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

Yirong Wu; Craig K. Abbey; Xianqiao Chen; Jie Liu; David C. Page; Oguzhan Alagoz; Peggy Peissig; Adedayo A. Onitilo; Elizabeth S. Burnside M.D.

doi:10.1117/1.JMI.2.4.041005

17 August 2015 Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation

Yirong Wu, Craig K. Abbey, Xianqiao Chen, Jie Liu, David C. Page, Oguzhan Alagoz, Peggy Peissig, Adedayo A. Onitilo, Elizabeth S. Burnside M.D.

Author Affiliations +

Journal of Medical Imaging, Vol. 2, Issue 4, 041005 (August 2015). https://doi.org/10.1117/1.JMI.2.4.041005

Abstract

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.

Citation Download Citation

Yirong Wu, Craig K. Abbey, Xianqiao Chen, Jie Liu, David C. Page, Oguzhan Alagoz, Peggy Peissig, Adedayo A. Onitilo, and Elizabeth S. Burnside M.D. "Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation," Journal of Medical Imaging 2(4), 041005 (17 August 2015). https://doi.org/10.1117/1.JMI.2.4.041005

Published: 17 August 2015

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available