Decision-Analytic Modeling on Imaging Modalities in Breast Cancer Staging: A Systematic Literature Review
Author(s)
Sibylle Puntscher, Dr.1, Caroline Steigenberger, MPH1, Ursula Rochau, MSc, MD1, veronika papon, MSc1, Maja Kjær Rasmussen, MSc2, Beate Jahn, PhD1, Wolfgang Buchberger, Dr.3, Malene Grubbe Hildebrandt, PhD4, Uwe Siebert, MPH, MSc, ScD, MD5.
1Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria, 2Odense University Hospital (OUH ), Centre for Innovative Medical Technology (CIMT), Odense, Denmark, 3Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL– University for Health Sciences and Technology, Hall in Tirol, Austria, 4University of Southern Denmark, Odense, Denmark, 5Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology; Harvard Chan School of Public Health, Hall in Tirol, Austria.
1Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria, 2Odense University Hospital (OUH ), Centre for Innovative Medical Technology (CIMT), Odense, Denmark, 3Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL– University for Health Sciences and Technology, Hall in Tirol, Austria, 4University of Southern Denmark, Odense, Denmark, 5Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology; Harvard Chan School of Public Health, Hall in Tirol, Austria.
OBJECTIVES: Accurate staging of advanced breast cancer is critical for guiding therapeutic strategies. While imaging plays a central role in staging, the medical and economic impacts of different imaging modalities remain uncertain. Decision-analytic models (DAM) can support evidence-based decisions by comparing imaging strategies in terms of effectiveness, cost, and long-term impact. This systematic review aims to identify and critically assess DAMs to inform future model development and optimize long-term care strategies.
METHODS: A systematic literature search was conducted in PubMed/MEDLINE, Embase, and the International Health Technology Assessment database following PRISMA guidelines to identify relevant studies published up to March 2025. Eligible studies were original peer-reviewed articles published in English, Danish, German, or Italian, reporting relevant DAMs of imaging modalities used in breast cancer staging. Relevant data such as population, compared intervention strategies, health and economic outcomes, and model/simulation type were extracted in predefined systematic evidence tables. Screening and data extraction were performed by two independent reviewers.
RESULTS: Of 937 records identified, 16 studies were included for full-text screening, and seven met inclusion criteria. The modeling approaches included decision trees (n=3), a decision tree combined with a Markov state-transition model (n=1), a simulation model (not further defined) (n=1), and discrete-event simulation (n=2). Reported outcomes included diagnostic accuracy (sensitivity/specificity), number of re-biopsies, costs, and incremental cost-effectiveness ratios. Advanced imaging can reduce biopsies (n=5) resulting in less adverse events and may be cost effective (n=2) but not in all settings (n=1). Key limitations of the studies included insufficient evidence on diagnostic accuracy of imaging techniques and a lack of data to support modeling.
CONCLUSIONS: Evidence from existing DAM studies suggests that the health impact and cost-effectiveness of imaging modalities for breast cancer staging is highly context-specific. Further evidence on performance and clinical impact is needed to inform future evaluations and policy decisions.
METHODS: A systematic literature search was conducted in PubMed/MEDLINE, Embase, and the International Health Technology Assessment database following PRISMA guidelines to identify relevant studies published up to March 2025. Eligible studies were original peer-reviewed articles published in English, Danish, German, or Italian, reporting relevant DAMs of imaging modalities used in breast cancer staging. Relevant data such as population, compared intervention strategies, health and economic outcomes, and model/simulation type were extracted in predefined systematic evidence tables. Screening and data extraction were performed by two independent reviewers.
RESULTS: Of 937 records identified, 16 studies were included for full-text screening, and seven met inclusion criteria. The modeling approaches included decision trees (n=3), a decision tree combined with a Markov state-transition model (n=1), a simulation model (not further defined) (n=1), and discrete-event simulation (n=2). Reported outcomes included diagnostic accuracy (sensitivity/specificity), number of re-biopsies, costs, and incremental cost-effectiveness ratios. Advanced imaging can reduce biopsies (n=5) resulting in less adverse events and may be cost effective (n=2) but not in all settings (n=1). Key limitations of the studies included insufficient evidence on diagnostic accuracy of imaging techniques and a lack of data to support modeling.
CONCLUSIONS: Evidence from existing DAM studies suggests that the health impact and cost-effectiveness of imaging modalities for breast cancer staging is highly context-specific. Further evidence on performance and clinical impact is needed to inform future evaluations and policy decisions.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
EE318
Topic
Economic Evaluation, Medical Technologies
Disease
Oncology