Biomarker selection for medical diagnosis using the partial area under the ROC curve

BMC Research Notes

Huey-Miin Hsueh¹  Yuan-Chin Ivan Chang¹  Man-Jen Hsu² 

[1]Department of Statistics, National ChengChi University, Taipei 11605, Taiwan

[2]Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan

关键词: Stepwise biomarker selection;    Partial area under ROC curve;    Optimal linear combination;    Hypothesis testing;    Discriminatory power;   

Others  :  1134872
DOI  :  10.1186/1756-0500-7-25

 received in 2013-09-25, accepted in 2013-12-23,  发布年份 2014

【 摘 要 】

Background

A biomarker is usually used as a diagnostic or assessment tool in medical research. Finding an ideal biomarker is not easy and combining multiple biomarkers provides a promising alternative. Moreover, some biomarkers based on the optimal linear combination do not have enough discriminatory power. As a result, the aim of this study was to find the significant biomarkers based on the optimal linear combination maximizing the pAUC for assessment of the biomarkers.

Methods

Under the binormality assumption we obtain the optimal linear combination of biomarkers maximizing the partial area under the receiver operating characteristic curve (pAUC). Related statistical tests are developed for assessment of a biomarker set and of an individual biomarker. Stepwise biomarker selections are introduced to identify those biomarkers of statistical significance.

Results

The results of simulation study and three real examples, Duchenne Muscular Dystrophy disease, heart disease, and breast tissue example are used to show that our methods are most suitable biomarker selection for the data sets of a moderate number of biomarkers.

Conclusions

Our proposed biomarker selection approaches can be used to find the significant biomarkers based on hypothesis testing.

【 授权许可】

   
2014 Hsu et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]National Cancer Institute: PDQ® Prostate Cancer Screening. Bethesda, MD: National Cancer Institute; Date last modified 06/08/2012. Available at: http://www.cancer.gov/cancertopics/pdq/screening/prostate/HealthProfessional/Page3#Section_67 webcite. Accessed 06/08/2012
• [2]Etzioni R, Kooperberg C, Pepe M, Smith R, Gann PH: Combining biomarkers to detect disease with application to prostate cancer. Biostatistics 2003, 4:523-538.
• [3]Madu CO, Lu Y: Novel diagnostic biomarkers for prostate cancer. J Cancer Educ 2010, 1:150-177.
• [4]Weng CG, Poon J: A new evaluation measure for imbalanced datasets. Glenelg, South Australia: Roddick JF, Li J, Christen P, Kennedy PJ: ACS; 2008:27-32. [Proceedings of the Seventh Australasian Data Mining Conference]
• [5]Pepe MS, Longton G, Anderson GL, Schummer M: Selecting differentially expressed genes from microarray experiments. Biometrics 2003, 59:133-142.
• [6]Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L: The use of receiver operating characteristic curves in biomedical informatics. J Biomed Inform 2005, 38:404-415.
• [7]Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, Muller M: pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinforma 2011, 12:77-84. BioMed Central Full Text
• [8]Turck N, Vutskits L, Sanchez-Pena P, Robin X, Hainard A, Gex-Fabry M, Fouda C, Bassem H, Muller M, Lisacek F, Puybasset L, Sanchez J-C: A multiparameter panel method for outcome prediction following aneurysmal subarachnoid hemorrhage. Intensive Care Med 2010, 36:107-115.
• [9]Su JQ, Liu JS: Linear combinations of multiple diagnostic markers. J Am Stat Assoc 1993, 88:1350-1355.
• [10]Liu A, Schisterman EF, Zhu Y: On linear combinations of biomarkers to improve diagnostic accuracy. Stat Med 2005, 24:37-47.
• [11]Pepe MS, Thompson ML: Combining diagnostic test results to increase accuracy. Biostatistics 2000, 1:123-140.
• [12]Pepe MS, Cai T, Longton G: Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics 2006, 62:221-229.
• [13]Hsu M-J, Hsueh H-M: The linear combinations of biomarkers which maximize the partial area under the ROC curves. Comput Stat 2013, 28:647-666.
• [14]Ma S, Huang J: Regularized ROC method for disease classification and biomarker selection with microarray data. Bioinformatics 2005, 21:4356-4362.
• [15]Ma S, Huang J: Combining multiple markers for classification using ROC. Biometrics 2007, 63:751-757.
• [16]Zhou XH, Chen B, Xie YM, Tian F, Liu H, Liang X: Variable selection using the optimal ROC curve: An application to a traditional Chinese medicine study on osteoporosis disease. Stat Med 2012, 31:628-635.
• [17]Lin H, Zhou L, Peng H, Zhou X-H: Selection and combination of biomarkers using ROC method for disease classification and prediction. Can J Stat 2011, 39:324-343.
• [18]Marrocco C, Duin RPW, Tortorella F: Maximizing the area under the ROC curve by pairwise feature combination. Pattern Recogn 2008, 41:1961-1974.
• [19]Ricamato MT, Tortorella F: Partial AUC maximization in a linear combination of dichotomizers. Pattern Recogn 2011, 44:2669-2677.
• [20]Komori O, Eguchi S: A boosting method for maximizing the partial area under the ROC curve. BMC Bioinforma 2010, 11:314-330. BioMed Central Full Text
• [21]Wang Z, Chang Y-CI: Marker selection via maximizing the partial area under the ROC curve of linear risk scores. Biostatistics 2011, 12:369-385.
• [22]Marsaglia G: Choosing a point from the surface of a sphere. The Annals of Mathematical Statistics 1972, 43:645-646.
• [23]Muller M: A note on a method for generating points uniformly on n-dimensional spheres. Commun ACM 1959, 2:19-20.
• [24]Tian L: Confidence interval estimation of partial area under curve based on combined biomarkers. Computational Statistics & Data Analysis 2010, 54:466-472.
• [25]Silva JE, Marques JP, Jossinet J: Classification of breast tissue by electrical impedance spectroscopy. Med Biol Eng Comput 2000, 38:26-30.
• [26]UCI Machine Learning Repository. : ; http://archive.ics.uci.edu/ml/datasets/Breast webcite+Tissue