Three-dimensional radiobiologic dosimetry: application of radiobiologic modeling to patient-specific 3-dimensional imaging-based internal dosimetry

Abstract

Phantom-based and patient-specific imaging-based dosimetry methodologies have traditionally yielded mean organ-absorbed doses or spatial dose distributions over tumors and normal organs. In this work, radiobiologic modeling is introduced to convert the spatial distribution of absorbed dose into biologically effective dose and equivalent uniform dose parameters. The methodology is illustrated using data from a thyroid cancer patient treated with radioiodine.

Methods: Three registered SPECT/CT scans were used to generate 3-dimensional images of radionuclide kinetics (clearance rate) and cumulated activity. The cumulated activity image and corresponding CT scan were provided as input into an EGSnrc-based Monte Carlo calculation: The cumulated activity image was used to define the distribution of decays, and an attenuation image derived from CT was used to define the corresponding spatial tissue density and composition distribution. The rate images were used to convert the spatial absorbed dose distribution to a biologically effective dose distribution, which was then used to estimate a single equivalent uniform dose for segmented volumes of interest. Equivalent uniform dose was also calculated from the absorbed dose distribution directly.

Results: We validate the method using simple models; compare the dose-volume histogram with a previously analyzed clinical case; and give the mean absorbed dose, mean biologically effective dose, and equivalent uniform dose for an illustrative case of a pediatric thyroid cancer patient with diffuse lung metastases. The mean absorbed dose, mean biologically effective dose, and equivalent uniform dose for the tumor were 57.7, 58.5, and 25.0 Gy, respectively. Corresponding values for normal lung tissue were 9.5, 9.8, and 8.3 Gy, respectively.

Conclusion: The analysis demonstrates the impact of radiobiologic modeling on response prediction. The 57% reduction in the equivalent dose value for the tumor reflects a high level of dose nonuniformity in the tumor and a corresponding reduced likelihood of achieving a tumor response. Such analyses are expected to be useful in treatment planning for radionuclide therapy.

FIGURE 1

Uniform-density sphere with effective half-life of 2 h in outer green region and 4 h within red region. Green and red regions have equal volume in this example. Initial activity in each region is selected so that total numbers of decays are equal in both regions.

FIGURE 2

Density distribution (uniform) (A) for uniform activity distribution model (B) and nonuniform activity distribution model (C). In nonuniform distribution, same total activity as shown in Figure 3B is concentrated into half the volume (outer shell). Assuming a uniform density sphere (A), 2 activity distributions are depicted: uniform (B) and nonuniform (C). In C, the same total activity as in B is concentrated into the outer shell of the sphere.

FIGURE 3

(A) Spheric nonuniform density model in which inner sphere is twice unit density (2.0 g/cm³) and outer shell is at unit density (1.0 g/cm³). (B) Uniform activity distribution for density model in Figure 2A. (C) Cross-sectional slice of 3D-RD output for spheric nonuniform density model.

FIGURE 4

(A) Clinical CT portion of a SPECT/CT scan of patient showing nonuniform density distribution in lungs. (B) Clinical SPECT scan of patient showing nonuniform activity distribution. (C) Rate map generated from 3 longitudinally aligned SPECT images; regions with effective half-life greater than physical half-life of ¹³¹I reflect tumor uptake. (D) Cumulative activity generated from rate map and SPECT.

FIGURE 5

Comparison between MCNP-based dose volume histogram of Song et al. (32) over lung and tumor regions and results from EGS using same inputs. Mean value of MCNP method is 3.01 × 10⁻⁵ mGy/MBq-s per pixel, whereas EGS mean is 2.88 × 10⁻⁵ mGy/MBq-s per pixel.

FIGURE 6

BED map resulting from 3D-RD using full patient-specific data. Although values of absorbed dose and BED are different, their relative changes from voxel to voxel are so similar that it is nearly impossible to visually differentiate the two.

FIGURE 7

Differential absorbed dose (solid line) and BED (dashed line) volume histograms of tumor (A) and of lung (B) resulting from full patient-specific 3D-RD calculation.