Internal radionuclide therapies such as radioembolization (RE) with Y-90 loaded microspheres offer a unique promise for personalized treatment of cancer because imaging-based, pre-treatment assessment can be used to determine administered activities which deliver tumoricidal absorbed doses to lesions while sparing critical organs. Currently, however, administered activities are typically determined using empirical or weight-adjusted formulas, especially in therapies utilizing pure beta emitters like Y-90. Although this approach to Y-90 RE has yielded promising response rates in unresectable primary liver cancer and metastases, it fails to exploit the strong potential for enhancing survival by dosimetry-guided treatment intensification. At present, the dose- effect relationships required for personalized planning are not well established, due primarily to inaccuracies in quantitative Y-90 imaging, the lack of reliable radiobiologic models, and the dearth of clinical data. While Y-90 has attractive characteristics for therapy, imaging is complex as it involves SPECT via bremsstrahlung photons or PET via a very low abundance positron. Despite these challenges, successes of innovative therapies and its utility as a theranostic agent have sparked interest in improving Y-90 imaging; preliminary studies show that there is much potential to improve quantitative accuracy of both Y-90 SPECT and PET. The long-term goal is clinical implementation of dosimetry-guided radionuclide therapy that is effective and safe. The objective here is to develop methods for accurate quantitative Y-90 imaging of lesions and normal organs and to apply these tools in a patient study to develop predictive dosimetric models for future RE treatment planning.
Aim 1 : To develop and optimize quantitative Y-90 imaging for 3-D dosimetry. For SPECT/CT, a novel multi-window joint reconstruction will be developed to address the continuous bremsstrahlung spectrum and tissue-dependent effects. For PET/CT, regularized reconstruction, with and without CT-side information, will be implemented to address the noisy images associated with low true coincidence count-rates in the setting of high randoms. Phantom evaluations will encompass the imaging conditions encountered in RE and some systemic Y-90 therapies.
Aim 2 : To estimate lesion dose vs. efficacy and normal liver dose vs. toxicity relationships using statistical models and radiobiologic tumor control and normal tissue complication probability models in a patient study of Y-90 (glass) microsphere RE in liver malignancies. Models will account for clinical heterogeneity by including non-dosimetric variables, including biomarkers, as covariates. The correlation between planning Tc- MAA SPECT/CT vs. post-therapy Y-90 SPECT/CT (and PET/CT) imaging will also be determined. The expected outcome of Aim 1 is new and accurate tools for imaging/dosimetry in Y-90 radionuclide therapies. The expected outcome of Aim 2 is a treatment planning strategy for future RE patients based on the tools and models developed here. This has the potential for significant positive impact because a personalized dosimetry -guided approach to RE is highly likely to substantially improve outcome compared to current practice.
The proposed research is expected to result in new and accurate tools for quantitative imaging-based dosimetry that have the potential to contribute significantly toward improving internal radionuclide therapies that utilize Yttrium-90. In addition, for Yttrium-90 microsphere radioembolization in primary liver cancer and metastases, predictive dosimetric models for efficacy and toxicity that can be used for therapy optimization in future clinical trials/practice are expected. This study is relevant to public health because a dosimetry-guided personalized approach to Yttrium-90 therapies is highly likely to substantially improve patient outcomes compared to current standard practice.
|Dewaraja, Yuni K; Chun, Se Young; Srinivasa, Ravi N et al. (2017) Improved quantitative 90 Y bremsstrahlung SPECT/CT reconstruction with Monte Carlo scatter modeling. Med Phys 44:6364-6376|