4.4 Article

Prediction of Therapy Tumor-Absorbed Dose Estimates in I-131 Radioimmunotherapy Using Tracer Data Via a Mixed-Model Fit to Time Activity

期刊

CANCER BIOTHERAPY AND RADIOPHARMACEUTICALS
卷 27, 期 7, 页码 403-411

出版社

MARY ANN LIEBERT INC
DOI: 10.1089/cbr.2011.1053

关键词

mixed model; radioimmunotherapy; SPECT/CT; tumor dosimetry

资金

  1. National Institute of Health, United States Department of Health and Human Services [2R01 EB001994]
  2. GlaxoSmithKline
  3. Bexxar

向作者/读者索取更多资源

Background: For individualized treatment planning in radioimmunotherapy (RIT), correlations must be established between tracer-predicted and therapy-delivered absorbed doses. The focus of this work was to investigate this correlation for tumors. Methods: The study analyzed 57 tumors in 19 follicular lymphoma patients treated with I-131 tositumomab and imaged with SPECT/CT multiple times after tracer and therapy administrations. Instead of the typical least-squares fit to a single tumor's measured time-activity data, estimation was accomplished via a biexponential mixed model in which the curves from multiple subjects were jointly estimated. The tumor-absorbed dose estimates were determined by patient-specific Monte Carlo calculation. Results: The mixed model gave realistic tumor time-activity fits that showed the expected uptake and clearance phases even with noisy data or missing time points. Correlation between tracer and therapy tumor-residence times (r = 0.98; p < 0.0001) and correlation between tracer-predicted and therapy-delivered mean tumor-absorbed doses (r = 0.86; p < 0.0001) were very high. The predicted and delivered absorbed doses were within +/- 25% (or within +/- 75 cGy) for 80% of tumors. Conclusions: The mixed-model approach is feasible for fitting tumor time-activity data in RIT treatment planning when individual least-squares fitting is not possible due to inadequate sampling points. The good correlation between predicted and delivered tumor doses demonstrates the potential of using a pretherapy tracer study for tumor dosimetry-based treatment planning in RIT.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据