Influence of image slice thickness on rectal dose–response relationships following radiotherapy of prostate cancer
Bibliographic record
Abstract
When pooling retrospective data from different cohorts, slice thicknesses of acquired computed tomography (CT) images used for treatment planning may vary between cohorts. It is, however, not known if varying slice thickness influences derived dose-response relationships. We investigated this for rectal bleeding using dose-volume histograms (DVHs) of the rectum and rectal wall for dose distributions superimposed on images with varying CT slice thicknesses. We used dose and endpoint data from two prostate cancer cohorts treated with three-dimensional conformal radiotherapy to either 74 Gy (N = 159) or 78 Gy (N = 159) at 2 Gy per fraction. The rectum was defined as the whole organ with content, and the morbidity cut-off was Grade ≥2 late rectal bleeding. Rectal walls were defined as 3 mm inner margins added to the rectum. DVHs for simulated slice thicknesses from 3 to 13 mm were compared to DVHs for the originally acquired slice thicknesses at 3 and 5 mm. Volumes, mean, and maximum doses were assessed from the DVHs, and generalized equivalent uniform dose (gEUD) values were calculated. For each organ and each of the simulated slice thicknesses, we performed predictive modeling of late rectal bleeding using the Lyman-Kutcher-Burman (LKB) model. For the most coarse slice thickness, rectal volumes increased (≤18%), whereas maximum and mean doses decreased (≤0.8 and ≤4.2 Gy, respectively). For all a values, the gEUD for the simulated DVHs were ≤1.9 Gy different than the gEUD for the original DVHs. The best-fitting LKB model parameter values with 95% CIs were consistent between all DVHs. In conclusion, we found that the investigated slice thickness variations had minimal impact on rectal dose-response estimations. From the perspective of predictive modeling, our results suggest that variations within 10 mm in slice thickness between cohorts are unlikely to be a limiting factor when pooling multi-institutional rectal dose data that include slice thickness variations within this range.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".