Methodology to incorporate biologically effective dose and equivalent uniform dose in patient-specific 3-dimensional dosimetry for non-Hodgkin lymphoma patients targeted with 131I-tositumomab therapy

Abstract

A 3-dimensional (3D) imaging-based patient-specific dosimetry methodology incorporating antitumor biologic effects using biologically effective dose (BED) and equivalent uniform dose (EUD) was developed in this study. The methodology was applied to the dosimetry analysis of 6 non-Hodgkin lymphoma patients with a total of 10 tumors.

Methods: Six registered SPECT/CT scans were obtained for each patient treated with (131)I-labeled antibody. Three scans were obtained after tracer administration and 3 after therapy administration. The SPECT/CT scans were used to generate 3D images of cumulated activity. The cumulated activity images and corresponding CT scans were used as input to Monte Carlo dose-rate calculations. The dose-rate distributions were integrated over time to obtain 3D absorbed dose distributions. The time-dependent 3D cumulative dose distributions were used to generate 3D BED distributions. Techniques to incorporate the effect of unlabeled antibody (cold protein) in the BED analysis were explored. Finally, BED distributions were used to estimate an EUD for each tumor volume. Model parameters were determined from optimal fits to tumor regression data. The efficiency of dose delivery to tumors--the ratio of EUD to cumulative dose--was extracted for each tumor and correlated with patient response parameters.

Results: The model developed in this study was validated for dosimetry of non-Hodgkin lymphoma patients treated with (131)I-labeled antibody. Correlations between therapy efficiency generated from the model and tumor response were observed using averaged model parameters. Model parameter determination favored a threshold for the cold effect and typical magnitude for tumor radiosensitivity parameters.

Conclusion: The inclusion of radiobiologic effects in the dosimetry modeling of internal emitter therapy provides a powerful platform to investigate correlations of patient outcome with planned therapy.

FIGURE 1

Fused SPECT/CT images (top) and matching Monte Carlo–generated dose-rate distribution (bottom). Tumor contours were outlined on CT images. 3D dose-rate calculations assumed that SPECT distributions represented radioactive source distributions.

FIGURE 2

Tumor volume response to tracer and therapy administrations. Plotted are ratios of tumor volumes to volume from first (tracer) scan. First 3 time points are from tracer studies, and last 2 or 3 are from therapy studies. Therapy administration time was approximately 200 h after tracer injection (within highlighted region for all patients). Symbol and color are used for each patient. Tumor response to cold effects and radiation varied widely. Solid lines are drawn to guide the eyes.

FIGURE 3

Fractional tumor shrinkage vs. initial tumor volume (A) and cumulative tumor dose (B). Plotted lines and R² values are from linear least square fits to data points. Slope values are significantly different from zero (P < 0.001) in A and marginally different (P = 0.03) in B. Data points are color-coded as in Figure 2.

FIGURE 4

Example dose–volume histograms (A) and survival–volume histograms (B) at 2 time points. Higher dose levels imply lower survival. Histogram width over mean increases with time (more nonuniform).

FIGURE 5

Treatment efficiency (EUD/dose) vs. initial tumor volume (A) and tumor shrinkage (B). Plotted lines and R² values are from linear least square fits to data points. Slope values are significantly different from zero (P < 0.001 for A and B). Data points are color-coded as in Figure 2.