  Breast cancer is a common and deadly disease, but it is often curable when
diagnosed early. While most countries have large-scale screening programs,
there is no consensus on a single globally accepted guideline for breast cancer
screening. The complex nature of the disease; the limited availability of
screening methods such as mammography, magnetic resonance imaging (MRI), and
ultrasound; and public health policies all factor into the development of
screening policies. Resource availability concerns necessitate the design of
policies which conform to a budget, a problem which can be modelled as a
constrained partially observable Markov decision process (CPOMDP). In this
study, we propose a multi-objective CPOMDP model for breast cancer screening
which allows for supplemental screening methods to accompany mammography. The
model has two objectives: maximize the quality-adjusted life years (QALYs) and
minimize lifetime breast cancer mortality risk (LBCMR). We identify the Pareto
frontier of optimal solutions for average and high-risk patients at different
budget levels, which can be used by decision-makers to set policies in
practice. We find that the policies obtained by using a weighted objective are
able to generate well-balanced QALYs and LBCMR values. In contrast, the
single-objective models generally sacrifice a substantial amount in terms of
QALYs/LBCMR for a minimal gain in LBCMR/QALYs. Additionally, our results show
that, with the baseline cost values for supplemental screenings as well as the
additional disutility that they incur, they are rarely recommended in CPOMDP
policies, especially in a budget-constrained setting. A sensitivity analysis
reveals the thresholds on cost and disutility values at which supplemental
screenings become advantageous to prescribe.
