A decision aid for intensity-modulated radiation-therapy plan selection in prostate cancer based on a prognostic Bayesian network and a Markov model.
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.
Smith
Wade P
WP
Department of Radiation Oncology, Box 356043, University of Washington, 1959 NE Pacific St., Seattle, WA 98195-6043, USA. wpsmith@u.washington.edu
Doctor
Jason
J
Meyer
Jürgen
J
Kalet
Ira J
IJ
Phillips
Mark H
MH
D001499:Bayes Theorem; D003661:Decision Support Techniques; D006801:Humans; D008297:Male; D008390:Markov Chains; D010347:Patient Care Planning; D011336:Probability; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000532:radiotherapy*; D011879:Radiotherapy Dosage