Utility of treatment planning for thermochemotherapy treatment of nonmuscle invasive bladder carcinoma

Abstract

Purpose: A recently completed Phase I clinical trial combined concurrent Mitomycin-C chemotherapy with deep regional heating using BSD-2000 Sigma-Ellipse applicator (BSD Corporation, Salt Lake City, UT, U.S.A.) for the treatment of nonmuscle invasive bladder cancer. This work presents a new treatment planning approach, and demonstrates potential impact of this approach on improvement of treatment quality.

Methods: This study retrospectively analyzes a subset of five patients on the trial. For each treatment, expert operators selected "clinical-optimal" settings based on simple model calculation on the BSD-2000 control console. Computed tomography (CT) scans acquired prior to treatment were segmented to create finite element patient models for retrospective simulations with Sigma-HyperPlan (Dr. Sennewald Medizintechnik GmbH, Munchen, Germany). Since Sigma-HyperPlan does not account for the convective nature of heat transfer within a fluid filled bladder, an effective thermal conductivity for bladder was introduced. This effective thermal conductivity value was determined by comparing simulation results with clinical measurements of bladder and rectum temperatures. Regions of predicted high temperature in normal tissues were compared with patient complaints during treatment. Treatment results using "computed-optimal" settings from the planning system were compared with clinical results using clinical-optimal settings to evaluate potential of treatment improvement by reducing hot spot volume.

Results: For all five patients, retrospective treatment planning indicated improved matches between simulated and measured bladder temperatures with increasing effective thermal conductivity. The differences were mostly within 1.3 °C when using an effective thermal conductivity value above 10 W/K/m. Changes in effective bladder thermal conductivity affected surrounding normal tissues within a distance of ∼1.5 cm from the bladder wall. Rectal temperature differences between simulation and measurement were large due to sensitivity to the sampling locations in rectum. The predicted bladder T90 correlated well with single-point bladder temperature measurement. Hot spot locations predicted by the simulation agreed qualitatively with patient complaints during treatment. Furthermore, comparison between the temperature distributions with clinical and computed-optimal settings demonstrated that the computed-optimal settings resulted in substantially reduced hot spot volumes.

Conclusions: Determination of an effective thermal conductivity value for fluid filled bladder was essential for matching simulation and treatment temperatures. Prospectively planning patients using the effective thermal conductivity determined in this work can potentially improve treatment efficacy (compared to manual operator adjustments) by potentially lower discomfort from reduced hot spots in normal tissue.

Figures

Figure 1

(a) CT images of a patient and the reconstructed (transparent) patient model. (b) Patient model inside the BSD-2000 Sigma-Ellipse applicator showing the body outlines, bones, and bladder. (c), (d) Illustration of bladder (target) and rectum and the temperature probes inside them.

Figure 2

(a) Measured bladder (solid line) and rectal (dashed line) temperatures as a function of time in the thermochemotherapy treatment. (b) Net power at the RF amplifier output (dashed line), net power at the applicator feed point (solid line), and (c) phase information as a function of treatment time. Note that (b) and (c) are for just one of the four antennas of the applicator.

Figure 3

The procedure of the retrospective treatment planning for nonmuscle invasive bladder cancer treatment: the middle column is the main procedure in the planning, and the left and right columns are inputs to the treatment planning.

Figure 4

Differences between predicted and measured temperature in bladder as function of the effective thermal conductivity. The curves of patient 3 and patient overlap for most Keff values in the figure.

Figure 5

Differences between predicted and measured temperatures in rectum as function of the effective thermal conductivity: (a) at the original measurement points in rectum and (b) at points 0.5 cm away form the original measurement points for patient 4 and patient 5.

Figure 6

Target treatment temperature statistics as function of keff for one patient, including maximum, minimum, median temperatures, and T90, defined as the temperature value exceeded in 90% of the target volume.

Figure 7

Temperature distribution profiles across the bladder and surrounding tissues in the (a) anterior–posterior, (b) inferior–superior, and (c) left–right directions, with various bladder keff values used for in the planning. The shaded regions represent the bladder. (A = anterior, P = posterior, I = inferior, S = superior, L = left, and R = right).

Figure 8

Demonstration of Sigma-HyperPlan simulated temperature distributions in one patient (patient 3) when the clinical setting was used: (a) three dimensional temperature distribution superimposed on the anatomy showing location of bladder and bones. Temperature distributions on the sagittal plane, coronal plane, and axial plane are illustrated in (b), (c), and (d), respectively.

Figure 9

Sigma-HyperPlan simulated temperature distribution in patient 3 (same as in Fig. 8) when the computed-optimal setting is used: (a) three dimensional temperature distribution in bladder and pelvic bones. Temperature distributions on the sagittal, coronal, and axial planes are illustrated in (b), (c), and (d), respectively. Comparing against Fig. 8, the hot spots are substantially reduced with the computed-optimal setting.