title: Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation.
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.
authors.affiliation: University of Wisconsin-Madison , Department of Radiology, 600 Highland Avenue, Madison, Wisconsin 53792, United States.
authors.forename: Yirong
authors.initials: Y
authors.lastname: Wu
authors.affiliation: University of California-Santa Barbara , Department of Psychological and Brain Sciences, 251 UCEN Road, Santa Barbara, California 93106, United States.
authors.forename: Craig K
authors.initials: CK
authors.lastname: Abbey
authors.affiliation: Wuhan University of Technology , School of Computer Science and Technology, 1178 Heping Avenue, Wuhan, Hubei 430070, China.
authors.forename: Xianqiao
authors.initials: X
authors.lastname: Chen
authors.affiliation: University of Washington-Seattle , Department of Genome Sciences, 3720 15th Avenue, Seattle, Washington 98105, United States.
authors.forename: Jie
authors.initials: J
authors.lastname: Liu
authors.affiliation: University of Wisconsin-Madison , Department of Biostatistics and Medical Informatics, 600 Highland Avenue, Madison, Wisconsin 53706, United States.
authors.forename: David C
authors.initials: DC
authors.lastname: Page
authors.affiliation: University of Wisconsin-Madison , Department of Industrial and Systems Engineering, 1513 University Avenue, Madison, Wisconsin 53706, United States.
authors.forename: Oguzhan
authors.initials: O
authors.lastname: Alagoz
authors.affiliation: Marshfield Clinic Research Foundation , 1000 North Oak Avenue, Marshfield, Wisconsin 54449, United States.
authors.forename: Peggy
authors.initials: P
authors.lastname: Peissig
authors.affiliation: 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.
authors.forename: Adedayo A
authors.initials: AA
authors.lastname: Onitilo
authors.affiliation: University of Wisconsin-Madison , Department of Radiology, 600 Highland Avenue, Madison, Wisconsin 53792, United States.
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Burnside
keywords: breast imaging; expected utility; genomics; mammography; receiver operating characteristic methodology
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