Sci‐Sat AM (1) General‐03: IMRT prostate planning: a graphical rectal NTCP determination
Bibliographic record
Abstract
Including the NTCP in the objective function of the inverse IMRT plan optimization would make the planning more effective in the prediction of the post‐radiation effects. However, doing so would lengthen the total planning time. The purpose of this work is to establish a method for rectal NTCP determination, independent of the DVH, as a means of improving the treatment planning efficiency. In this study, IMRT plans of ten randomly selected prostate patients are performed using Pinnacle3 V 6.2b planning system. The DVH control points and prescriptions for contouring of the PTVs and OARs were adapted from the prescriptions of the Radiation Therapy Oncology Group protocol P‐0126. PTVs with margins in the range of 2 to 10 mm and prescribed dose ranging from 70 to 82 Gy were employed in our study. This paper presents a new model for determination of the rectal NTCP (RNTCP). The method uses a special function, named GVN (from Gy, Volume, NTCP). It describes the RNTCP if a volume of 1 cm3 of the intersection between the PTV and rectum is irradiated uniformly by a dose of 1 Gy. The function was “geometrically” normalized using a prostate‐prostate‐ratio (PPR) of the patients' prostates. Also, a correction of the RNTCP for different prescribed doses was used. The argument of the normalized function is the rectum intersection, and parameters are the prescribed dose, prostate volume, PTV margin and PPR.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.511 | 0.251 |
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".