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There are several different modalities, e.g., surgery, chemotherapy, andradiotherapy, that are currently used to treat cancer. It is common practice touse a combination of these modalities to maximize clinical outcomes, which areoften measured by a balance between maximizing tumor damage and minimizingnormal tissue side effects due to treatment. However, multi-modality treatmentpolicies are mostly empirical in current practice, and are therefore subject toindividual clinicians' experiences and intuition. We present a novelformulation of optimal multi-modality cancer management using a finite-horizonMarkov decision process approach. Specifically, at each decision epoch, theclinician chooses an optimal treatment modality based on the patient's observedstate, which we define as a combination of tumor progression and normal tissueside effect. Treatment modalities are categorized as (1) Type 1, which has ahigh risk and high reward, but is restricted in the frequency of administrationduring a treatment course, (2) Type 2, which has a lower risk and lower rewardthan Type 1, but may be repeated without restriction, and (3) Type 3, notreatment (surveillance), which has the possibility of reducing normal tissueside effect at the risk of worsening tumor progression. Numerical simulationsusing various intuitive, concave reward functions show the structural insightsof optimal policies and demonstrate the potential applications of using arigorous approach to optimizing multi-modality cancer management.

A Markov decision process approach to optimizing cancer therapy using multiple modalities