Determination of subcellular compartment sizes for estimating dose variations in radiotherapy
Bibliographic record
Abstract
The variation in specific energy absorbed to different cell compartments caused by variations in size and chemical composition is poorly investigated in radiotherapy. The aim of this study was to develop an algorithm to derive cell and cell nuclei size distributions from 2D histology samples, and build 3D cellular geometries to provide Monte Carlo (MC)-based dose calculation engines with a morphologically relevant input geometry. Stained and unstained regions of the histology samples are segmented using a Gaussian mixture model, and individual cell nuclei are identified via thresholding. Delaunay triangulation is applied to determine the distribution of distances between the centroids of nearest neighbour cells. A pouring simulation is used to build a 3D virtual tissue sample, with cell radii randomised according to the cell size distribution determined from the histology samples. A slice with the same thickness as the histology sample is cut through the 3D data and characterised in the same way as the measured histology. The comparison between this virtual slice and the measured histology is used to adjust the initial cell size distribution into the pouring simulation. This iterative approach of a pouring simulation with adjustments guided by comparison is continued until an input cell size distribution is found that yields a distribution in the sliced geometry that agrees with the measured histology samples. The thus obtained morphologically realistic 3D cellular geometry can be used as input to MC-based dose calculation programs for studies of dose response due to variations in morphology and size of tumour/healthy tissue cells/nuclei, and extracellular material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".