A proof-of-concept study of personalized dosimetry for targeted radioligand therapy using pre-treatment diagnostic dynamic PET/CT and Monte Carlo simulation
Bibliographic record
Abstract
Purpose Theranostics integrates diagnostic imaging (e.g., 18F-PSMA-1007 PET) with targeted radioligand therapy (TRT; e.g., 177Lu-PSMA-617), but personalized dosimetry remains challenging due to complex dose calculations. Current methods like Monte Carlo simulations are accurate but require impractical post-treatment multi-day SPECT/CT imaging. Here we establish a proof-of-concept framework using pre-treatment PET/CT to predict TRT doses via graphical analysis and Monte Carlo modeling, eliminating the need for serial imaging. Our voxel-based approach demonstrates significant dose variations in prostate cancer patients under standard TRT with a one-size-fits-all radioligand dose, enabling pre-treatment dose personalization—a critical step toward precision radiotheranostics. Methods Dynamic PET/CT scans obtained with 18F-DCFPyL over 22 min from six prostate cancer patients were used in this study. Tissue time-integrated activity (TIA), that is, the total number of decays from the accumulated radioligand, was calculated as the product of the area under the curve (AUC) of an extrapolated arterial time activity curve (TAC) and the Logan distribution volume (LDV) determined by graphical analysis of tissue TAC. The resulting 177Lu-PSMA-617 TIA map, along with the CT-derived tissue geometry, density, and composition maps, were used to calculate the absorbed dose in the prostate tumor, overall prostate, and bone marrow in the femurs by egs_mird, a Monte Carlo-based absorbed dose calculation. Biological effective dose (BED) was calculated using the voxel-based absorbed dose and an extended radiobiological linear quadratic model accounting for dose rate, DNA repair, and clonal repopulation. Results Voxel-wise LDV graphical analysis demonstrated strong linearity, with an interpatient mean R2 of 0.999973 ± 0.000047 (mean ± SD). Using a one-size-fits-all radioligand dosing approach, significant variations in absorbed dose were observed: 10.4 ± 4.9 Gy/GBq in tumors, 5.1 ± 0.7 Gy/GBq in normal prostate tissue, and 1.0 ± 0.3 Gy/GBq in bone marrow. These variations were influenced by differences in both LDV and arterial TACs among the patients—the former due to radioligand binding avidity and the latter to tumor burden and clearance rates. Conclusion We developed a framework for personalized TRT dose calculations using pre-treatment diagnostic PET/CT scans, eliminating the need for post-treatment SPECT/CT scans via the LDV-based method. This approach addresses variability in tumor and organ-at-risk doses from one-size-fits-all radioligand dosing, enabling optimized pre-treatment planning and integration with external beam radiation therapy (EBRT) or brachytherapy, if indicated, for precise and effective therapy. This method shows promise but requires further validation through larger studies and direct comparison with post-treatment dosimetry to confirm its accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".