SU-FF-T-151: Perfusion-Based Plan Optimization for Lung Cancer Using An Anatomy-Based Aperture Inverse Planning System
Bibliographic record
Abstract
Purpose: To implement SPECT-based plan optimization in an anatomy-based aperture inverse planning system (IPS) for the avoidance of functional pulmonary regions for cases of lung cancer. Method and Materials: The IPS allows simultaneous optimization of beam orientations and weights from apertures defined by an anatomy-based segmentation. SPECT perfusion information has been integrated in the dose-volume-based cost function of the inverse planning system through a voxel-by-voxel linear spatial modulation of the importance factors (IFs) according to local perfusion score. For two cases of lung cancer, plans have been generated by the IPS using four non-coplanar incidences (gantry and couch angles optimized) using a purely anatomical approach and the SPECT-based approach. Planning target volume (PTV) coverage and lung avoidance (both volumetric and functional) have been compared. Results: Maximum dose to PTV is usually increased when increasing importance of functional lung regions in the optimization, creating boost regions. For the first case, the functional volume of lung receiving 20 Gy (F20) decreases from 28.4% to 22.0% while the mean lung perfused dose (MpLD) decreases from 16.5 Gy to 13.7 Gy. For the second case, the F20 does not vary (26.5%) and the MpLD decreases from 17.4 Gy to 16.6 Gy. All plans produced are simpler than typical IMRT plans, with few segments (5 or 10) and few monitor units (range 285–375) used. Conclusions: The system allows generation of simple aperture-based IMRT plans with the addition of functional lung sparing when considering SPECT-based information. However, the extent of the benefit is patient-dependant and varies according to the perfusion pattern and proximity of other critical structures to the PTV. Boost regions created by the redistribution of dose might prove useful in the context of dose escalation in lung irradiation.
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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 | 0.001 |
| 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) | 0.003 | 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".