Development and Evaluation of Novel Statistical Methods in Urine Biomarker-Based Hepatocellular Carcinoma Screening View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Jeremy Wang, Surbhi Jain, Dion Chen, Wei Song, Chi-Tan Hu, Ying-Hsiu Su

ABSTRACT

Hepatocellular carcinoma is one of the fastest growing cancers in the US and has a low survival rate, partly due to difficulties in early detection. Because of HCC's high heterogeneity, it has been suggested that multiple biomarkers would be needed to develop a sensitive HCC screening test. This study applied random forest (RF), a machine learning technique, and proposed two novel models, fixed sequential (FS) and two-step (TS), for comparison with two commonly used statistical techniques, logistic regression (LR) and classification and regression trees (CART), in combining multiple urine DNA biomarkers for HCC screening using biomarker values obtained from 137 HCC and 431 non-HCC (224 hepatitis and 207 cirrhosis) subjects. The sensitivity, specificity, area under the receiver operating curve, and variability were estimated through repeated 10-fold cross-validation to compare the models' performances in accuracy and robustness. We show that RF and TS have higher accuracy and stability; specifically, they reach 90% specificity and 86%/87% sensitivity respectively along with 15% higher sensitivity and 10% higher specificity than LR in cross-validation. The potential of RF and TS to develop a panel of multiple biomarkers and the possibility for self-training, cloud-based models for HCC screening are discussed. More... »

PAGES

3799

References to SciGraph publications

  • 2000-12-01. Ensemble Methods in Machine Learning in MULTIPLE CLASSIFIER SYSTEMS
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2008-12. Conditional variable importance for random forests in BMC BIOINFORMATICS
  • 2007-12. Bias in random forest variable importance measures: Illustrations, sources and a solution in BMC BIOINFORMATICS
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    http://scigraph.springernature.com/pub.10.1038/s41598-018-21922-9

    DOI

    http://dx.doi.org/10.1038/s41598-018-21922-9

    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29491388


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