Dosimetric Outcomes of Stereotactic Body Radiation Therapy to Ultracentral Lung Tumors: Lessons From the SUNSET Trial
Bibliographic record
Abstract
PURPOSE: The Stereotactic Radiation Therapy for Ultra-Central Non-Small Cell Lung Cancer: Safety and Efficacy Trial (SUNSET) trial investigated the maximum tolerated dose for ultracentral lung tumors treated with stereotactic body radiation therapy. Here, we report a spatial and dosimetric secondary analysis of the treatment plans and assess relationships between doses to targets, organs at risk (OARs), and clinical outcomes. METHODS AND MATERIALS: Five institutions enrolled patients with ultracentral lung cancer, cT1-3N0M0, and all received 60 Gy in 8 fractions. Maximum dose was limited to 120% of prescription. Planning data sets and treatment plans were imported into a central repository. Univariable logistic and Cox proportional hazards regression modeling were performed to identify significant dosimetric predictors for related grade ≥2 adverse events, overall survival, and local control (LC). RESULTS: ) with toxicity. PTV undercoverage (D98) was not associated with worse LC (HR per 5 Gy, 1.54; P = .68); however, lower PTV coverage was significantly associated with reduced overall survival for D98 (HR, 0.65; P = .014) and D95 (HR per 5 Gy, 0.66; P = .035). CONCLUSIONS: Within the dose constraints used in the trial, there was no relationship identified between OAR doses and toxicity. LC decreased with increasing overlap of PTV with OARs; however, this was not associated with dosimetric undercoverage of the target.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".