Dataset Information

AutoRPA: A web server for constructing cancer staging models by recursive partitioning analysis.

ABSTRACT: Cancer staging provides a common language that is used to describe the severity of an individual's cancer, which plays a critical role in optimizing cancer treatment. Recursive partitioning analysis (RPA) is the most widely accepted method for cancer staging. Despite its widespread use, to date, only limited tools have been developed to implement the RPA algorithm for cancer staging. Moreover, most of the available tools can be accessed only from command lines and also lack visualization, making them difficult for clinical investigators without programing skills to use. Therefore, we developed a web server called autoRPA that is dedicated to supporting the construction of prognostic staging models and performance comparisons among different staging models. Based on the RPA algorithm and log-rank test statistics, autoRPA can establish a decision-making tree from survival data and provide clinicians an intuitive method to further prune the decision tree. Moreover, autoRPA can evaluate the contribution of each submitted covariate that is involved in the grouping process and help identify factors that significantly contribute to cancer staging. Four indicators, including hazard consistency, hazard discrimination, percentage of variation explained, and sample size balance, are introduced to validate the performance of the designed staging models. In addition, autoRPA can also be used to compare the performance of different prognostic staging models using a standard bootstrap evaluation method. The web server of autoRPA is freely available at http://rpa.renlab.org.

SUBMITTER: Xie Y

PROVIDER: S-EPMC7688999 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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autoRPA: A web server for constructing cancer staging models by recursive partitioning analysis.

Xie Yubin Y Luo Xiaotong X Li Huiqin H Xu Qingxian Q He Zhihao Z Zhao Qi Q Zuo Zhixiang Z Ren Jian J

Computational and structural biotechnology journal 20201110

Cancer staging provides a common language that is used to describe the severity of an individual's cancer, which plays a critical role in optimizing cancer treatment. Recursive partitioning analysis (RPA) is the most widely accepted method for cancer staging. Despite its widespread use, to date, only limited tools have been developed to implement the RPA algorithm for cancer staging. Moreover, most of the available tools can be accessed only from command lines and also lack visualization, making ...[more]

PMID: 33294132

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Project description:Objective: To identify narcolepsy related regional brain activity alterations compared with matched healthy controls. To determine whether these changes can be used to distinguish narcolepsy from healthy controls by recursive partitioning analysis (RPA) and receiver operating characteristic (ROC) curve analysis. Method: Fifty-one narcolepsy with cataplexy patients (26 adults and 25 juveniles) and sixty matched heathy controls (30 adults and 30 juveniles) were recruited. All subjects underwent a resting-state functional magnetic resonance imaging scan. Fractional low-frequency fluctuations (fALFF) was used to investigate narcolepsy induced regional brain activity alterations among adult and juveniles, respectively. Recursive partitioning analysis and Receiver operating curve analysis was used to seek the ability of fALFF values within brain regions in distinguishing narcolepsy from healthy controls. Results: Compared with healthy controls, both adult and juvenile narcolepsy had lower fALFF values in bilateral medial superior frontal gyrus, bilateral inferior parietal lobule and supra-marginal gyrus. Compared with healthy controls, both adult and juvenile narcolepsy had higher fALFF values in bilateral sensorimotor cortex and middle temporal gyrus. Also juvenile narcolepsy had higher fALFF in right putamen and right thalamus compared with healthy controls. Based on RPA and ROC curve analysis, in adult participants, fALFF differences in right medial superior frontal gyrus can discriminate narcolepsy from healthy controls with high degree of sensitivity (100%) and specificity (88.9%). In juvenile participants, fALFF differences in left superior frontal gyrus can discriminate narcolepsy from healthy controls with moderate degree of sensitivity (57.1%) and specificity (88.9%). Conclusion: Compared with healthy controls, both the adult and juvenile narcolepsy showed overlap brain regions in fALFF differences after case-control comparison. Furthermore, we propose that fALFF value can be a helpful imaging biomarker in distinguishing narcolepsy from healthy controls among both adults and juveniles.

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Dataset Information

AutoRPA: A web server for constructing cancer staging models by recursive partitioning analysis.

Publications

autoRPA: A web server for constructing cancer staging models by recursive partitioning analysis.

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