Poster — Thurs Eve‐40: The potential of using SPECT ventilation information with IMRT for functional lung avoidance in radiotherapy of non small cell lung cancer
Bibliographic record
Abstract
We have investigated the feasibility of using ventilation scans obtained from single photon emission computed tomography (SPECT) in intensity-modulated radiation therapy (IMRT) planning in lung cancer radiotherapy to avoid well functioning lung. We fused SPECT ventilation scans acquired at GE Hawkeye SPECT-CT of ten stage-III lung radiotherapy patients with planning CT in treatment planning system (Pinnacle v8.0, Philips Medical Systems). We automatically segment out 50% and 70% ventilated volumes. For each patient, we generated IMRT plans using nine equally spaced beams with and without avoiding well ventilated volume. They were compared with three beam IMRT plans with beam directions chosen to minimize the mean dose to the ventilated lung volumes, while keeping cord dose below tolerance and dose uniformity in the target. The plans generated using functional lung avoidance information reduces the doses to the functioning lung. With both IMRT avoidance plans, we could not obtain better functional avoidance or lower V-20Gy (volume receiving 20Gy or more) for total lung when the planning target volume (PTV) was surrounded by functional lung volumes. We were able to achieve better ventilated lung avoidance and lower total lung V-20Gy when the PTV is close to, but not surrounded by functioning lung volumes. For patients with the PTV that is far from 50% and 70% functional lung volumes, three-field IMRT spare the ventilated lung as well as nine-field IMRT ventilation avoidance plan, with a lower total lung V20-Gy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".