SU‐C‐BRC‐04: Avoidance of Perfused Lung by Optimization of Functional Mean Dose in Direct Machine Parameter Optimization
Bibliographic record
Abstract
Purpose: To implement and evaluate a functional mean dose‐based objective in a well‐established IMRT system, using lung perfusion images and direct machine parameter optimization (DMPO). Methods: Nineteen patients underwent SPECT perfusion imaging prior to treatment. In this retrospective study, plans were generated using Pinnacleˈ DMPO (Philips Healthcare, Andover, MA) with the aim of minimizing either the mean lung dose (MLD) or the lung functional mean dose (FMD). A seven equidistant beam configuration was used in all plans. Two levels of dose were prescribed: 50 Gy to the PTV1 (clinical target volume with margins) and 66 Gy to the PTV2 (gross tumor volume with margins). The MLD or FMD objective was decreased by steps of 1 Gy until dose to target volumes or organs at risk was deemed unacceptable. Plans were compared in terms of dose‐volume and dose‐function parameters. Statistical significance was assessed with a Wilcoxon matched pairs test. Results: While keeping PTV coverage similar (volumes receiving 93% of the prescribed dose were all over 98%), differences in MLD between both types of plans for a given patient ranged from –1.0 to +1.5 Gy (p = 0.2050), while FMD decreased significantly with a range of –2.1 to 0.0 Gy (p = 0.0003). The net improvement (FMD difference − MLD difference) ranged between −2.2 and 0.0 Gy. Dose to other organs at risk were similar and below widely‐ accepted tolerances. Conclusions: The use of SPECT perfusion images in conjunction with DMPO allowed a significant decrease of FMD while keeping dose to other structures at an acceptable level. Functionality‐aware dose redistribution could prove useful for dose escalation to improve tumor control with similar or lower lung complication probabilities. The approach can also be easily ported to arc therapy treatment planning using SmartArc. This work is supported by the Natural Sciences and Engineering Research Council of Canada and by a research agreement with Philips Healthcare.
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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 | 0.000 |
| 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) | 0.001 | 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".