Inverse treatment planning by physically constrained minimization of a biological objective function.
In the current state-of-the art of clinical inverse planning, the design of clinically acceptable IMRT plans is predominantly based on the optimization of physical rather than biological objective functions. A major impetus for this trend is the unproven predictive power of radiobiological models, which is largely due to the scarcity of data sets for an accurate evaluation of the model parameters. On the other hand, these models do capture the currently known dose-volume effects in tissue dose-response, which should be accounted for in the process of optimization. In order to incorporate radiobiological information in clinical treatment planning optimization, we propose a hybrid physico-biological approach to inverse treatment planning based on the application of a continuous penalty function method to the constrained minimization of a biological objective. The objective is defined as the weighted sum of normal tissue complication probabilities evaluated with the Lyman normal-tissue complication probability model. Physical constraints specify the admissible minimum and maximum target dose. The continuous penalty function method is then used to find an approximate solution of the resulting large-scale constrained minimization problem. Plans generated by our approach are compared to ones produced by a commercial planning system incorporating physical optimization. The comparisons show clinically negligible differences, with the advantage that the hybrid technique does not require specifications of any dose-volume constraints to the normal tissues. This indicates that the proposed hybrid physico-biological method can be used for the generation of clinically acceptable plans.
Stavrev
P
P
Department of Medical Physics, Cross Cancer Institute, University of Alberta, 11560 University Avenue, Edmonton, Alberta T6G 1Z2, Canada. pstavrev@phys.ualberta.ca
Hristov
D
D
Warkentin
B
B
Sham
E
E
Stavreva
N
N
Fallone
B G
BG
D000465:Algorithms; D002470:Cell Survival / Q000528:radiation effects; D003198:Computer Simulation; D004307:Dose-Response Relationship, Radiation; D006801:Humans; D008175:Lung Neoplasms / Q000503:physiopathology / Q000532:radiotherapy*; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D011471:Prostatic Neoplasms / Q000503:physiopathology / Q000532:radiotherapy*; D011835:Radiation Protection / Q000379:methods; D011874:Radiometry / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000379:methods*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity