An Evaluation of the Impact of MR Image Slice Thickness on the Accuracy of High-Dose-Rate Brachytherapy for Gynaecological Cancers
Bibliographic record
Abstract
High-dose-rate (HDR) Magnetic Resonance (MR) guided brachytherapy (BT) is rapidly becoming the standard for treatment of locally advanced cervical cancer, globally. MR is an integral aspect of this treatment, enabling the level of soft tissue visualization required for precise delineation of organ and target contours with respect to the BT applicator or needles during treatment planning. The optimal slice thickness for MR datasets, and the role of super-resolved datasets are questions yet to be investigated. A digital phantom-based study assessed the impact of slice thickness on volumetric and geometric uncertainties in traditional MR datasets and estimated the resultant dosimetric uncertainty. Datasets with traditional slice thicknesses produced uncertainties up to 27% of the imaged structure volume, and contour uncertainty up to one third of the slice thickness This resulted in the exceeding of the American Association of Physicists in Medicine’s (AAPM) recommended dosimetric uncertainty in HDR BT. Trilinearly interpolated datasets reduced these uncertainties substantially, allowing imaging with 2.7 mm coarser slices while conferring an imaging time reduction of 6 minutes. The results of this thesis demonstrate that the recommended range of slice thicknesses introduces uncertainties on a level known to impact dosimetry more than 9%. Trilinearly interpolated datasets may thus confer benefit in this clinical setting.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".