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Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation

Publication ,  Journal Article
Wu, Y; Abbey, CK; Chen, X; Liu, J; Page, DC; Alagoz, O; Peissig, P; Onitilo, AA; Burnside, ES
Published in: Journal of Medical Imaging
October 1, 2015

Combining imaging and genetic information to predict disease presence and progression is being codified into an emerging discipline called "radiogenomics." Optimal evaluation methodologies for radiogenomics have not been well established. We aim to develop a decision framework based on utility analysis to assess predictive models for breast cancer diagnosis. We garnered Gail risk factors, single nucleotide polymorphisms (SNPs), and mammographic features from a retrospective case-control study. We constructed three logistic regression models built on different sets of predictive features: (1) Gail, (2) Gail + Mammo, and (3) Gail + Mammo + SNP. Then we generated receiver operating characteristic (ROC) curves for three models. After we assigned utility values for each category of outcomes (true negatives, false positives, false negatives, and true positives), we pursued optimal operating points on ROC curves to achieve maximum expected utility of breast cancer diagnosis. We performed McNemar's test based on threshold levels at optimal operating points, and found that SNPs and mammographic features played a significant role in breast cancer risk estimation. Our study comprising utility analysis and McNemar's test provides a decision framework to evaluate predictive models in breast cancer risk estimation.

Duke Scholars

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Published In

Journal of Medical Imaging

DOI

EISSN

2329-4310

ISSN

2329-4302

Publication Date

October 1, 2015

Volume

2

Issue

4

Related Subject Headings

  • 4003 Biomedical engineering
  • 3202 Clinical sciences
 

Citation

APA
Chicago
ICMJE
MLA
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Wu, Y., Abbey, C. K., Chen, X., Liu, J., Page, D. C., Alagoz, O., … Burnside, E. S. (2015). Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation. Journal of Medical Imaging, 2(4). https://doi.org/10.1117/1.JMI.2.4.041005
Wu, Y., C. K. Abbey, X. Chen, J. Liu, D. C. Page, O. Alagoz, P. Peissig, A. A. Onitilo, and E. S. Burnside. “Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation.” Journal of Medical Imaging 2, no. 4 (October 1, 2015). https://doi.org/10.1117/1.JMI.2.4.041005.
Wu Y, Abbey CK, Chen X, Liu J, Page DC, Alagoz O, et al. Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation. Journal of Medical Imaging. 2015 Oct 1;2(4).
Wu, Y., et al. “Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation.” Journal of Medical Imaging, vol. 2, no. 4, Oct. 2015. Scopus, doi:10.1117/1.JMI.2.4.041005.
Wu Y, Abbey CK, Chen X, Liu J, Page DC, Alagoz O, Peissig P, Onitilo AA, Burnside ES. Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation. Journal of Medical Imaging. 2015 Oct 1;2(4).

Published In

Journal of Medical Imaging

DOI

EISSN

2329-4310

ISSN

2329-4302

Publication Date

October 1, 2015

Volume

2

Issue

4

Related Subject Headings

  • 4003 Biomedical engineering
  • 3202 Clinical sciences