A New Computational Algorithm for Treatment Planning of Targeted Radionuclide Therapy

Supervision by: Dr. Kuangyu Shi, Shadi Albarqouni, Prof. Nassir Navab
Contact: Dr. Kuangyu Shi, Email: shi@lrz.tum.de

Abstract

Targeted radionuclide therapy [1] is a cutting-edge molecular therapy enabling precise treatment of several tumors such as neuroendocrine carcinoma [2,3] or prostate cancer [4]. Significantly extended survival and markedly reduced complication [5] has been achieved and it is increasingly listed in several guidelines in oncological practices.
In contrast to conventional external beam radiotherapy, targeted radionuclide therapy can selectively deliver radiation to tumors or target organs. The cell-targeting protein is combined with a small amount of radionuclide. When injected into the patient’s bloodstream, this radiopeptide travels to and binds to tumor cells, delivering a high dose of radiation to the cancer. This pharmacokinetic-driven dose-delivery procedure however makes the treatment planning extremely difficult [6,7] due to two reasons: 1) the limited dynamic information obtained in planning imaging; 2) the limited consideration of complicated physiological individualities.
The proposed master thesis will develop a novel computational method to extrapolate the missing kinetic information and integrate more individual information to improve the dosimetry planning of targeted radionuclide therapy. Pharmacokinetic modeling and information retrieval will be applied to optimize the kinetic estimation and dose calculation.
The student is expected to have good knowledge in medical imaging. Advanced skill in python programming is required.

References

[1] Gill et al Lancet 2017
[2] Kwekkeboom et al. J Clin Onco 2005
[3] Öberg et al. Ann Oncol 2012
[4] Rahbar et al. J Nucl Med 2017
[5] Strosberg et al. New Engl J Med 2017
[6] Kletting et al. J Nucl Med 2017
[7] Heribert et al. J Nucl Med 2017