Development and verification of a nomogram for prediction of recurrence‐free survival in clear cell renal cell carcinoma

Chen, Yuanlei; Jiang, Shangjun; Lu, Zeyi; Xue, Dingwei; Xia, Liqun; Lu, Jinmei; Wang, Huan; Xu, Liwei; Li, Liyang; Li, Gonghui

doi:10.1111/jcmm.14748

Cited by 7 publications

(9 citation statements)

References 34 publications

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“…Obviously, the performance of our model was better in the external validation set. Apart from the good generalization ability of our model, we believe that the different data sources could explain this phenomenon, which also occurs in other studies ( 44 ). Finally, we simplified our model and developed a web-based tool that allows convenient use of the model.…”

Section: Discussionsupporting

confidence: 82%

A Simple Weaning Model Based on Interpretable Machine Learning Algorithm for Patients With Sepsis: A Research of MIMIC-IV and eICU Databases

et al. 2022

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BackgroundInvasive mechanical ventilation plays an important role in the prognosis of patients with sepsis. However, there are, currently, no tools specifically designed to assess weaning from invasive mechanical ventilation in patients with sepsis. The aim of our study was to develop a practical model to predict weaning in patients with sepsis.MethodsWe extracted patient information from the Medical Information Mart for Intensive Care Database-IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Kaplan–Meier curves were plotted to compare the 28-day mortality between patients who successfully weaned and those who failed to wean. Subsequently, MIMIC-IV was divided into a training set and an internal verification set, and the eICU-CRD was designated as the external verification set. We selected the best model to simplify the internal and external validation sets based on the performance of the model.ResultsA total of 5020 and 7081 sepsis patients with invasive mechanical ventilation in MIMIC-IV and eICU-CRD were included, respectively. After matching, weaning was independently associated with 28-day mortality and length of ICU stay (p < 0.001 and p = 0.002, respectively). After comparison, 35 clinical variables were extracted to build weaning models. XGBoost performed the best discrimination among the models in the internal and external validation sets (AUROC: 0.80 and 0.86, respectively). Finally, a simplified model was developed based on XGBoost, which included only four variables. The simplified model also had good predictive performance (AUROC:0.75 and 0.78 in internal and external validation sets, respectively) and was developed into a web-based tool for further review.ConclusionsWeaning success is independently related to short-term mortality in patients with sepsis. The simplified model based on the XGBoost algorithm provides good predictive performance and great clinical applicablity for weaning, and a web-based tool was developed for better clinical application.

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Section: Discussionsupporting

confidence: 82%

A Simple Weaning Model Based on Interpretable Machine Learning Algorithm for Patients With Sepsis: A Research of MIMIC-IV and eICU Databases

et al. 2022

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“…Therefore, new reliable prognostic biomarkers are urgently needed to establish more accurate prognostic models. Recently, mRNA gene signatures have been considered as the potential predictors of prognosis for KIRC (Chen et al 2019 ; Wu et al 2019 ; Zhang et al 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of a seven-gene signature for predicting overall survival in patients with kidney renal clear cell carcinoma via an integrated bioinformatics analysis

Jiang

Chen

2020

Animal Cells and Systems

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“…Nomogram is a logistic regression-based model that has been extensively applied in prognostic prediction of various cancers, such as colorectal cancer, renal cancer, and bladder cancer. [25][26][27] It formulates the scoring criteria according to regression coefficients of all independent variables and assigns a score level for each independent variable. All of the selected samples entered the logistic regression model and the total score of each feature was calculated.…”

Section: Identifying Clinical Factors With Independent Prognostic Valmentioning

confidence: 99%

Prognostic Score-based Clinical Factors and Metabolism-related Biomarkers for Predicting the Progression of Hepatocellular Carcinoma

Jia

Shu

et al. 2020

Evol Bioinform Online

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Hepatocellular carcinoma (HCC) is a common malignant tumor representing more than 90% of primary liver cancer. This study aimed to identify metabolism-related biomarkers with prognostic value by developing the novel prognostic score (PS) model. Transcriptomic profiles derived from TCGA and EBIArray databases were analyzed to identify differentially expressed genes (DEGs) in HCC tumor samples compared with normal samples. The overlapped genes between DEGs and metabolism-related genes (crucial genes) were screened and functionally analyzed. A novel PS model was constructed to identify optimal signature genes. Cox regression analysis was performed to identify independent clinical factors related to prognosis. Nomogram model was constructed to estimate the predictability of clinical factors. Finally, protein expression of crucial genes was explored in different cancer tissues and cell types from the Human Protein Atlas (HPA). We screened a total of 305 overlapped genes (differentially expressed metabolism-related genes). These genes were mainly involved in “oxidation reduction,” “steroid hormone biosynthesis,” “fatty acid metabolic process,” and “linoleic acid metabolism.” Furthermore, we screened ten optimal DEGs (CYP2C9, CYP3A4, and TKT, among others) by using the PS model. Two clinical factors of pathologic stage (P < .001, HR: 1.512 [1.219-1.875]) and PS status (P <.001, HR: 2.259 [1.522-3.354]) were independent prognostic predictors by cox regression analysis. Nomogram model showed a high predicted probability of overall survival time, and the AUC value was 0.837. The expression status of 7 proteins was frequently altered in normal or differential tumor tissues, such as liver cancer and stomach cancer samples.We have identified several metabolism-related biomarkers for prognosis prediction of HCC based on the PS model. Two clinical factors were independent prognostic predictors of pathologic stage and PS status (high/low risk). The prognosis prediction model described in this study is a useful and stable method for novel biomarker identification.

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Development and verification of a nomogram for prediction of recurrence‐free survival in clear cell renal cell carcinoma

Cited by 7 publications

References 34 publications

A Simple Weaning Model Based on Interpretable Machine Learning Algorithm for Patients With Sepsis: A Research of MIMIC-IV and eICU Databases

A Simple Weaning Model Based on Interpretable Machine Learning Algorithm for Patients With Sepsis: A Research of MIMIC-IV and eICU Databases

Construction and validation of a seven-gene signature for predicting overall survival in patients with kidney renal clear cell carcinoma via an integrated bioinformatics analysis

Prognostic Score-based Clinical Factors and Metabolism-related Biomarkers for Predicting the Progression of Hepatocellular Carcinoma

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