the 50% cutoff were 74.9% and 65.2%, respectively. The area under the receiver operating characteristic curve was 0.76 (95% CI: 0.74, 0.78). The regression model presented can be used to map MRI data to disease probability maps. These probability maps present a future possibility of using both measurements in a more clinically feasible method of diagnosing this disease.

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Evaluation of factors that predict the success rate of trial of labor after the cesarean section

BMC Pregnancy and Childbirth ◽

10.1186/s12884-021-04004-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yang Mi ◽

Pengfei Qu ◽

Na Guo ◽

Ruimiao Bai ◽

Jiayi Gao ◽

...

Keyword(s):

Logistic Regression ◽

Cesarean Section ◽

Receiver Operating Characteristic Curve ◽

Success Rate ◽

Operating Characteristic ◽

Characteristic Curve ◽

Predictive Ability ◽

Training Set ◽

History Of ◽

Operating Characteristic Curve

Abstract Background For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). Methods All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. Results A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m2, cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. Conclusion The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

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Predictors of thermal increase in magnetic resonance–guided focused ultrasound treatment for essential tremor: histogram analysis of skull density ratio values for 1024 elements

Journal of Neurosurgery ◽

10.3171/2021.5.jns21669 ◽

2021 ◽

pp. 1-6

Author(s):

Ken Iijima ◽

Hajime Yokota ◽

Toshio Yamaguchi ◽

Masayuki Nakano ◽

Takahiro Ouchi ◽

...

Keyword(s):

Logistic Regression ◽

Operating Characteristic ◽

Focused Ultrasound ◽

Characteristic Curve ◽

Density Ratio ◽

Predictive Ability ◽

Curve Analysis ◽

Regression Analyses ◽

Multivariate Logistic Regression ◽

Operating Characteristic Curve

OBJECTIVE Sufficient thermal increase capable of generating thermocoagulation is indispensable for an effective clinical outcome in patients undergoing magnetic resonance–guided focused ultrasound (MRgFUS). The skull density ratio (SDR) is one of the most dominant predictors of thermal increase prior to treatment. However, users currently rely only on the average SDR value (SDRmean) as a screening criterion, although some patients with low SDRmean values can achieve sufficient thermal increase. The present study aimed to examine the numerical distribution of SDR values across 1024 elements to identify more precise predictors of thermal increase during MRgFUS. METHODS The authors retrospectively analyzed the correlations between the skull parameters and the maximum temperature achieved during unilateral ventral intermediate nucleus thalamotomy with MRgFUS in a cohort of 55 patients. In addition, the numerical distribution of SDR values was quantified across 1024 elements by using the skewness, kurtosis, entropy, and uniformity of the SDR histogram. Next, the authors evaluated the correlation between the aforementioned indices and a peak temperature > 55°C by using univariate and multivariate logistic regression analyses. Receiver operating characteristic curve analysis was performed to compare the predictive ability of the indices. The diagnostic performance of significant factors was also assessed. RESULTS The SDR skewness (SDRskewness) was identified as a significant predictor of thermal increase in the univariate and multivariate logistic regression analyses (p < 0.001, p = 0.013). Moreover, the receiver operating characteristic curve analysis indicated that the SDRskewness exhibited a better predictive ability than the SDRmean, with area under the curve values of 0.847 and 0.784, respectively. CONCLUSIONS The SDRskewness is a more accurate predictor of thermal increase than the conventional SDRmean. The authors suggest setting the SDRskewness cutoff value to 0.68. SDRskewness may allow for the inclusion of treatable patients with essential tremor who would have been screened out based on the SDRmean exclusion criterion.

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Machine Learning-based in-hospital Mortality Prediction Models for Patients With Acute Coronary Syndrome

10.21203/rs.3.rs-134944/v1 ◽

2020 ◽

Author(s):

Jun Ke ◽

Yiwei Chen ◽

Xiaoping Wang ◽

Zhiyong Wu ◽

qiongyao Zhang ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forest ◽

Hospital Mortality ◽

Operating Characteristic ◽

Prediction Models ◽

Characteristic Curve ◽

Multivariate Logistic Regression Analysis ◽

Hdl Cholesterol ◽

Coronary Syndrome

Abstract BackgroundThe purpose of this study is to identify the risk factors of in-hospital mortality in patients with acute coronary syndrome (ACS) and to evaluate the performance of traditional regression and machine learning prediction models.MethodsThe data of ACS patients who entered the emergency department of Fujian Provincial Hospital from January 1, 2017 to March 31, 2020 for chest pain were retrospectively collected. The study used univariate and multivariate logistic regression analysis to identify risk factors for in-hospital mortality of ACS patients. The traditional regression and machine learning algorithms were used to develop predictive models, and the sensitivity, specificity, and receiver operating characteristic curve were used to evaluate the performance of each model.ResultsA total of 7810 ACS patients were included in the study, and the in-hospital mortality rate was 1.75%. Multivariate logistic regression analysis found that age and levels of D-dimer, cardiac troponin I, N-terminal pro-B-type natriuretic peptide (NT-proBNP), lactate dehydrogenase (LDH), high-density lipoprotein (HDL) cholesterol, and calcium channel blockers were independent predictors of in-hospital mortality. The study found that the area under the receiver operating characteristic curve of the models developed by logistic regression, gradient boosting decision tree (GBDT), random forest, and support vector machine (SVM) for predicting the risk of in-hospital mortality were 0.963, 0.960, 0.963, and 0.959, respectively. Feature importance evaluation found that NT-proBNP, LDH, and HDL cholesterol were top three variables that contribute the most to the prediction performance of the GBDT model and random forest model.ConclusionsThe predictive model developed using logistic regression, GBDT, random forest, and SVM algorithms can be used to predict the risk of in-hospital death of ACS patients. Based on our findings, we recommend that clinicians focus on monitoring the changes of NT-proBNP, LDH, and HDL cholesterol, as this may improve the clinical outcomes of ACS patients.

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Evaluation of keratoconus progression

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2017-311651 ◽

2018 ◽

Vol 103 (4) ◽

pp. 551-557 ◽

Cited By ~ 6

Author(s):

Mehdi Shajari ◽

Gernot Steinwender ◽

Kim Herrmann ◽

Kate Barbara Kubiak ◽

Ivana Pavlovic ◽

...

Keyword(s):

Logistic Regression ◽

Roc Analysis ◽

Operating Characteristic ◽

Characteristic Curve ◽

Youden Index ◽

Back Surface ◽

Corneal Surface ◽

Maximum Elevation ◽

Index Index ◽

Variance Index

AimTo define variables for the evaluation of keratoconus progression and to determine cut-off values.MethodsIn this retrospective cohort study (2010–2016), 265 eyes of 165 patients diagnosed with keratoconus underwent two Scheimpflug measurements (Pentacam) that took place 1 year apart ±3 months. Variables used for keratoconus detection were evaluated for progression and a correlation analysis was performed. By logistic regression analysis, a keratoconus progression index (KPI) was defined. Receiver-operating characteristic curve (ROC) analysis was performed and Youden Index calculated to determine cut-off values.ResultsVariables used for keratoconus detection showed a weak correlation with each other (eg, correlation r=0.245 between RPImin and Kmax, p<0.001). Therefore, we used parameters that took several variables into consideration (eg, D-index, index of surface variance, index for height asymmetry, KPI). KPI was defined by logistic regression and consisted of a Pachymin coefficient of −0.78 (p=0.001), a maximum elevation of back surface coefficient of 0.27 and coefficient of corneal curvature at the zone 3 mm away from the thinnest point on the posterior corneal surface of −12.44 (both p<0.001). The two variables with the highest Youden Index in the ROC analysis were D-index and KPI: D-index had a cut-off of 0.4175 (70.6% sensitivity) and Youden Index of 0.606. Cut-off for KPI was −0.78196 (84.7% sensitivity) and a Youden Index of 0.747; both 90% specificity.ConclusionsKeratoconus progression should be defined by evaluating parameters that consider several corneal changes; we suggest D-index and KPI to detect progression.

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Accuracy of apparent diffusion coefficients and enhancement ratios on magnetic resonance imaging in differentiating primary cerebral lymphomas from glioblastoma

The Neuroradiology Journal ◽

10.1177/1971400919857556 ◽

2019 ◽

Vol 32 (5) ◽

pp. 328-334 ◽

Cited By ~ 1

Author(s):

Shayan Sirat Maheen Anwar ◽

Mirza Zain Baig ◽

Altaf Ali Laghari ◽

Fatima Mubarak ◽

Muhammad Shahzad Shamim ◽

...

Keyword(s):

Operating Characteristic ◽

Characteristic Curve ◽

Area Under The Curve ◽

Regions Of Interest ◽

Post Contrast ◽

Apparent Diffusion Coefficients ◽

Normal Appearing White Matter ◽

Apparent Diffusion ◽

Adc Values ◽

Patient Subgroups

Background and purposeThis study aimed to determine the accuracy of apparent diffusion coefficient (ADC) and enhancement ratio (ER) in discriminating primary cerebral lymphomas (PCL) and glioblastomas.Materials and methodsCircular regions of interest were randomly placed centrally within the largest solid-enhancing area of all lymphomas and glioblastomas on both post-contrast T1-weighted images and corresponding ADC maps. Regions of interest were also drawn in the contralateral hemisphere to obtain enhancement and ADC values of normal-appearing white matter. This helped us to calculate the ER and ADC ratio.ResultsMean enhancement and ADC (mm2/s) values for PCL were 2220.56 ± 2948.30 and 712.00 ± 137.87, respectively. Mean enhancement and ADC values for glioblastoma were 1537.07 ± 1668.33 and 1037.93 ± 280.52, respectively. Differences in ADC values, ratios and ERs were all statistically significant between the two groups ( p < 0.05). ADC values correctly predicted 71.4% of the lesions as glioblastoma and 83.3% as PCL (area under the curve (AUC) = 0.86 on receiver operating characteristic curve analysis). ADC ratios correctly predicted 85.7% of the lesions as glioblastoma and 100% as PCL (AUC = 0.93). ERs correctly predicted 71.4% of the lesions as glioblastoma and 88.9% as PCL (AUC = 0.92). The combination of ADC ratio and ER correctly predicted 100% tumour type in both patient subgroups.ConclusionsADC values, ADC ratios and ERs may serve as useful variables to distinguish PCL from glioblastoma. The combination of ADC ratio and ER yielded the best results in identification of both patient subgroups.

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Overestimation of the receiver operating characteristic curve for logistic regression

Biometrika ◽

10.1093/biomet/89.2.315 ◽

2002 ◽

Vol 89 (2) ◽

pp. 315-331 ◽

Cited By ~ 45

Author(s):

J. B. Copas

Keyword(s):

Logistic Regression ◽

Receiver Operating Characteristic Curve ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Characteristic Curve ◽

Operating Characteristic Curve ◽

Receiver Operating

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Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer

Frontiers in Oncology ◽

10.3389/fonc.2020.631831 ◽

2021 ◽

Vol 10 ◽

Author(s):

Mou Li ◽

Ling Yang ◽

Yufeng Yue ◽

Jingxu Xu ◽

Chencui Huang ◽

...

Keyword(s):

Prostate Cancer ◽

Diagnostic Performance ◽

Roc Analysis ◽

Operating Characteristic ◽

Characteristic Curve ◽

Training Set ◽

Combined Model ◽

Contrast Enhanced ◽

Apparent Diffusion ◽

Validation Set

ObjectiveTo investigate whether a radiomics model can help to improve the performance of PI-RADS v2.1 in prostate cancer (PCa).MethodsThis was a retrospective analysis of 203 patients with pathologically confirmed PCa or non-PCa between March 2015 and December 2016. Patients were divided into a training set (n = 141) and a validation set (n = 62). The radiomics model (Rad-score) was developed based on multi-parametric MRI including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast enhanced (DCE) imaging. The combined model involving Rad-score and PI-RADS was compared with PI-RADS for the diagnosis of PCa by using the receiver operating characteristic curve (ROC) analysis.ResultsA total of 112 (55.2%) patients had PCa, and 91 (44.8%) patients had benign lesions. For PCa versus non-PCa, the Rad-score had a significantly higher area under the ROC curve (AUC) [0.979 (95% CI, 0.940–0.996)] than PI-RADS [0.905 (0.844–0.948), P = 0.002] in the training set. However, the AUC between them was insignificant in the validation set [0.861 (0.749–0.936) vs. 0.845 (0.731–0.924), P = 0.825]. When Rad-score was added to PI-RADS, the performance of the PI-RADS was significantly improved for the PCa diagnosis (AUC = 0.989, P < 0.001 for the training set and AUC = 0.931, P = 0.038 for the validation set).ConclusionsThe radiomics based on multi-parametric MRI can help to improve the diagnostic performance of PI-RADS v2.1 in PCa.

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A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation

Health Informatics Journal ◽

10.1177/1460458218813600 ◽

2018 ◽

Vol 26 (1) ◽

pp. 34-44 ◽

Cited By ~ 1

Author(s):

Muhammad Faisal ◽

Andy Scally ◽

Robin Howes ◽

Kevin Beatson ◽

Donald Richardson ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Hospital Mortality ◽

Receiver Operating Characteristic Curve ◽

Operating Characteristic ◽

Characteristic Curve ◽

External Validation ◽

Learning Methods ◽

Machine Learning Methods ◽

Operating Characteristic Curve

We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients’ first blood test results and physiological measurements using an external validation approach. We trained and tested each model using data from one hospital ( n = 24,696) and compared the performance of these models in data from another hospital ( n = 13,477). We used two performance measures – the calibration slope and area under the receiver operating characteristic curve. The logistic model performed reasonably well – calibration slope: 0.90, area under the receiver operating characteristic curve: 0.847 compared to the other machine learning methods. Given the complexity of choosing tuning parameters of these methods, the performance of logistic regression with transformations for in-hospital mortality prediction was competitive with the best performing alternative machine learning methods with no evidence of overfitting.

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Predictive Factors of Severe Coronavirus Disease 2019 in Previously Healthy Young Adults: A Single-Center, Retrospective Study

10.21203/rs.3.rs-23496/v1 ◽

2020 ◽

Author(s):

Changzhi Zhou ◽

Zhe Huang ◽

Yi Hu ◽

Shuang Geng ◽

Weijun Tan ◽

...

Keyword(s):

Young Adults ◽

Logistic Regression ◽

Receiver Operating Characteristic ◽

Clinical Outcomes ◽

Operating Characteristic ◽

Characteristic Curve ◽

Tertiary Hospital ◽

Factors Affecting ◽

Severe Group ◽

Receiver Operating

Abstract Background: Several previously healthy young adults have developed Coronavirus Disease 2019 (COVID-19), and a few of them progressed severe COVID-19. However, the factors are not yet determined.Method: We retrospectively analyzed 123 previously healthy young adults diagnosed with COVID-19 from January 2020 to March 2020 in a tertiary hospital in Wuhan. Patients were classified as having mild or severe COVID-19 based on their respiratory rate, SpO2 and PaO2/FiO2 levels. Patients' symptoms, computer tomography (CT) images, preadmission drugs received and the admission serum biochemical examination were compared between the mild and severe group. Significant variables were enrolled logistic regression model to predict the factors affecting disease outcomes. A receiver operating characteristic (ROC) curve was applied to validate the predictive value of predictors.Result: Age; temperature; anorexia; and white blood cell count, neutrophil percentage, platelet count, lymphocyte count, C-reactive protein, aspartate transaminase, creatine kinase, albumin, and fibrinogen values were significantly different between patients with mild and severe COVID-19 (P<0.05). Logistic regression analysis confirmed that lymphopenia (P=0.010) indicated poor clinical outcomes in previously healthy young adults with COVID-19, with area under the receiver operating characteristic curve (AUC) was 0.791(95%CI 0.704–0.877)(P<0.001).Conclusion: For previously healthy young adults with COVID-19, lymphopenia on admission can predict poor clinical outcomes.

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