SU‐E‐T‐413: Optimizing the Delivery of Intensity‐Modulated Stereotactic Body Radiation Therapy to Lung Tumours Influenced by Respiratory Motion
Bibliographic record
Abstract
Purpose: To quantify the interplay effect for various IMRT techniques used to deliver Stereotactic‐Body Radiation Therapy (SBRT) to early‐stage lung cancer. Methods: Five lung cancer patients who received SBRT were retrospectively planned (54Gy/3fx) on the average 4D‐CT dataset with eight different IMRT techniques: three fixed‐beam IMRT plans (simple step‐and‐shoot (S/S), complex S/S, and sliding‐window (S/W)), Tomotherapy plans using three different beam sizes (1cm, 2.5cm, and 5cm), and two Volumetric‐Modulated Arc Therapy (VMAT) plans (one planned with Rapid Arc (Eclipse v8.9, Varian Medical Systems, Palo Alto, USA) and one planned with Smart Arc (Pinnacle v9.0, Philips Medical Systems, Cleveland, USA)). All treatment plans were calculated on a CT of the ArcCHECK phantom by Sun Nuclear (Gland, Switzerland) that was mounted on the QUASAR™ Programmable Respiratory Motion Platform (Modus Medical Devices Inc., London, Ontario). Each plan was delivered under the following motion conditions: 1) Static mode; 2) Sinusoidal mode (4s period) with S/I motion ranging from 5mm–20mm peak‐to‐peak in 5 mm increments; 3) Real‐patient waveforms ranging from 5mm–30mm peak‐to‐peak. A standard 3%/3mm gamma analysis compared each delivery to their corresponding calculated plan. Results: All methods were acceptable on average (>90% points pass) up to 15mm peak‐to‐peak motion except for the Rapid Arc plan, which had a significantly less pass rate (75.6+/−3.9%) than all other techniques (p<.02). Tomotherapy plans using either a 2.5cm beam (98.7+/−2.1%) or 5cm beam (96.8+/−3.5%) were still acceptable for motion up to 20 mm peak‐to‐peak. For irregular motion greater than 15mm, only the VMAT techniques failed ((74.9+/−16.6)% for Rapid Arc and (89.3+/−8.9)% for Smart Arc on average. Conclusions: Interplay effect in SBRT delivery was dependent on the IMRT technique for peak‐to‐peak motions greater than 15 mm. Tomotherapy was the least sensitive for motion up to 20mm. Rapid Arc was the most sensitive for motion greater than 15mm peak‐to‐peak.
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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 | 0.000 |
| 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) | 0.000 | 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".