Optimization of Transpedicular Electrode Insertion for Electroporation-Based Treatments of Vertebral Tumors

Abstract

Electroporation-based treatments such as electrochemotherapy and irreversible electroporation ablation have sparked interest with respect to their use in medicine. Treatment planning involves determining the best possible electrode positions and voltage amplitudes to ensure treatment of the entire clinical target volume (CTV). This process is mainly performed manually or with computationally intensive genetic algorithms. In this study, an algorithm was developed to optimize electrode positions for the electrochemotherapy of vertebral tumors without using computationally intensive methods. The algorithm considers the electric field distribution in the CTV, identifies undertreated areas, and uses this information to iteratively shift the electrodes from their initial positions to cover the entire CTV. The algorithm performs successfully for different spinal segments, tumor sizes, and positions within the vertebra. The average optimization time was 71 s with an average of 4.9 iterations performed. The algorithm significantly reduces the time and expertise required to create a treatment plan for vertebral tumors. This study serves as a proof of concept that electrode positions can be determined (semi-)automatically based on the spatial information of the electric field distribution in the target tissue. The algorithm is currently designed for the electrochemotherapy of vertebral tumors via a transpedicular approach but could be adapted for other anatomic sites in the future.

Keywords: bone tumors; minimally invasive treatment; numerical modeling; treatment planning; tumor treatment.

Figure 1

Three different tumor locations within the vertebral body: (a) central location, (b) anterior-lateral location, and (c) posterior-inferior location. At each location, the tumor is modelled with three different radii: 5 mm, 7.5 mm, and 10 mm. This illustration was created from an axial CT section of an L3 vertebra and does not show the actual geometry of the numerical model used for computation.

Figure 2

Flowchart of the algorithm.

Figure 3

A weighting map of the clinical target volume (CTV). The contour (black) and center of mass (CoM) of the tumor gross volume (GTV, black circle) are shown. The weight uniformly decreases with distance from tumor border and reaches zero on the outer border of the CTV.

Figure 4

An example of point selection shown on the (a) axial, (b) sagittal, and (c) coronal CT slice of a thoracic vertebra. (d) An example of the model’s geometry in COMSOL Multiphysics, showing a thoracic vertebra and a spherical tumor, with starting electrode positions, obtained from the selected points. (e) Corrected electrode positions after algorithm initialization step.

Figure 5

Illustration of the forces acting on the electrodes. (a) Attractive force (F_geo1, F_geo2) toward the tumor’s center of mass (CoM_tum). (b) Attractive force (at the electrode tip, F_iT, and rear F_iR) toward the undertreated areas of the clinical target volume. (c) Repulsive force (F_dd1, F_dd2) maintaining appropriate distance between electrodes. (d) Final forces (F₁ and F₂) acting on the electrodes are a weighted sum of all forces. This figure is for illustration of the concept only; distances, vectors, and sums do not represent actual values.