SU‐E‐T‐670: Using Overlap Volume Histogram Analysis of a Prior Plan Dataset to Generate Clinically Acceptable Plans for CyberKnife Robotic Radiosurgery Treatment of Localized Prostate Cancer
Bibliographic record
Abstract
Purpose: CyberKnife offers the potential benefits of non‐isocentric, non‐coplanar treatment delivery. However, its planning is a laborious manual, trial‐and‐error process. This study is to investigate whether an overlap volume histogram (OVH)‐driven planning approach can produce clinically acceptable plans for treatment of localized prostate cancer. Methods: It is assumed that given consistent target coverage, patients with a relevant target‐organ spatial relationship should have similar organ sparing. The OVH is used to characterize the 3‐D spatial relationship between an organ and a target. A database containing the OVH and DVH of prior plans is built to serve as an external reference. During the initial planning, the OVH is used to search through the database to find a prior patient group whose target‐organ relationship is related to that of a new patient. The planning objectives for the new patient are then estimated from the group and input into the CyberKnife TPS for optimization. To demonstrate the effectiveness of the method, the plans of 12 prostate patients (prescription: 36.25Gy in 5 fractions) are generated by the OVH approach and compared to the corresponding clinical plans using the in‐house dosimetric guidelines. Results: Physicians confirm that OVH plans are clinically acceptable. OVH plans: on average, Vcc(37Gy) and V(18.12Gy) to the bladder decrease 1cc and 6% (p<0.05); Vcc(36Gy) and V(18.12Gy) to the rectum decrease 0.02cc and 4.3% (p=0.3); Vcc(40Gy) to the prostatic urethra decreases 0.08cc (p=0.01); V(14.5Gy) to the femur heads decreases 0.58% (p=0.01). V(37Gy) to the membranous urethra increases from 23.1% to 24.5% (p=0.75); V(29.5Gy) to the penile bulb increases from 5.5% to 10.7% (p=0.35); V(36.25Gy) to PTV increases 0.2% (p=0.4); the estimated delivery time deceases 3.5 minutes (p=0.01). Conclusion: With respect to planner‐created clinical plans, this approach offers an alternative way to generate clinically acceptable plans. It advances the possibility of automated CyberKnife planning. C. Sims, employed by Accuray, Inc;S.P Collins, Accuray clinical consultant
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".