SU‐E‐T‐593: Comparison of Three IMRT Adaptive Methods: The Case of Prostate Cancer
Bibliographic record
Abstract
Purpose: Independant movements of prostate and pelvic lymph nodes during IMRT can limit margin reduction and affect the protection of organs‐at‐risk (OAR). In this study we perform an analysis of three adaptive treatments that combine information from both bony and gold marker registrations. The efficiency of those treatments against interfraction prostate movements was evaluated. Methods: A retrospective study was conducted on four prostate cancer patients with 5 to 10 daily CBCTs. Clinical target volumes (CTVs) consisting of pelvic lymph nodes, prostate and seminal vesicles (SV) and OAR were delineated on each CBCT and on the initial CT. Three adaptive methods were analyzed. Two methods relied on a double patient positioning at each fraction. For these, nodal CTVs prescription was delivered on bony registration. Gold markers match was then used either to: (1) complete the dose delivered to prostate and SV (Complement); (2) give almost the entire prescription to prostate and SV with slow gradient between targets to compensate for motions (Controlled Gradient). The third method (COR) used a pool of pre‐calculated plans from anterior fractions where the plan with the prostate center of mass closest to the daily anatomy was chosen. Adaptive techniques were compared to the standard non‐corrected CT plan. Results: With adaptive techniques, prostate and SV V(100%) is improved by 4% compared to non‐corrective method. This improvement does not Result in dose increase to OAR. EUD to bladder is reduced with an average reduction of 15% for COR. Conclusion: Because of their increase in CTV coverage and OAR sparing, COR and Complement methods may be good candidates for simple and effective adaptive treatment strategies for prostate cancer. Further improvements may be obtained by tuning CTV margins. A cumulative dose to simulate a complete treatment will show real effects and allow better comparison between each method. Ministere de la Sante et des Services Sociaux
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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".