Influence of dosimetry method on bone lesion absorbed dose estimates in PSMA therapy: application to mCRPC patients receiving Lu-177-PSMA-I&T
Bibliographic record
Abstract
Abstract Background Patients with metastatic, castration-resistant prostate cancer (mCRPC) present with an increased tumor burden in the skeleton. For these patients, Lutetium-177 (Lu-177) radioligand therapy targeting the prostate-specific membrane antigen (PSMA) has gained increasing interest with promising outcome data. Patient-individualized dosimetry enables quantification of therapy success with the aim of minimizing absorbed dose to organs at risk while maximizing absorbed dose to tumors. Different dosimetric approaches with varying complexity and accuracy exist for this purpose. The relatively simple OLINDA method applied to tumors assumes a homogeneous activity distribution in a sphere with unit density. Voxel S value (VSV) approaches can account for heterogeneous activities but are simulated for a specific tissue. Full patient-individual Monte Carlo (MC) dose simulation addresses both, heterogeneous activity and density distributions. Subsequent CT-based density correction has the potential to overcome the assumption of homogeneous density in OLINDA and VSV methods, which could be a major limitation for the application in bone metastases with heterogeneous density. The aim of this investigation is a comparison of these methods for bone lesion dosimetry in mCRPC patients receiving Lu-177-PSMA therapy. Results In total, 289 bone lesions in 15 mCRPC patients were analyzed. Percentage deviation (PD) of absorbed lesion doses compared to full MC was + 7 ± 13% (min: -60%; max: +47%) for the OLINDA unit density sphere model. With an applied CT-based density weighting to account for density differences in bone lesions, PD was − 15 ± 6% (min: -54%; max: -2%). For a soft tissue VSV approach, large PDs of + 16 ± 13% (min: -56%; max: +57%) were found; after voxel-wise density correction this was reduced to -5 ± 2% (min: -15%; max: -2%). The use of a combination of standard soft tissue and cortical bone VSVs showed deviations of -35 ± 8% (min: -76%; max: +5%). With additional voxel-wise density weighting, the PD was − 3 ± 2% (min: -13%; max: 0%). Conclusion Based on our bone lesion dosimetry results, a VSV approach with subsequent CT-based, voxel-wise density correction enabled dose estimates, that closely replicate computationally-demanding gold-standard full MC dose simulations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".