Poster — Thur Eve — 32: Stereotactic Body Radiation Therapy for Peripheral Lung Lesion: Treatment Planning and Quality Assurance
Bibliographic record
Abstract
Stereotactic body radiation therapy (SBRT), due to its high precision for target localizing, has become widely used to treat tumours at various locations, including the lungs. Lung SBRT program was started at our institution a year ago. Eighteen patients with peripheral lesions up to 3 cm diameter have been treated with 48 Gy in 4 fractions. Based on four‐dimensional computed tomography (4DCT) simulation, internal target volume (ITV) was delineated to encompass the respiratory motion of the lesion. A margin of 5 mm was then added to create the planning target volume (PTV) for setup uncertainties. There was no expansion from gross tumour volume (GTV) to clinical target volume (CTV). Pinnacle 9.6 was used as the primary treatment planning system. Volumetric modulated arc therapy (VMAT) technique, with one or two coplanar arcs, generally worked well. For quality assurance (QA), each plan was exported to Eclipse 10 and dose calculation was repeated. Dose volume histograms (DVHs) of the targets and organs at risk (OARs) were then compared between the two treatment planning systems. Winston‐Lutz tests were carried out as routine machine QA. Patient‐specific QA included ArcCheck measurement with an insert, where an ionization chamber was placed at the centre to measure dose at the isocenter. For the first several patients, and subsequently for the plans with extremely strong modulation, Gafchromic film dosimetry was also employed. For each patient, a mock setup was scheduled prior to treatments. Daily pre‐ and post‐CBCT were acquired for setup and assessment of intra‐fractional motion, respectively.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.025 |
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".