WE‐C‐BRB‐11: On the Biological Basis for Competing Macroscopic Dose Descriptors for Kilovoltage Dosimetry
Bibliographic record
Abstract
Purpose: To determine which competing macroscopic dose descriptor best tracks absorbed dose to biologically relevant subcellular targets via Monte Carlo analysis of cellular models for a variety of normal and cancerous tissues, and evaluate relations between bulk dose‐specification media and absorbed doses to cellular targets for kilovoltage radiation. Methods: The relative proportions of water, proteins, inorganic elements, and lipids in cell cytoplasm and nuclei, extracellular fluid, and the corresponding average bulk media were determined for normal and cancerous tissues through a literature review. Mass‐energy absorption coefficients, attenuation coefficients, and stopping powers for these media were calculated. Representative models of cells and cell clusters for normal and cancerous tissues were developed; doses to cellular targets were computed with Monte Carlo (MC) simulation for photon sources (energies between 20 keV and 380 keV) and compared to bulk medium dose descriptors. Results: Cells contain significant and varying mass fractions of proteins, inorganic elements, and lipids (adipocytes only). Variations in mass energy absorption coefficients for cytoplasmic and nuclear media as large as 10% compared to water are observed for sub‐50 keV photon energies (I‐125 and Pd‐103). In adipose tissues, 10% differences persist to 90 keV. Doses to cellular targets differ by up to 10% compared to doses to the corresponding average bulk medium or to water. The relationships between cellular target doses and doses to the bulk medium are sensitive to source energy and cell morphology, particularly for low energy brachytherapy. Conclusions: There are significant variations in cellular morphology (composition, size) with cell type; cells are not generally radiologically water equivalent. Thus neither dose to bulk medium nor dose to water in inhomogeneous macroscopic media quantitatively tracks energy imparted to biologically relevant subcellular targets for the range of cellular geometries investigated and kilovoltage photon sources.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".