This article does not have an abstract to display.
Purpose: Brachytherapy is commonly used as an adjuvant therapy in conjunction with surgery to remove tumors associated with soft tissue sarcoma. In one form of this therapy, catheters are inserted into the tumorbed during surgery, and several days post surgery, radioactive seeds are placed into the catheters and left in place for several days in order to deliver a high dose of radiation to the tumor bed. In current practice, the planning volume for this procedure is either not determined apriori (in this case, seed locations are selected based on isodose curves conforming to a visual estimate of the planning volume), or it is derived via a tedious manual process. In either case, the process is subjective and time-consuming, and is highly dependent on the human planner.
The work presented here involves the development of an automated contouring algorithm to outline the planning volume. Such an automatic procedure will save time and provide a consistent and objective method for determining planning volumes. In addition, a definitive representation of the planning volume will allow for sophisticated brachytherapy treatment planning approaches to be applied when designing treatment plans, so as to maximize local tumor control and minimize normal tissue complications.
Materials and methods: The automated planning volume contouring algorithm is developed utilizing computational geometry, artificial intelligence, and numerical interpolation techniques. The target volume is defined to be the slab of tissue r cm perpendicularly away from the curvilinear plane defined by the mesh of catheters. We assume that if adjacent catheters are over 2r cm apart, the tissue between the two catheters is part of the tumor bed. Input data consists of the digitized coordinates of the catheter positions in each of several cross-sectional slices of the tumor bed, and the estimated distance r from the catheters to the tumor surface. The point of intersection of a catheter and a cross-sectional slice is referred to as a center. We associate with each of these centers a circle of radius r.
The algorithm begins by first labeling the centers in each slice in a natural order. The shape formed by following the associated circles in the specified order provides the overall shape of the tumor bed. Next, for every triplet of circles, tangent points are determined and local interpolation is performed using Bezier curves. At each interpolation iteration, it may be beneficial to generate the local curve without the involvement of the middle circle. In this case, artificial intelligence is used to derive a predictive rule for skipping circles. The effectiveness of the algorithm is evaluated based on its performance on a collection of soft-tissue sarcoma tumor beds within various anatomical structures.
Results: For each of fifteen patient cases considered, the algorithm takes approximately 2 minutes to generate the planning volume. Although the tumor shapes are rather different, the algorithm consistently generates planning volumes that visually demonstrate smooth curves compactly encapsulating the circles. This general-purpose contouring algorithm works well whether the catheters are all close together (with the target a volume mass), or when the catheters are spread far apart in the plane, or arranged in a convoluted way.
Conclusion: The automatic contouring algorithm significantly reduces labor time and provides a consistent and objective method for determining planning volumes for soft tissue sarcoma. Further studies are needed to validate the significance of the resulting planning volumes in designing treatment plans, and the role that sophisticated brachytherapy treatment planning optimization may have in producing good plans.
