scholarly journals Establishment and Verification of Synchronous Metastatic Nomogram for Gastrointestinal Stromal Tumors (GISTs): A Population-Based Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-7 ◽  
Author(s):  
Yuqiang Li ◽  
Guangfeng Zhang ◽  
Xiangping Song ◽  
Lilan Zhao ◽  
Cenap Güngör ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Gastrointestinal Stromal Tumors ◽  
Logistic Regression Model ◽  
Validation Cohort ◽  
Tumor Location ◽  
Population Based ◽  
Brier Score ◽  
Validation Group ◽  
Synchronous Metastasis ◽  
Stromal Tumors

Aim. Assess the risk of synchronous metastasis and establish a nomogram in patients with GISTs. Methods. Surveillance, Epidemiology and End Results database (2004-2014) was accessed. With the logistic regression model as the basis, a nomogram was constructed. Results. 7,256 target patients were contained in our study. The nomogram discrimination for mGIST prediction revealed that tumor size contributed most to synchronous metastasis, followed by lymph nodes, extension, pathologic grade, tumor location, and mitotic count. C-index values of predictions were 0.821 (95% CI, 0.805-0.836) and 0.815 (95% CI, 0.800-0.831), and Brier score were 0.109 and 0.112 in training and validation group, respectively. The value of area under the ROCs were 0.813 (p<0.001) in the primary cohort and 0.819 (p<0.001) in the validation cohort. Through the calibration curves (as seen in the figures), nomogram prediction proved to have excellent agreement with actual metastatic diseases. Conclusion. A new nomogram was created that can evaluate synchronous metastatic diseases in patients with GISTs.

scholarly journals Computed-Tomography-Based Radiomics Model for Predicting the Malignant Potential of Gastrointestinal Stromal Tumors Preoperatively: A Multi-Classifier and Multicenter Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Minhong Wang ◽  
Zhan Feng ◽  
Lixiang Zhou ◽  
Liang Zhang ◽  
Xiaojun Hao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Risk Stratification ◽  
Gastrointestinal Stromal Tumors ◽  
Validation Cohort ◽  
External Validation ◽  
Random Forest Model ◽  
Training Cohort ◽  
Stromal Tumors ◽  
Forest Model

Background: Our goal was to establish and verify a radiomics risk grading model for gastrointestinal stromal tumors (GISTs) and to identify the optimal algorithm for risk stratification.Methods: We conducted a retrospective analysis of 324 patients with GISTs, the presence of which was confirmed by surgical pathology. Patients were treated at three different hospitals. A training cohort of 180 patients was collected from the largest center, while an external validation cohort of 144 patients was collected from the other two centers. To extract radiomics features, regions of interest (ROIs) were outlined layer by layer along the edge of the tumor contour on CT images of the arterial and portal venous phases. The dimensionality of radiomic features was reduced, and the top 10 features with importance value above 5 were selected before modeling. The training cohort used three classifiers [logistic regression, support vector machine (SVM), and random forest] to establish three GIST risk stratification prediction models. The receiver operating characteristic curve (ROC) was used to compare model performance, which was validated by external data.Results: In the training cohort, the average area under the curve (AUC) was 0.84 ± 0.07 of the logistic regression, 0.88 ± 0.06 of the random forest, and 0.81 ± 0.08 of the SVM. In the external validation cohort, the AUC was 0.85 of the logistic regression, 0.90 of the random forest, and 0.80 of the SVM. The random forest model performed the best in both the training and the external validation cohorts and could be generalized.Conclusion: Based on CT radiomics, there are multiple machine-learning models that can predict the risk of GISTs. Among them, the random forest algorithm had the highest prediction efficiency and could be readily generalizable. Through external validation data, we assume that the random forest model may be used as an effective tool to guide preoperative clinical decision-making.

scholarly journals Effect of fluid administration on scene to traffic accident patients by EMS personnel: a propensity score-matched study using population-based ambulance records and nationwide trauma registry in Japan

2021 ◽  
Author(s):  
Yusuke Katayama ◽  
Tetsuhisa Kitamura ◽  
Kosuke Kiyohara ◽  
Kenichiro Ishida ◽  
Tomoya Hirose ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Propensity Score ◽  
Hospital Mortality ◽  
Regression Model ◽  
Traffic Accident ◽  
Logistic Regression Model ◽  
Cardiopulmonary Arrest ◽  
Population Based ◽  
Fluid Administration ◽  
Secondary Outcome

Abstract Purpose The aim of this study was to assess the effect of fluid administration by emergency life-saving technicians (ELST) on the prognosis of traffic accident patients by using a propensity score (PS)-matching method. Methods The study included traffic accident patients registered in the JTDB database from January 2016 to December 2017. The main outcome was hospital mortality, and the secondary outcome was cardiopulmonary arrest on hospital arrival (CPAOA). To reduce potential confounding effects in the comparisons between two groups, we estimated a propensity score (PS) by fitting a logistic regression model that was adjusted for 17 variables before the implementation of fluid administration by ELST at the scene. Results During the study period, 10,908 traffic accident patients were registered in the JTDB database, and we included 3502 patients in this study. Of these patients, 142 were administered fluid by ELST and 3360 were not administered fluid by ELST. After PS matching, 141 patients were selected from each group. In the PS-matched model, fluid administration by ELST at the scene was not associated with discharge to death (crude OR: 0.859 [95% CI, 0.500–1.475]; p = 0.582). However, the fluid group showed statistically better outcome for CPAOA than the no fluid group in the multiple logistic regression model (adjusted OR: 0.231 [95% CI, 0.055–0.967]; p = 0.045). Conclusion In this study, fluid administration to traffic accident patients by ELST was associated not with hospital mortality but with a lower proportion of CPAOA.

Gastrointestinal stromal tumors (GISTs) at uncommon locations: A large population based analysis

2015 ◽  
Vol 111 (6) ◽  
pp. 696-701 ◽  
Author(s):  
Moshim Kukar ◽  
Aditi Kapil ◽  
Wesley Papenfuss ◽  
Adrienne Groman ◽  
Stephen R. Grobmyer ◽  
...  
Keyword(s):  
Gastrointestinal Stromal Tumors ◽  
Large Population ◽  
Population Based ◽  
Stromal Tumors

scholarly journals Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2)

2021 ◽  
Vol 8 ◽  
Author(s):  
Robert A. Reed ◽  
Andrei S. Morgan ◽  
Jennifer Zeitlin ◽  
Pierre-Henri Jarreau ◽  
Héloïse Torchin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Expert Opinion ◽  
Logistic Regression Model ◽  
Population Based ◽  
Regression Modelling ◽  
Preterm Babies ◽  
Logistic Regression Modelling

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.

Long-term Survival among Patients with Gastrointestinal Stromal Tumors Diagnosed after Another Malignancy: A SEER Population-Based Study

2020 ◽  
Author(s):  
Chaoyong Shen ◽  
Chengshi Wang ◽  
Tao He ◽  
Zhaolun Cai ◽  
Xiaonan Yin ◽  
...  
Keyword(s):  
Gastrointestinal Stromal Tumors ◽  
Population Based ◽  
Clinicopathological Characteristics ◽  
Second Primary ◽  
Primary Malignancy ◽  
Clinicopathologic Characteristics ◽  
Term Survival ◽  
Stromal Tumors ◽  
Group A

Abstract BACKGROUND: To explore overall survival (OS) and GISTs-specific survival (GSS) among cancer survivors developing a second primary gastrointestinal stromal tumors (GISTs). METHODS: We conducted a cohort study, where patients with GISTs after another malignancy (AM-GISTs, n=851) and those with only GISTs (GISTs-1, n=7660) were identified from the Surveillance, Epidemiology, End Results registries (1988-2016). Clinicopathologic characteristics and survival were compared between the two groups. RESULTS: The most commonly diagnosed first primary malignancy was prostate cancer (27.7%), followed by breast cancer (16.2%). OS among AM-GISTs was significantly inferior to that of GISTs-1: 10-year OS was 40.3% vs. 50.0%, (p<0.001); A contrary finding was observed for GSS (10-year GSS: 68.9% vs. 61.8%, p=0.002). In the AM-GISTs group, a total of 338 patients died, of which 26.0% died of their initial cancer and 40.8% died of GISTs. Independent of demographics and clinicopathological characteristics, mortality from GISTs among AM-GISTs patients was decreased compared with their GISTs-1 counterparts (HR, 0.71; 95% CI, 0.59-0.84; p<0.001); whereas OS was inferior among AM-GISTs (HR, 1.11; 95% CI, 0.99-1.25; p=0.085). CONCLUSIONS: AM-GISTs patients have decreased risk of dying from GISTs compared with GIST-1. Although another malignancy history does not seemingly affect OS for GISTs patients, clinical treatment of such patients should be cautious.

scholarly journals Association between Maternal Pre-pregnancy Body Mass Index and Breastfeeding Duration in Taiwan: A Population-Based Cohort Study

Nutrients ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2361
Author(s):  
Chi-Nien Chen ◽  
Hung-Chen Yu ◽  
An-Kuo Chou
Keyword(s):  
Body Mass Index ◽  
Logistic Regression ◽  
Regression Model ◽  
Body Mass ◽  
Logistic Regression Model ◽  
Maternal Obesity ◽  
Population Based ◽  
Normal Weight ◽  
Breastfeeding Duration ◽  
Breastfeeding Cessation

An association between high pre-pregnancy body mass index (BMI) and early breastfeeding cessation has been previously observed, but studies examining the effect of underweight are still scant and remain inconclusive. This study analyzed data from a nationally representative cohort of 18,312 women (mean age 28.3 years; underweight 20.1%; overweight 8.2%; obesity 1.9%) who delivered singleton live births in 2005 in Taiwan. Comprehensive face-to-face interviews and surveys were completed at 6 and 18 months postpartum. BMI status and breastfeeding duration were calculated from the self-reported data in the questionnaires. In the adjusted ordinal logistic regression model, maternal obesity and underweight had a higher odds of shorter breastfeeding duration compared with normal-weight women. The risk of breastfeeding cessation was significantly higher in underweight women than in normal-weight women after adjustments in the logistic regression model (2 m: aOR = 1.11, 95% CI = 1.03–1.2; 4 m: aOR = 1.32, 95% CI = 1.21–1.43; 6 m: aOR = 1.3, 95% CI = 1.18–1.42). Our findings indicated that maternal underweight and obesity are associated with earlier breastfeeding cessation in Taiwan. Optimizing maternal BMI during the pre-conception period is essential, and future interventions to promote and support breastfeeding in underweight mothers are necessary to improve maternal and child health.

Efficacy of surgical treatment of patients with localized gastrointestinal stromal tumors

2016 ◽  
Vol 21 (5) ◽  
pp. 233-237
Author(s):  
Petr P. Arkhiri ◽  
I. S Stilidi ◽  
I. V Poddubnaya ◽  
S. N Nered ◽  
M. P Nikulin ◽  
...  
Keyword(s):  
Gastrointestinal Stromal Tumors ◽  
Cellular Structure ◽  
Kinase Inhibitors ◽  
Radical Surgery ◽  
Locally Advanced ◽  
Tumor Location ◽  
Main Method ◽  
Mutation Status ◽  
Stromal Tumors ◽  
Risk Of Disease

The main method of the treatment of patients with localized and locally advanced gastrointestinal stromal tumors (GIST) currently remains to be surgical, but its effectiveness is limited and determined by the degree of local expansion of the disease and radicality of the surgery. Before the era of the use of tyrosine kinase inhibitors (TKI) in the treatment of GIST patients the overall 5-year survival after radical surgery in the group of patients with size of the tumor larger than 10 cm failed to exceed 20%. For the time present with the comprehensive approach to the treatment indices of survival in these patients have significantly improved. Overall 5-year survival in patients with a high risk of disease progression reaches 93%. The most important prognostic factors in patients with primary localized GIST are: the size of the primary tumor, mitotic index, tumor location, mutation status and the morphological variant of the cellular structure of GIST.

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