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A logistic regression model to predict long-term survival for borderline resectable pancreatic cancer patients with upfront surgery

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CANCER IMAGING 2025年第1期25卷 1-13页

作者： Huang, Jin-Can Lyu, Shao-Cheng Pan, Bing Wang, Han-Xuan Ma, You-Wei Jiang, Tao He, Qiang Lang, Ren Capital Med Univ Beijing Chao Yang Hosp Hepatobiliary Pancreas & Spleen Surg Dept 8 Gongren Tiyuchang Nanlu Beijing 100020 Peoples R China

BackgroundThe machine learning model, which has been widely applied in prognosis assessment, can comprehensively evaluate patient status for accurate prognosis classification. There still has been a debate about which predictive strategy is better in patients with borderline resectable pancreatic cancer (BRPC). In the present study, we establish a logistic regression model, aiming to predict long-term survival and identify related prognostic factors in patients with BRPC who underwent upfront surgery. MethodsMedical records of patients with BRPC who underwent upfront surgery with portal vein resection and reconstruction from Jan. 2011 to Dec. 2020 were reviewed. Based on postoperative overall survival (OS), patients were divided into the short-term group (<= 2 years) and the long-term group (> 2 years). Univariate and multivariate analyses were performed to compare perioperative variables and long-term prognoses between groups to identify related independent prognostic factors. All patients are randomly divided into the training set and the validation set at a 7:3 ratio. The logistic regression model was established and evaluated for accuracy through the above variables in the training set and the validation set, respectively, and was visualized by Nomograms. Meanwhile, the model was further verified and compared for accuracy, the area under the curve (AUC) of the receiver operating characteristic curves (ROC), and calibration analysis. Then, we plotted and sorted perioperative variables by SHAP value to identify the most important variables. The first 4 most important variables were compared with the above independent prognostic factors. Finally, other models including support vector machines (SVM), random forest, decision tree, and XGBoost were also constructed using the above 4 variables. 10-fold stratified cross-validation and the AUC of ROC were performed to compare accuracy between models. Results104 patients were enrolled in the study, and the median OS was 1

关键词： Borderline resectable pancreatic cancer Risk factor Upfront surgery Nomogram logistic regression model Machine learning

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A new estimator for the multicollinear logistic regression model

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STATISTICAL PAPERS 2025年第3期66卷 1-19页

作者： Cetinkaya, Merve Kandemir Revenue Adm Tax Audit & Coordinat Directorate TR-06450 Ankara Turkiye

logistic regression model applications have become very popular in the analysis of social, economy, finance, agriculture, health economics and medical science in recent years. However, if there is a high degree of relationship between the independent variables, the problem of multicollinearity arises in this model. In this paper, we introduce a new Jackknifed two-parameter estimator (JTPE) and the two-parameter estimator (TPE) for the logistic regression model by unifying the JTPE of Kandemir & Ccedil;etinkaya and Ka & ccedil;ranlar (Biased estimators and their applications in generalized linear models. Thesis, 2024). We examined bias vectors and matrix mean squared error (MMSE) of the TPE and the JTPE. The generalization of some estimation methods for ridge and Liu parameters in logistic regression model are provided. Also, the superiority of JTPE is assessed by the simulated mean squared error (SMSE) via Monte Carlo simulation study where the response follows logistic regression model. We finally consider real data applications. The proposed estimators are compared and interpreted.

关键词： logistic regression model Jackknifed estimator Multicollinearity Two-parameter estimator Monte Carlo

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Predicting the recurrence of facial synkinesis after epineurectomy of facial nerve trunk using logistic regression model

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JOURNAL OF PLASTIC RECONSTRUCTIVE AND AESTHETIC SURGERY 2025年 101卷 119-125页

作者： Li, Yihua Shen, Yiman Wang, Haopeng Zhang, Zhongding Wang, Baimiao Cai, Xiaomin Li, Shiting Shanghai Jiao Tong Univ Med Shanghai Xinhua Hosp Dept Neurosurg Shanghai 200020 Peoples R China Shanghai Jiao Tong Univ Cranial Nerve Dis Ctr Shanghai Peoples R China

Background: This study aimed to investigate the risk factors affecting epineurectomy of the facial nerve trunk for facial synkinesis and use them to establish a prediction model to assess the recurrence of post-operative facial synkinesis. Methods: A total of 68 patients with synkinesis after facial paralysis were enrolled in this study. They were randomized to the training and testing sets. All patients underwent standard surgical procedures and their clinical data were collected and analyzed. The condition of facial synkinesis was evaluated at 12 months after the operation. Univariable and multivariable logistic regression analysis was performed to identify independent risk factors and establish a model and related nomogram to predict the recurrence of post-operative facial synkinesis. Area under receiver operating characteristic curve (AUC) and calibration curves were employed to assess the predictive accuracy of the nomogram. Results: Multivariate logistic regression analysis indicated that older age and higher preoperative score of synkinesis may be the potential factors for the recurrence of post-operative facial synkinesis at 12 months. The prediction model showed a good discrimination with mean AUC of 5-fold cross-validation of 0.781. The accuracy of the nomogram predicting the recurrence in the testing set reached 78.57%, respectively. Bias-corrected curve revealed a strong consistency between the actual observation and prediction. Conclusion: The study illustrated that the proposed logistic regression model based on the age and preoperative score of synkinesis of 2 potential factors could be a promising tool to predict the recurrence of facial synkinesis after the surgery. (c) 2024 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Facial synkinesis logistic regression model Prediction nomogram Risk factors Surgical treatment

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logistic regression model can reduce unnecessary artificial liver support in hepatitis B virus-associated acute-on-chronic liver failure: decision curve analysis

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2016年第1期16卷 1-7页

作者： Qin, Gang Bian, Zhao-Lian Shen, Yi Zhang, Lei Zhu, Xiao-Hong Liu, Yan-Mei Shao, Jian-Guo Nantong Univ Nantong Peoples Hosp 3 Ctr Liver Dis Nantong Jiangsu Peoples R China Nantong Univ Dept Epidemiol & Med Stat Sch Publ Hlth Nantong Jiangsu Peoples R China Monash Univ Fac Med Nursing & Hlth Sci Melbourne Vic 3004 Australia

Background: Several models have been proposed to predict the short-term outcome of acute-on-chronic liver failure (ACLF) after treatment. We aimed to determine whether better decisions for artificial liver support system (ALSS) treatment could be made with a model than without, through decision curve analysis (DCA). Methods: The medical profiles of a cohort of 232 patients with hepatitis B virus (HBV)-associated ACLF were retrospectively analyzed to explore the role of plasma prothrombin activity (PTA), model for end-stage liver disease (MELD) and logistic regression model (LRM) in identifying patients who could benefit from ALSS. The accuracy and reliability of PTA, MELD and LRM were evaluated with previously reported cutoffs. DCA was performed to evaluate the clinical role of these models in predicting the treatment outcome. Results: With the cut-off value of 0.2, LRM had sensitivity of 92.6 %, specificity of 42.3 % and an area under the receiving operating characteristic curve (AUC) of 0.68, which showed superior discrimination over PTA and MELD. DCA revealed that the LRM-guided ALSS treatment was superior over other strategies including "treating all" and MELD-guided therapy, for the midrange threshold probabilities of 16 to 64 %. Conclusions: The use of LRM-guided ALSS treatment could increase both the accuracy and efficiency of this procedure, allowing the avoidance of unnecessary ALSS.

关键词： model for end-stage liver disease logistic regression model Acute-on-chronic liver failure Hepatitis B virus Decision curve analysis

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logistic regression model: The Effect of Endogenous Magnesium Level on the Concentration of Magnesium Drugs in a Bioequivalence Study

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PHARMACEUTICAL CHEMISTRY JOURNAL 2023年第5期57卷 621-626页

作者： Eremenko, N. N. Shikh, E. V. Ramenskaya, G. V. Sechenov Univ Minist Hlth Russia 8-2 Trubetskaya St Moscow 119991 Russia Minist Hlth Russian Federat Sci Ctr Expert Evaluat Med Prod 8-2 Petrovskii Blvd Moscow 127051 Russia

The comparative bioavailability and bioequivalence of drugs containing magnesium citrate were studied. logistic regression models were constructed to assess the effect of the background magnesium content on the magnesium concentration after use of the drugs. The following pharmacokinetic parameters were evaluated: the maximum concentration of magnesium in whole blood of volunteers (C-max), the time to reach the maximum concentration of magnesium in whole blood of volunteers (t(max)), and the area under the plasma-concentration(time pharmacokinetic curve (AUC((0-t))). Arecalculation taking into account the endogenous magnesium level was used for a correct calculation of the pharmacokinetic parameters. The studied drugs were found to be bioequivalent with calculated 90% confidence intervals falling within the acceptable range of 80 - 125%. logistic regression models were constructed where the concentrations after taking the studied drugs were chosen as the dependent variables;the background magnesium contents, the influential factor. The correlation between the background value and C(max )in volunteers that took the studied and reference drugs was noticeable and positive (on the Chaddock scale) at 0.650 and 0.667, respectively, and statistically significant (p < 0.006 and p < 0.005). The resulting model took into account 42.3% and 44.4%, respectively, of the factors determining changes in the C-max level. It was concluded that construction of a logistic regression model of the relationship between the background magnesium concentration and the concentration after taking the studied drugs could be considered one possible predictor that can be used to assess the bioavailability of drugs containing magnesium salts.

关键词： endogenous level magnesium concentration bioavailability logistic regression model

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Forest cover dynamics analysis and prediction modeling using logistic regression model

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ECOLOGICAL INDICATORS 2014年 45卷 444-455页

作者： Kumar, Rakesh Nandy, S. Agarwal, Reshu Kushwaha, S. P. S. Indian Space Res Org Indian Inst Remote Sensing Forestry & Ecol Dept Dehra Dun 248001 Uttar Pradesh India

Forest cover conversion and depletion are of global concern due to their role in global warming. The present study attempted to study the forest cover dynamics and prediction modeling in Bhanupratappur Forest Division of Kanker district in Chhattisgarh province of India. The study aims to examine and analyze the various explanatory variables associated with forest conversion process and predict forest cover change using logistic regression model (LRM). The forest cover for the periods 1990 and 2000, derived from Landsat TM satellite imagery, was used to predict the forest cover for 2010. The predictive performance of the model was assessed by comparing the model-predicted forest cover with the actual forest cover for 2010. To explain the effects of anthropogenic pressure on forest, this study considered three distance variables viz., distance from forest edge, roads and settlements, and slope position classes as explanatory variables of forest change. The highest regression coefficient (beta = -26.892) was noticed in case of distance from forest edge, which signifies the higher probability of forest change in areas that are closer to the forest edges. The analysis showed that forest cover has undergone continuous change between 1990 and 2010, leading to the loss of 107.2 km(2) of forest area. The LRM successfully predicted the forest cover for the period 2010 with reasonably high accuracy (ROC = 87%). (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Forest cover dynamics Prediction Dependent variable Explanatory variables logistic regression model

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Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy

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COMPUTERS & GEOSCIENCES 2013年 57卷 81-92页

作者： Wang, Liang-Jie Sawada, Kazuhide Moriguchi, Shuji Gifu Univ Grad Sch Engn Gifu 5011193 Japan Gifu Univ River Basin Res Ctr Gifu 5011193 Japan Gifu Univ Dept Civil Engn Gifu 5011193 Japan

Several mathematical models are used to predict the spatial distribution characteristics of landslides to mitigate damage caused by landslide disasters. Although some studies have achieved excellent results around the world, few studies take the inter-relationship of the selected points (training points) into account. In this paper, we present the Fuzzy c-means (FCM) algorithm as an optimal method for choosing the appropriate input landslide points as training data. Based on different combinations of the Fuzzy exponent (m) and the number of clusters (c), five groups of sampling points were derived from formal seed cells points and applied to analyze the landslide susceptibility in Mizunami City, Gifu Prefecture, Japan. A logistic regression model is applied to create the models of the relationships between landslide-conditioning factors and landslide occurrence. The pre-existing landslide bodies and the area under the relative operative characteristic (ROC) curve were used to evaluate the performance of all the models with different m and c. The results revealed that model no. 4 (m=1.9, c=4) and model no. 5 (m=1.9, c=5) have significantly high classification accuracies, i.e., 90.0%. Moreover, over 30% of the landslide bodies were grouped under the very high susceptibility zone. Otherwise, model no. 4 and model no. 5 had higher area under the ROC curve (AUC) values, which were 0.78 and 0.79, respectively. Therefore, model no. 4 and model no. 5 offer better model results for landslide susceptibility mapping. Maps derived from model no. 4 and model no. 5 would offer the local authorities crucial information for city planning and development. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Landslide susceptibility FCM logistic regression model

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Simulation of decadal alpine permafrost distributions in the Qilian Mountains over past 50 years by using logistic regression model

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COLD REGIONS SCIENCE AND TECHNOLOGY 2012年 73卷 32-40页

作者： Zhao, Shangmin Cheng, Weiming Zhou, Chenghu Chen, Xi Chen, Jin Inst Geog Sci & Nat Resources Res CAS State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Xinjiang Inst Ecol & Geog CAS Urumqi 830011 Peoples R China Chinese Acad Sci Grad Univ Beijing 100049 Peoples R China

Based on a bench-mark distribution map of alpine permafrost, topographic factors and meteorological factors in every decade, the decadal distributions of alpine permafrost from 1960 to 2009 in the Qilian Mountains were simulated by using logistic regression model (LM), and the overall accuracy of the simulation results was estimated to above 80%. Moreover, the changing conditions of alpine permafrost in the Qilian Mountains during each adjoining two decades, as well as over the past 50 years, had also been analyzed. The research results show: (1) LM simulation in this research not only decreases the dependency on the field survey data, but achieves dynamic simulation of alpine permafrost distribution;(2) the simulated areas of alpine permafrost in 1960s, 1970s, 1980s, 1990s and 2000s are 10.7 x 10(4) km(2), 9.8 x 10(4) km(2), 10.0 x 10(4) km(2), 8.5 x 10(4) km(2) and 8.7 x 10(4) km(2) respectively. Hence, the distribution of alpine permafrost presents an overall degraded tendency with two slight fluctuations. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Alpine permafrost simulation logistic regression model Qilian Mountains Topographic factors Meteorological factors

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Ultrasound Radiomics-Based logistic regression model to Differentiate Between Benign and Malignant Breast Nodules

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JOURNAL OF ULTRASOUND IN MEDICINE 2023年第4期42卷 869-879页

作者： Shi, Shanshan An, Xin Li, Yuhong Jinzhou Med Univ Ultrasound Dept Affiliated Hosp 1 Jinzhou Peoples R China

Objectives To explore the potential value of ultrasound radiomics in differentiating between benign and malignant breast nodules by extracting the radiomic features of two-dimensional (2D) grayscale ultrasound images and establishing a logistic regression model. Methods The clinical and ultrasound data of 1000 female patients (500 pathologically benign patients, 500 pathologically malignant patients) who underwent breast ultrasound examinations at our hospital were retrospectively analyzed. The cases were randomly divided into training and validation sets at a ratio of 7:3. Once the region of interest (ROI) of the lesion was manually contoured, Spearman's rank correlation, least absolute shrinkage and selection operator (LASSO) regression, and the Boruta algorithm were adopted to determine optimal features and establish a logistic regression classification model. The performance of the model was assessed using the area under the receiver operating characteristic curve (AUC), and calibration and decision curves (DCA). Results Eight ultrasound radiomic features were selected to establish the model. The AUC values of the model were 0.979 and 0.977 in the training and validation sets, respectively (P = .0029), indicating good discriminative ability in both datasets. Additionally, the calibration and DCA suggested that the model's calibration efficiency and clinical application value were both superior. Conclusions The proposed logistic regression model based on 2D grayscale ultrasound images could facilitate differential diagnosis of benign and malignant breast nodules. The model, which was constructed using ultrasound radiomic features identified in this study, demonstrated good diagnostic performance and could be useful in helping clinicians formulate individualized treatment plans for patients.

关键词： breast cancer logistic regression model radiomics ultrasound

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Quantitative assessment of the effects of outside temperature on farrowing rate in gilts and sows by using a multivariate logistic regression model

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ANIMAL SCIENCE JOURNAL 2018年第8期89卷 1187-1193页

作者： Sasaki, Yosuke Fujie, Madoka Nakatake, Shingo Kawabata, Tadahiro Univ Miyazaki Org Promot Tenure Track Miyazaki 8892192 Japan Univ Miyazaki Ctr Anim Dis Control Miyazaki Japan Univ Miyazaki Fac Agr Dept Anim & Grassland Sci Miyazaki Japan Miyazaki Prefectural Econ Federat Agr Cooperat Miyazaki Japan Kagoshima Prefectural Econ Federat Agr Cooperat Sect Swine Kagoshima Japan

It is well known that pigs are sensitive to heat stress, but few studies have assessed the critical temperature that affects farrowing rate. Therefore, the objective of the present study was to assess the effects of outside temperature on farrowing rate by using a multivariate logistic regression model. Data were obtained from 25 commercial farms, including 26,128 service records for gilts and 120,655 service records for sows. Two variables, maximum temperature (MAX) and temperature humidity index (THI), were used as an indicator for climate conditions. In gilts, an interaction between climate conditions and service number was associated with farrowing rate (p<.05). In the first service, farrowing rate decreased as climate conditions increased, whereas no relationship was found in the second service or later. In sows, farrowing rate at first service decreased as MAX increased from 22 degrees C or THI increased from 66 (p<.05), but no apparent reduction under heat conditions was found in the second service or later. Additionally, effect of heat stress on farrowing rate in parities 1-2 was higher than those in parities 3-5 and 6 (p<.05). These results can be applied to field conditions as a standard for the critical temperature for farrowing rate.

关键词： farrowing rate heat stress logistic regression model sows temperature

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