Specific absorbed fractions and radionuclide S-values for tumors of varying size and composition

Computational Nuclear Oncology Toward Precision Radiopharmaceutical Therapies: Current Tools, Techniques, and Uncharted Territories

Radiopharmaceutical therapy (RPT), with its targeted delivery of cytotoxic ionizing radiation, demonstrates significant potential for treating a wide spectrum of malignancies, with particularly unique benefits for metastatic disease. There is an opportunity to optimize RPTs and enhance the precision of theranostics by moving beyond a one-size-fits-all approach and using patient-specific image-based dosimetry for personalized treatment planning. Such an approach, however, requires accurate methods and tools for the mathematic modeling and prediction of dose and clinical outcome. To this end, the SNMMI AI-Dosimetry Working Group is promoting the paradigm of computational nuclear oncology: mathematic models and computational tools describing the hierarchy of etiologic mechanisms involved in RPT dose response. This includes radiopharmacokinetics for image-based internal dosimetry and radiobiology for the mapping of dose response to clinical endpoints. The former area originates in pharmacotherapy, whereas the latter originates in radiotherapy. Accordingly, models and methods developed in these predecessor disciplines serve as a foundation on which to develop a repurposed set of tools more appropriate to RPT. Over the long term, this computational nuclear oncology framework also promises to facilitate widespread cross-fertilization of ideas between nuclear medicine and the greater mathematic and computational oncology communities.

The MIRD Schema for Radiopharmaceutical Dosimetry: A Review

Internal dosimetry evaluates the amount and spatial and temporal distributions of radiation energy deposited in tissue from radionuclides within the body. Historically, nuclear medicine had been largely a diagnostic specialty, and the implicitly performed risk-benefit analyses have been straightforward, with relatively low administered activities yielding important diagnostic information whose benefit far outweighs any potential risk associated with the attendant normal-tissue radiation doses. Although dose estimates based on anatomic models and population-average kinetics in this setting may deviate rather significantly from the actual normal-organ doses for individual patients, the large benefit-to-risk ratios are very forgiving of any such inaccuracies. It is in this context that the MIRD schema was originally developed and has been largely applied. The MIRD schema, created and maintained by the MIRD committee of the Society of Nuclear Medicine and Molecular Imaging, comprises the notation, terminology, mathematic formulas, and reference data for calculating tissue radiation doses from radiopharmaceuticals administered to patients. However, with the ongoing development of new radiopharmaceuticals and the increasing therapeutic application of such agents, internal dosimetry in nuclear medicine and the MIRD schema continue to evolve-from population-average and organ-level to patient-specific and suborgan to voxel-level to cell-level dose estimation. This article will review the basic MIRD schema, relevant quantities and units, reference anatomic models, and its adaptation to small-scale and patient-specific dosimetry.

MIRD Pamphlet No. 28, Part 1: MIRDcalc-A Software Tool for Medical Internal Radiation Dosimetry

Medical internal radiation dosimetry constitutes a fundamental aspect of diagnosis, treatment, optimization, and safety in nuclear medicine. The MIRD committee of the Society of Nuclear Medicine and Medical Imaging developed a new computational tool to support organ-level and suborgan tissue dosimetry (MIRDcalc, version 1). Based on a standard Excel spreadsheet platform, MIRDcalc provides enhanced capabilities to facilitate radiopharmaceutical internal dosimetry. This new computational tool implements the well-established MIRD schema for internal dosimetry. The spreadsheet incorporates a significantly enhanced database comprising details for 333 radionuclides, 12 phantom reference models (International Commission on Radiological Protection), 81 source regions, and 48 target regions, along with the ability to interpolate between models for patient-specific dosimetry. The software also includes sphere models of various composition for tumor dosimetry. MIRDcalc offers several noteworthy features for organ-level dosimetry, including modeling of blood source regions and dynamic source regions defined by user input, integration of tumor tissues, error propagation, quality control checks, batch processing, and report-preparation capabilities. MIRDcalc implements an immediate, easy-to-use single-screen interface. The MIRDcalc software is available for free download (www.mirdsoft.org) and has been approved by the Society of Nuclear Medicine and Molecular Imaging.

Dosimetric variability across a library of computational tumor phantoms

In radiopharmaceutical therapy, dosimetry-based treatment planning and response evaluation require accurate estimates of tumor-absorbed dose. Tumor dose estimates are routinely derived using simplistic spherical models, despite the well-established influence of tumor geometry on the dosimetry. Moreover, the degree of disease invasiveness correlates with departure from ideal geometry; malignant lesions often possess lobular, spiculated, or otherwise irregular margins in contrast to the commonly regular or smooth contours characteristic of benign lesions. To assess the effects of tumor shape, size, and margin contour on absorbed dose, an array of tumor geometries was modeled using computer-aided design software, and the models were used to calculate absorbed dose per unit of time-integrated activity (i.e., S values) for several clinically applied therapeutic radionuclides (90Y, 131I, 177Lu, 211At, 225Ac, 213Bi, and 223Ra). Methods: Three-dimensional tumor models of several different shape classifications were generated using Blender software. Ovoid shapes were generated using axial scaling. Lobulated, spiculated, and irregular contours were generated using noise-based mesh deformation. The meshes were rigidly scaled to different volumes, and S values were then computed using PARaDIM software. Radiomic features were extracted for each shape, and the impact on S values was examined. Finally, the systematic error present in dose calculations that model complex tumor shapes versus equivalent-mass spheres was estimated. Results: The dependence of tumor S values on shape was largest for extreme departures from spherical geometry and for long-range emissions (e.g., 90Y β-emissions). S values for spheres agreed reasonably well with lobulated, spiculated, or irregular contours if the surface perturbation was small. For marked deviations from spherical shape and small volumes, the systematic error of the equivalent-sphere approximation increased to 30%–75% depending on radionuclide. The errors were largest for shapes with many long spicules and for spherical shells with a thickness less than or comparable to the particle range in tissue. Conclusion: Variability in tumor S values as a function of tumor shape and margin contour was observed, suggesting use of contour-matched phantoms to improve the accuracy of tumor dosimetry in organ-level dosimetry paradigms. Implementing a library of tumor phantoms in organ-level dosimetry software may facilitate optimization strategies for personalized radionuclide therapies.

Patient-specific dosimetry adapted to variable number of SPECT/CT time-points per cycle for 177 Lu-DOTATATE therapy

BACKGROUND

The number of SPECT/CT time-points is important for accurate patient dose estimation in peptide receptor radionuclide therapy. However, it may be limited by the patient's health and logistical reasons. Here,  an image-based dosimetric workflow adapted to the number of SPECT/CT acquisitions available throughout the treatment cycles was proposed, taking into account patient-specific pharmacokinetics and usable in clinic for all organs at risk.

METHODS

Thirteen patients with neuroendocrine tumors were treated with four injections of 7.4 GBq of177 Lu-DOTATATE. Three SPECT/CT images were acquired during the first cycle (1H, 24H and 96H or 144H post-injection) and a single acquisition (24H) for following cycles. Absorbed doses were estimated for kidneys (LK and RK), liver (L), spleen (S), and three surrogates of bone marrow (L2 to L4, L1 to L5 and T9 to L5) that were compared. 3D dose rate distributions were computed with Monte Carlo simulations. Voxel dose rates were averaged at the organ level. The obtained Time Dose-Rate Curves (TDRC) were fitted with a tri-exponential model and time-integrated. This method modeled patient-specific uptake and clearance phases observed at cycle 1. Obtained fitting parameters were reused for the following cycles, scaled to the measure organ dose rate at 24H. An alternative methodology was proposed when some acquisitions were missing based on population average TDRC (named STP-Inter). Seven other patients with three SPECT/CT acquisitions at cycles 1 and 4 were included to estimate the uncertainty of the proposed methods.

RESULTS

Absorbed doses (in Gy) per cycle available were: 3.1 ± 1.1 (LK), 3.4 ± 1.5 (RK), 4.5 ± 2.8 (L), 4.6 ± 1.8 (S), 0.3 ± 0.2 (bone marrow). There was a significant difference between bone marrow surrogates (L2 to L4 and L1 to L5, Wilcoxon's test: p value < 0.05), and while depicting very doses, all three surrogates were significantly different than dose in background (p value < 0.01). At cycle 1, if the acquisition at 24H is missing and approximated, medians of percentages of dose difference (PDD) compared to the initial tri-exponential function were inferior to 3.3% for all organs. For cycles with one acquisition, the median errors were smaller with a late time-point. For STP-Inter, medians of PDD were inferior to 7.7% for all volumes, but it was shown to depend on the homogeneity of TDRC.

CONCLUSION

The proposed workflow allows the estimation of organ doses, including bone marrow, from a variable number of time-points acquisitions for patients treated with177 Lu-DOTATATE.

An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 1: Design and Early Results of the SNMMI Dosimetry Challenge

In this work, we present details and initial results from a 177Lu dosimetry challenge that has been designed to collect data from the global nuclear medicine community aiming at identifying, understanding, and quantitatively characterizing the consequences of the various sources of variability in dosimetry. Methods: The challenge covers different approaches to performing dosimetry: planar, hybrid, and pure SPECT. It consists of 5 different and independent tasks to measure the variability of each step in the dosimetry workflow. Each task involves the calculation of absorbed doses to organs and tumors and was meant to be performed in sequential order. The order of the tasks is such that results from a previous one would not affect subsequent ones. Different sources of variability are removed as the participants advance through the challenge by giving them the data required to begin the calculations at different steps of the dosimetry workflow. Data from 2 patients after a therapeutic administration of 177Lu-DOTATATE were used for this study. The data are hosted in Deep Blue Data, a data repository service run by the University of Michigan. Participants submit results in standardized spreadsheets and with a short description summarizing their methods. Results: In total, 178 participants have signed up for the challenge, and 119 submissions have been received. Sixty percent of submissions have used voxelized dose methods, with 47% of those using commercial software. In initial analysis, the volume of organs showed a variability of up to 49.8% whereas for lesions this was up to 176%. Variability in time-integrated activity was up to 192%. Mean absorbed doses varied up to 57.7%. Segmentation is the step that required the longest time to complete, with a median of 43 min. The median total time to perform the full calculation was 89 min. Conclusion: To advance dosimetry and encourage its routine use in radiopharmaceutical therapy applications, it is critical that dosimetry results be reproducible across centers. Our initial results provide insights into the variability associated with performing dose calculations. It is expected that this dataset, including results from future stages, will result in efforts to standardize and harmonize methods and procedures.