Fast beta‐emitter Monte Carlo simulations and full patient dose calculations of targeted radionuclide therapy: introducing egs_mird. Issue 9 (15th July 2022)

• Record Type: Journal Article
• Authors: Martinov, Martin P.; Opara, Chidera; Thomson, Rowan M.; Lee, Ting‐Yim
• Is Part Of: Medical physics. Volume 49:Issue 9(2022)
• Page Start: 6137
• Page End: 6149
• Publication Date: 2022-07-15
• Subjects: full patient modeling -- Monte Carlo -- targeted radionuclide therapy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15786
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Background: Targeted radionuclide therapy (TRT) is a fast‐growing field garnering much interest, with several clinical trials currently underway, that has a steady increase in development of treatment techniques. Unfortunately, within the field and many clinical trials, the dosimetry calculation techniques used remain relatively simple, often using a mix of S‐value calculations and kernel convolutions. Purpose: The common TRT calculation techniques, although very quick, can often ignore important aspects of patient anatomy and radionuclide distribution, as well as the interplay there‐in. This paper introduces egs_mird, a new Monte Carlo (MC) application built in EGSnrc which allows users to model full patient tissue and density (using clinical CT images) and radionuclide distribution (using clinical PET images) for fast and detailed dose TRT calculation. Methods: The novel application egs_mird is introduced along with a general outline of the structure of egs_mird simulations. The general structure of the code, and the track‐length (TL) estimator scoring implementation for variance reduction, is described. A new egs++ source class egs_internal_source, created to allow detailed patient‐wide source distribution, and a modified version of egs_radionuclide_source, changed to be able to work with egs_internal_source, are also described. The new code is compared to other MC calculations of S‐values kernels of 131 I, 90 Y, and 177 Lu in the literature, along with further self‐validation using a histogram variant of the electron Fano test. Several full patient prostate 177 Lu TRT prostate cancer treatment simulations are performed using a single set of patient DICOM CT and [ 18 F]‐DCFPyL PET data. Results: Good agreement is found between S‐value kernels calculated using egs_mird with egs_internal_source and those found in the literature. Calculating 1000 doses (individual voxel uncertainties of ∼0.05%) in a voxel grid Fano test for monoenergetic 500 keV electrons and 177 Lu electrons results in 94% and 99% of the doses being within 0.1% of the expected dose, respectively. For a hypothetical 177 Lu treatment, patient prostate, rectum, bone marrow, and bladder dose volume histograms (DVHs) results did not vary significantly when using the TL estimator and not modeling electron transport, modeling bone marrow explicitly (rather than using generic tissue compositions), and reducing activity to voxels containing partial or full calcifications to half or none, respectively. Dose profiles through different regions demonstrate there are some differences with model choices not seen in the DVH. Simulations using the TL estimator can be completed in under 15 min (∼30 min when using standard interaction scoring). Conclusion: This work shows egs_mird to be a reliable MC code for computing TRT doses as realistically as the patient Computed Tomography (CT) and Positron Emission Tomography (PET) data allow. Furthermore, the code can compute doses to sub‐1% uncertainty within 15 min, with little to no optimization. Thus, this work supports the use of egs_mird for dose calculations in TRT.