Cellular dosimetry of 111In using Monte Carlo N-particle (MCNP) computer code: Comparison with reported analytical methods and predictive value
Bibliographic record
Abstract
1858 Objectives Our goal was to compare MCNP self- and cross-doses to the nucleus of breast cancer (BC) cells from the Auger electron-emitter 111In to those calculated by reported analytical methods (Goddu et al. and Farragi et al.). We also incorporated different cell geometries and experimental conditions and determined if MCNP could predict clonogenic survival of BC cells exposed in vitro to 111In-DTPA-hEGF. Methods MCNP was used to simulate the transport of all electrons emitted by 111In from locations at cell surface (CS), cytoplasm (CY) or nucleus (N). The doses to N per decay (S values) were calculated for single, closely packed monolayer cells or clusters of cells with various cell and nuclear dimensions. Results For self-doses, MCNP SN→N-values agreed well with those of Goddu et al. [relative difference (RD) of 0.5– 4.0%] and Faraggi et al. (RD -1.1– -2.4%). MCNP S-values of CY and CS to N compared fairly well with the reported values for cells with radii >7 um (RD -7.9 –13.5% for Goddu et al.; -4.9–5.8% for Faraggi et al.). S-values of monolayer cells were significantly different than those of single cell and cell clusters. The predicted cell survival based on calculated S values for monolayer MDA-MB-468, MDA-MB-231 and MCF-7 cells, and cell fractionation data and gamma-ray survival correlated well with experimental data (RD 3.1, -1.0 and 1.7%). Conclusions MCNP is feasible and can reliably assess cellular dosimetry of Auger electron-emitters. S values for monolayer cells predicted the cell survival of BC cells after exposure to 111In-DTPA-hEGF better than S values of Goddu et al. and Faraggi et al. Research Support Canadian Breast Cancer Research Alliance (Grant No. 19374).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".