Sci‐AM2 Sat ‐ 03: Anatomy‐based MLC field optimization for the treatment of gynecologic malignancies
Bibliographic record
Abstract
To evaluate the use of a new inverse planning system with anatomy‐based field segmentation, as an alternative between “4‐field box” and beamlet‐based IMRT, to treat whole pelvis of women with resected gynecologic malignancies. A class solution has been elaborated with the assistance of an in‐house optimization tool named Ballista. This inverse planning system can generate anatomy‐based MLC fields and simultaneously optimize their orientation and weight. The selected geometry consists of 7 coplanar and 2 noncoplanar incidences. For 10 patients planned to receive 45Gy for resected endometrial or cervix neoplasia, target volume and organ at risk (bowel, region “at risk to find bowel” (B‐RAR), bladder, rectum, bone marrow) were delineated. Using the Pinnacle3 planning system, four plans were generated for each patient: conventional 4‐field, enlarged 4‐field (aperture shaped to PTV),“step‐and‐shoot” IMRT and Ballista plans. Dose‐volume histograms, number of segments and monitor units (MU) were analyzed. Statistical significance is based on Student's paired t‐test. The mean volume of B‐RAR receiving 45Gy was: 4‐field, 49.7±7.1%; enlarged 4‐field, 63.4±5.8%; IMRT, 26.4±3.1%; Ballista, 29.0±3.0%. No statistical difference was noted between the ability of IMRT and Ballista to spare bowel (p=0.14), while both plans were better than 4‐field (p<0.001). The mean number of segments for Ballista was 33.3±2.3 vs 128.6±2.6 for IMRT and the mean number of MU was 325.0±11.8 vs 731.5±25.0. Weight optimization, with anatomy‐based MLC fields, is a good alternative between manual planning and IMRT for the treatment of gynecologic malignancies. Clinical results of treatment tolerance will follow.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".