TH-E-BRA-08: MR Guided Radiotherapy for Cervix Cancer Treatment; Retrospective Feasibility Study
Bibliographic record
Abstract
Purpose: To evaluate the efficacy of on-line MR guided radiotherapy for cervix cancer patients. MR guidance was simulated in order to optimize the fractional dose to the on-line targets. Methods: 33 cervical cancer patients underwent planning and weekly pelvic MRI scans during radiotherapy. In the previous retrospective adaptive planning study using 3 mm PTV margin, 5 over 33 patient cases were identified and enrolled in this study in which the coverage of GTV/CTVs was not acceptable with single IMRT adaptation with bone matching. MR guidance was simulated in order to maximize online high risk CTV (HRCTV) volume to be within 95% of the prescription dose (95p). Fractional dose after the image guidance was calculated, and was deformed back to the reference (planning) image for dose accumulation. Accumulated dose of the proposed technique was compared with that of current standard image guidance technique, bone matching in terms of the target coverage (cervix, GTV, HRCTV, lower uterus, parametria, and upper vagina) and OAR sparing (bladder, bowl, rectum, and sigmoid). Target coverage was considered acceptable if 95p dose or more was delivered to 98% of the target volume. OAR sparing was evaluated with accumulated V45 and D2cc. Results: On line MR based soft tissue guidance proposed in this study achieved the acceptance of target coverage to 97% from 53% (bone matching). Dose delivery to HRCTV and lower uterus was significantly improved (p<0.001, paired t-test). The mean D2cc and V45 were reduced in bladder, rectum and sigmoid compared to bone matching. Conclusions: The retrospective study revealed that the on-line MR based soft-tissue image guidance is very effective for cervix cancer treatment. The technique significantly and successfully improved target coverage for the most difficult patient group identified from the previous study. Statistically significant improvement in OAR sparing was also noted. Anna Lundin and Henrik Rehbinder are both employees and shareholders of RaySearch Laboratories AB. All other authors have no conflicts to report.
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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 | 0.001 |
| 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) | 0.002 | 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".