Impact of cell geometry, cellular uptake region, and tumour morphology on 225Ac and 177Lu dose distributions in prostate cancer
Bibliographic record
Abstract
Abstract Background Radiopharmaceutical therapy with 225 Ac- and 177 Lu-PSMA has shown promising results for the treatment of prostate cancer. However, the distinct physical properties of alpha and beta radiation elicit varying cellular responses, which could be influenced by factors such as tumour morphology. In this study, we use simulations to examine how cell geometry, region of pharmaceutical uptake within the cell to model different internalization fractions, and the presence of tumour hypoxia and necrosis impact nucleus absorbed doses and dose heterogeneity with 225 Ac and 177 Lu. We also develop nucleus absorbed dose kernels for application to autoradiography images. Methods We used the GATE Monte Carlo software to simulate three geometries of LNCaP prostate cancer cells (spherical, cubic, and ovoid) with activity of 225 Ac or 177 Lu internalized in the cytoplasm or bound to the extracellular membrane. Nucleus S-values were calculated for each geometry, source region, and isotope. The cell models were used to create nucleus absorbed dose kernels for each source region describing the dose to each nucleus in a cell layer, which were applied to simulated tumours composed of normoxic, hypoxic, or necrotic cancer cells to obtain dose rate maps. Absorbed doses within the tumours and dose heterogeneity were analyzed for each tumour morphology and isotope. Cell geometry made a minimal impact on S-values to the nucleus, however internalization resulted in higher nucleus doses. Applying the kernels to the simulated tumour maps showed that doses to each cell type varied between 225 Ac and 177 Lu depending on tumour morphology. Dose heterogeneity within tumours was slightly higher with 225 Ac, however the tumour morphology made a larger impact on dose heterogeneity compared to the choice of isotope, with hypoxic and necrotic tumours having very heterogeneous dose distributions. Conclusions Cell geometry simplifications may still allow robust results in simulation studies. Furthermore, the morphology of the tumour itself may make a larger impact on treatment response compared to other variables such as ratio of internalization. Finally, nucleus absorbed dose kernels were created that could enable microdosimetric studies with autoradiography.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".