Stereotactic ablative radiotherapy for ultra-central lung tumors: prioritize target coverage or organs at risk?
Bibliographic record
Abstract
BACKGROUND: Lung stereotactic ablative radiotherapy (SABR) is associated with low morbidity, however there is an increased risk of treatment-related toxicity in tumors directly abutting or invading the proximal bronchial tree, termed 'ultra-central' tumors. As there is no consensus regarding the optimal radiotherapy treatment regimen for these tumors, we performed a modeling study to evaluate the trade-offs between predicted toxicity and local control for commonly used high-precision dose-fractionation regimens. METHODS: Ten patients with ultra-central lung tumors were identified from our institutional database. New plans were generated for 3 different hypofractionated schemes: 50 Gy in 5 fractions, 60 Gy in 8 fractions and 60 Gy in 15 fractions. For each regimen, one plan was created that prioritized planning target volume (PTV) coverage, potentially at the expense of organ at risk (OAR) tolerance, and a second that compromised PTV coverage to respect OAR dose constraints. Published radiobiological models were employed to evaluate competing treatment plans based on estimates for local control and the likelihood for toxicity to OAR. RESULTS: The risk of esophageal or pulmonary toxicity was low (< 5%) in all scenarios. When PTV coverage was prioritized, tumor control probabilities were 92.9% for 50 Gy in 5 fractions, 92.4% for 60 Gy in 8 fractions, and 52.0% for 60 Gy in 15 fractions; however the estimated risk of grade ≥ 4 toxicity to the proximal bronchial tree was 68%, 44% and 2% respectively. When dose to OAR was prioritized, the risk of major pulmonary toxicity was reduced to < 1% in all schemes, but this compromise reduced tumor control probability to 60.3% for 50 Gy in 5 fractions, 65.7% for 60 Gy in 8 fractions and 47.8% for 60 Gy in 15 fractions. CONCLUSIONS: The tradeoff between local control and central airway toxicity are considerable in the use of 3 commonly used hypofractionated radiotherapy regimens for ultra-central lung cancer. The results of this planning study predict that the best balance may be achieved with 60 Gy in 8 fractions compromising PTV coverage as required to maintain acceptable doses to OAR. A prospective phase I trial (SUNSET) is planned to further evaluate this challenging clinical scenario.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".