SU‐F‐T‐420: Dosimetry Comparison of Advanced External Beam Radiation Treatment Modalities to Brachytherapy Treatments in Patients with Cervical Cancer
Bibliographic record
Abstract
Purpose: A retrospective comparison of dose distributions achievable by High dose rate brachytherapy (HDRBT), Helical TomoTherapy (TOMO), CyberKnife (CK) and RapidArc (RA) in locally advanced inoperable cervical cancer patients is presented. Methods: Five patients with advanced stage cervical carcinoma were selected for this study after a full course of external beam radiotherapy (EBRT), chemotherapy and HDR Brachytherapy. To highlight any significant similarities/differences in dose distributions, high‐risk clinical target volume (HRCTV) coverage, organs at risk (OAR) sparing, and machine specific delivery limitations, we used D90 (dose received by 90% of the volume) as the parameter for HRCTV coverage as recommended by the GEC‐ESTRO Working Group. We also compared both integral and differential dose volume histograms (DVH) between different dose distributions treatment modalities for HRCTV and OAR. Results: TOMO and RA provided the most conformal dose distributions to HRCTV. Median doses (in Gy) to organs at risk were; for rectal wall: 1.7±0.6, 2.5±0.6,1.2±0.3, and 1.5±0.6, and for bladder wall: 1.6±0.1, 2.4±0.4, 0.8±0.6, and 1.5±0.5, for HDRBT, TOMO, CK, and RA, respectively. Conclusion: Contemporary EBRT modalities might be able to replace brachytherapy treatments for cervix cancer. While brachytherapy dose distributions feature high dose gradients, EBRT modalities provide highly conformal dose distributions to the target. However, it is still not clear whether a highly conformal dose or high gradient dose is more clinically relevant for the HRCTV in cervix cancer patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".