title: Factors affecting recall rate and false positive fraction in breast cancer screening with breast tomosynthesis - A statistical approach.
abstract: In this study, we investigate which factors affect the false positive fraction (FPF) for digital breast tomosynthesis (DBT) compared to digital mammography (DM) in a screening population by using classification and regression trees (C&RT) and binary marginal generalized linear models. The data was obtained from the Malmö Breast Tomosynthesis Screening Trial, which aimed to compare the performance of DBT to DM in breast cancer screening. By using data from the first half of the study population (7500 women), a tree with the recall probability for different groups was calculated. The effect of age and breast density on the FPF was estimated using a binary marginal generalized linear model. Our results show that breast density and breast cancer were the main factors influencing recall. The FPF is mainly affected by breast density and increases with breast density for DBT and DM. In conclusion, the results obtained with C&RT are easy to interpret and similar to those obtained using binary marginal generalized linear models. The FPF is approximately 40% higher for DBT compared to DM for all breast density categories.
authors.affiliation: Epidemiology and Register Centre South, Skåne University Hospital, Lund, Sweden. Electronic address: aldana.rosso@skane.se.
authors.forename: Aldana
authors.initials: A
authors.lastname: Rosso
authors.affiliation: Department of Diagnostic Radiology, Translational Medicine Malmö, Lund University, Malmö, Sweden.
authors.forename: Kristina
authors.initials: K
authors.lastname: Lång
authors.affiliation: Epidemiology and Register Centre South, Skåne University Hospital, Lund, Sweden; Orthopaedics, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
authors.forename: Ingemar F
authors.initials: IF
authors.lastname: Petersson
authors.affiliation: Department of Diagnostic Radiology, Translational Medicine Malmö, Lund University, Malmö, Sweden.
authors.forename: Sophia
authors.initials: S
authors.lastname: Zackrisson
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D055088:Early Detection of Cancer / Q000706:statistics & numerical data*; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D016014:Linear Models; D042361:Mammary Glands, Human / Q000002:abnormalities*; D008327:Mammography / Q000706:statistics & numerical data*; D008875:Middle Aged; D012017:Referral and Consultation / Q000706:statistics & numerical data*; D018709:Statistics, Nonparametric; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Binary marginal generalized linear models; Breast cancer screening; Classification and regression trees; Digital breast tomosynthesis; Digital mammography; Generalized estimating equations