Title: A Markov decision process approach to optimizing cancer therapy using multiple modalities,
Abstract: There are several different modalities, e.g., surgery, chemotherapy, and
radiotherapy, that are currently used to treat cancer. It is common practice to
use a combination of these modalities to maximize clinical outcomes, which are
often measured by a balance between maximizing tumor damage and minimizing
normal tissue side effects due to treatment. However, multi-modality treatment
policies are mostly empirical in current practice, and are therefore subject to
individual clinicians' experiences and intuition. We present a novel
formulation of optimal multi-modality cancer management using a finite-horizon
Markov decision process approach. Specifically, at each decision epoch, the
clinician chooses an optimal treatment modality based on the patient's observed
state, which we define as a combination of tumor progression and normal tissue
side effect. Treatment modalities are categorized as (1) Type 1, which has a
high risk and high reward, but is restricted in the frequency of administration
during a treatment course, (2) Type 2, which has a lower risk and lower reward
than Type 1, but may be repeated without restriction, and (3) Type 3, no
treatment (surveillance), which has the possibility of reducing normal tissue
side effect at the risk of worsening tumor progression. Numerical simulations
using various intuitive, concave reward functions show the structural insights
of optimal policies and demonstrate the potential applications of using a
rigorous approach to optimizing multi-modality cancer management.