Sci-Thur PM: YIS - 03: Comparing 4D-VMAT, Gated-VMAT and 3D-VMAT in SBRT treatment of lung cancer
Bibliographic record
Abstract
PURPOSE: To evaluate the treatment plan qualities of 4D-VMAT, gated-VMAT and 3D-VMAT in the treatment of non-small cell lung cancer (NSCLC) in stereotactic body radiation therapy (SBRT). METHODS: 4D-VMAT is a motion compensation strategy that aims to exploit relative target and OAR motion to increase OAR sparing over 3D-VMAT without the long treatment times associated with gated-VMAT. The 4D-VMAT algorithm incorporates the entire patient respiratory cycle and 4D-CT in the optimization process. Resulting treatment plans synchronize the delivery of each MLC aperture to a specific phase of the target motion. Using software developed in Matlab™, SBRT treatment plans for 4D-VMAT, gated-VMAT and 3D-VMAT were generated on 3 patients with NSCLC. Tumour motion ranged from 1.4-3.4 cm. The fractionation scheme was 48Gy in 4 fractions with the GTV receiving 100% of the prescribed dose. For gated-VMAT, the treatment window constrained residual tumour motion to 3 mm or less corresponding to duty cycles of 40-60%. In 3D-VMAT, the ITV was generated by merging the GTV from all phases. A b-spline transformation model was used to register the 4D-CT images and DVHs were calculated from total dose accumulated on the max expiration phase. RESULTS AND CONCLUSION: For the majority of OARs, gated-VMAT provided the greatest radiation sparing but significantly extended treatment times (25-35 gantry interruptions/arc). For 3D-VMAT, only 2 patients had clinically acceptable plans that met all the strict dose limits. OAR sparing in 4D-VMAT was comparable to gated-VMAT but with significantly improved delivery efficiency.
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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 | 0.001 |
| 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) | 0.005 | 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".