A medical expert system approach using artificial neural networks for standardized treatment planning.
OBJECTIVE
Many radiotherapy treatment plans involve some level of standardization (e.g., in terms of beam ballistics, collimator settings, and wedge angles), which is determined primarily by tumor site and stage. If patient-to-patient variations in the size and shape of relevant anatomical structures for a given treatment site are adequately sampled, then it would seem possible to develop a general method for automatically mapping individual patient anatomy to a corresponding set of treatment variables. A medical expert system approach to standardized treatment planning was developed that should lead to improved planning efficiency and consistency.


METHODS
The expert system was designed to specify treatment variables for new patients based upon a set of templates (a database of treatment plans for previous patients) and a similarity metric for determining the goodness of fit between the relevant anatomy of new patients and patients in the database. A set of artificial neural networks was used to optimize the treatment variables to the individual patient. A simplified example, a four-field box technique for prostate treatments based upon a single external contour, was used to test the viability of the approach.


RESULTS
For a group of new prostate patients, treatment variables specified by the expert system were compared to treatment variables chosen by the dosimetrists. Performance criteria included dose uniformity within the target region and dose to surrounding critical organs. For this standardized prostate technique, a database consisting of approximately 75 patient records was required for the expert system performance to approach that of the dosimetrists.


CONCLUSIONS
An expert system approach to standardized treatment planning has the potential of improving the overall efficiency of the planning process by reducing the number of iterations required to generate an optimized dose distribution, and to function most effectively, should be closely integrated with a dosimetric based treatment planning system.
Wells
D M
DM
Department of Medical Physics, Tom Baker Cancer Centre, Calgary, Alberta, Canada.
Niederer
J
J
D005103:Expert Systems; D006801:Humans; D008297:Male; D016347:Medical Records Systems, Computerized; D016571:Neural Networks, Computer; D011471:Prostatic Neoplasms / Q000532:radiotherapy*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*