A decision aid for intensity-modulated radiation-therapy plan selection in prostate cancer based on a prognostic Bayesian network and a Markov model

Authors:
Wade P. Smith;Jason Doctor;Jürgen Meyer;Ira J. Kalet;Mark H. Phillips
Affiliations:
1959 NE Pacific St., Department of Radiation Oncology, Box 356043, University of Washington, Seattle, WA 98195-6043, United States;School of Pharmacy, University of Southern California, 1985 Zonal Avenue, Los Angeles, CA 90033, United States;Department of Physics and Astronomy, University of Canterbury, Private Bag 4800, Christchurch, New Zealand;1959 NE Pacific St., Department of Radiation Oncology, Box 356043, University of Washington, Seattle, WA 98195-6043, United States;1959 NE Pacific St., Department of Radiation Oncology, Box 356043, University of Washington, Seattle, WA 98195-6043, United States
Venue:
Artificial Intelligence in Medicine
Year:
2009

Citing 6
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Abstract

Objective: The prognosis of cancer patients treated with intensity-modulated radiation-therapy (IMRT) is inherently uncertain, depends on many decision variables, and requires that a physician balance competing objectives: maximum tumor control with minimal treatment complications. Methods: In order to better deal with the complex and multiple objective nature of the problem we have combined a prognostic probabilistic model with multi-attribute decision theory which incorporates patient preferences for outcomes. Results: The response to IMRT for prostate cancer was modeled. A Bayesian network was used for prognosis for each treatment plan. Prognoses included predicting local tumor control, regional spread, distant metastases, and normal tissue complications resulting from treatment. A Markov model was constructed and used to calculate a quality-adjusted life-expectancy which aids in the multi-attribute decision process. Conclusions: Our method makes explicit the tradeoffs patients face between quality and quantity of life. This approach has advantages over current approaches because with our approach risks of health outcomes and patient preferences determine treatment decisions.