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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Wu, Y; Abbey, CK; Chen, X; Liu, J; Page, DC; Alagoz, O; Peissig, P; Onitilo, AA; Burnside, ES

Published Date

  • October 1, 2015

Published In

Volume / Issue

  • 2 / 4

Electronic International Standard Serial Number (EISSN)

  • 2329-4310

International Standard Serial Number (ISSN)

  • 2329-4302

Digital Object Identifier (DOI)

  • 10.1117/1.JMI.2.4.041005

Citation Source

  • Scopus