Journal article
Development and Evaluation of Novel Statistical Methods in Urine Biomarker-Based Hepatocellular Carcinoma Screening
Scientific reports, v 8(1), 3799
28 Feb 2018
PMID: 29491388
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Development and Evaluation of Novel Statistical Methods in Urine Biomarker-Based Hepatocellular Carcinoma Screening
- Creators
- Jeremy Wang - JBS Science (United States)Surbhi Jain - JBS Science (United States)Dion Chen - ClinPharma Consulting Inc, Phoenixville, PA USAWei Song - JBS Science (United States)Chi-Tan Hu - Tzu Chi UniversityYing-Hsiu Su - Baruch S. Blumberg Institute
- Publication Details
- Scientific reports, v 8(1), 3799
- Publisher
- NATURE PORTFOLIO
- Number of pages
- 8
- Grant note
- R44CA165312; R01CA202769 / National Institute of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Microbiology and Immunology
- Web of Science ID
- WOS:000426262400052
- Scopus ID
- 2-s2.0-85042938343
- Other Identifier
- 991021463456104721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Multidisciplinary Sciences