Sci‐Fri PM: Planning‐04: Dose escalation study using anatomy‐based aperture IMRT and SPECT perfusion images for lung cancer
Bibliographic record
Abstract
In the case of non‐small cell lung cancer, doses typically prescribed (60–66 Gy) are not sufficient to ensure a satisfactory tumor control probability. Dose escalation needs to be realized, but dose to organs at risk (OARs) must be kept under widely accepted clinical thresholds. Also, lung functionality is not homogeneously distributed over all the volume: single‐photon emission computed tomography (SPECT) allows spatial characterization of perfusion, open the way to the design of treatments plans that could preferentially avoid highly‐functional lung. In this study, three cases of lung cancer were retrospectively used to assess the capacity of an anatomy‐based aperture inverse planning system to realize dose escalation while limiting dose to perfused lung. Plans were generated for four‐beam non‐coplanar configurations, mixing 6 and 23 MV photon beams. All dose calculations were performed using Pinnacle3 superposition/convolution algorithm. An increasing dose was prescribed to a subvolume of the initial planning target volume. Levels of escalation achieved for the three cases studied were 81 Gy, 111 Gy and 66 Gy to the subvolume. Escalation was limited in two cases by the dose to the esophagus and in the other case by the presence of overdosages near beam entry ports. Calculation of dose‐volume parameters for OARs shows that they respect clinical thresholds. Plans generated by the system are less complex than plans generated in beamlet‐based IMRT, because of the use of few, large segments. The approach used in this study allows important dose escalation, potentially improving treatment outcome.
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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 | 0.001 | 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) | 0.006 | 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".