TH‐A‐213AB‐03: Inverse Planning for 3D Intensity‐Modulated Grid‐Therapy
Bibliographic record
Abstract
Purpose: To develop an inverse optimization method for 3D intensity‐modulated grid‐therapy to improve dose distribution of grid therapy of advanced stage tumors. Methods: The following process was used to generate a 3D intensity‐modulated grid‐therapy plan. First, based on the geometry of the target volume and organs at risk (OAR), three to six radiation fields were selected to minimize the overlap between the target volume and OARs. Typically, three orthogonal fields were used to minimize the overlap between the fields to maintain grid‐like dose distribution for each field. Second, a step‐and‐shoot IMRT plan was generated with selected fields using Pinnacle treatment planning system (Philips Medical Systems). Third, each MLC segment was converted to a grid field defined by additional MLC segments using an in‐house developed program. Finally, the plan was further optimized using segment weight optimization in Pinnacle treatment planning system, maintaining the grid shape for each IMRT field with optimized intensities for the opening grids. The method was tested for a few clinical cases with bulky tumors. Dose distributions and dose volume histograms were compared between conventional single‐field grid‐therapy plan and 3D intensity‐modulated grid‐therapy plan. Results: Compared to conventional single‐field grid‐therapy plan, 3D intensity‐modulated grid‐therapy plan gives higher minimum dose to the target volume for potential improved tumor control probability and lower space‐fractionated doses to OARs for potential reduction of normal tissue complication probability. The drawback of the method is longer radiation treatment time. Conclusions: A method was developed to generate 3D intensity‐modulated grid‐therapy plan. Comparing to single‐field grid‐therapy plan, it gives higher dose to the target volume and lower space‐fractionated dose to OARs for a potential therapeutic advantage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".