Special Section on Radiomics and Imaging Genomics

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

[+] Author Affiliations
Yirong Wu, Elizabeth S. Burnside

University of Wisconsin-Madison, Department of Radiology, 600 Highland Avenue, Madison, Wisconsin 53792, United States

Craig K. Abbey

University of California-Santa Barbara, Department of Psychological and Brain Sciences, 251 UCEN Road, Santa Barbara, California 93106, United States

Xianqiao Chen

Wuhan University of Technology, School of Computer Science and Technology, 1178 Heping Avenue, Wuhan, Hubei 430070, China

Jie Liu

University of Washington-Seattle, Department of Genome Sciences, 3720 15th Avenue, Seattle, Washington 98105, United States

David C. Page

University of Wisconsin-Madison, Department of Biostatistics and Medical Informatics, 600 Highland Avenue, Madison, Wisconsin 53706, United States

Oguzhan Alagoz

University of Wisconsin-Madison, Department of Industrial and Systems Engineering, 1513 University Avenue, Madison, Wisconsin 53706, United States

Peggy Peissig

Marshfield Clinic Research Foundation, 1000 North Oak Avenue, Marshfield, Wisconsin 54449, United States

Adedayo A. Onitilo

Marshfield Clinic Research Foundation, 1000 North Oak Avenue, Marshfield, Wisconsin 54449, United States

Marshfield Clinic Weston Center, Department of Hematology/Oncology, 3501 Cranberry Boulevard, Weston, Wisconsin 54476, United States

J. Med. Imag. 2(4), 041005 (Aug 17, 2015). doi:10.1117/1.JMI.2.4.041005
History: Received March 31, 2015; Accepted July 20, 2015
Text Size: A A A

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.

Figures in this Article
© 2015 Society of Photo-Optical Instrumentation Engineers

Topics

Breast cancer

Citation

Yirong Wu ; Craig K. Abbey ; Xianqiao Chen ; Jie Liu ; David C. Page, et al.
"Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation", J. Med. Imag. 2(4), 041005 (Aug 17, 2015). ; http://dx.doi.org/10.1117/1.JMI.2.4.041005


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.